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Efficacy and Safety of Fentanyl Pectin Nasal Spray Compared with Immediate-Release Morphine Sulfate Tablets in the Treatment of Breakthrough Cancer Pain: A Multicenter, Randomized, Controlled, Double-Blind, Double-Dummy Multiple-Crossover Study
Volume 9, Issue 6, November-December 2011, Pages 224-231
| doi:10.1016/j.suponc.2011.07.004 | How to Cite or Link Using DOI |
| Permissions & Reprints |
Original research
Marie Fallon MB, ChB, MD, FRCP 
Received 10 February 2011; Accepted 18 July 2011. Available online 3 November 2011.
Background
Immediate-release morphine sulfate (IRMS) remains the standard treatment for breakthrough cancer pain (BTCP), but its onset of effect does not match the rapid onset and short duration of most BTCP episodes.
Objective
This study will evaluate the efficacy/tolerability of fentanyl pectin nasal spray (FPNS) compared with IRMS for BTCP.
Methods
Patients (n = 110) experiencing one to four BTCP episodes/day while taking ≥60 mg/day oral morphine (or equivalent) for background cancer pain entered a double-blind, double-dummy (DB/DD), multiple-crossover study. Patients completing a titration phase (n = 84) continued to a DB/DD phase: 10 episodes of BTCP were randomly treated with FPNS and oral capsule placebo (five episodes) or IRMS and nasal spray placebo (5 episodes). The primary end point was pain intensity (P < .05 FPNS vs. IRMS) difference from baseline at 15 minutes (PID15). Secondary end points were onset of pain intensity (PI) decrease (≥1-point) and time to clinically meaningful pain relief (CMPR, ≥2-point PI decrease). Safety and tolerability were evaluated by adverse events (AEs) and nasal assessments. By-patient and by-episode analyses were completed.
Results
Compared with IRMS, FPNS significantly improved mean PID15 scores. 57.5% of FPNS-treated episodes significantly demonstrated onset of PI improvement by 5 minutes and 95.7% by 30 minutes. CMPR (≥2-point PI decrease) was seen in 52.4% of episodes by 10 minutes. Only 4.7% of patients withdrew from titration (2.4% in DB/DD phase) because of AEs; no significant nasal effects were reported.
Conclusion
FPNS was efficacious and well tolerated in the treatment of BTCP and provided faster onset of analgesia and attainment of CMPR than IRMS.
The authors acknowledge i3Research, which conducted the study; the technical and editorial support provided by Anita Chadha-Patel at ApotheCom; and the Fentanyl Nasal Spray Study 044 Investigators. This study was sponsored by Archimedes Development, Ltd.
Conflicts of interest Disclosure: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr. Davies has served as a consultant for Archimedes and received support from Archimedes to travel to meetings to present trial data. No other conflicts of interest were reported.
 |  |
Volume 9, Issue 6, November-December 2011, Pages 224-231
| doi:10.1016/j.suponc.2011.07.004 | How to Cite or Link Using DOI |
| Permissions & Reprints |
Original research
Marie Fallon MB, ChB, MD, FRCP
Received 10 February 2011; Accepted 18 July 2011. Available online 3 November 2011.
Background
Immediate-release morphine sulfate (IRMS) remains the standard treatment for breakthrough cancer pain (BTCP), but its onset of effect does not match the rapid onset and short duration of most BTCP episodes.
Objective
This study will evaluate the efficacy/tolerability of fentanyl pectin nasal spray (FPNS) compared with IRMS for BTCP.
Methods
Patients (n = 110) experiencing one to four BTCP episodes/day while taking ≥60 mg/day oral morphine (or equivalent) for background cancer pain entered a double-blind, double-dummy (DB/DD), multiple-crossover study. Patients completing a titration phase (n = 84) continued to a DB/DD phase: 10 episodes of BTCP were randomly treated with FPNS and oral capsule placebo (five episodes) or IRMS and nasal spray placebo (5 episodes). The primary end point was pain intensity (P < .05 FPNS vs. IRMS) difference from baseline at 15 minutes (PID15). Secondary end points were onset of pain intensity (PI) decrease (≥1-point) and time to clinically meaningful pain relief (CMPR, ≥2-point PI decrease). Safety and tolerability were evaluated by adverse events (AEs) and nasal assessments. By-patient and by-episode analyses were completed.
Results
Compared with IRMS, FPNS significantly improved mean PID15 scores. 57.5% of FPNS-treated episodes significantly demonstrated onset of PI improvement by 5 minutes and 95.7% by 30 minutes. CMPR (≥2-point PI decrease) was seen in 52.4% of episodes by 10 minutes. Only 4.7% of patients withdrew from titration (2.4% in DB/DD phase) because of AEs; no significant nasal effects were reported.
Conclusion
FPNS was efficacious and well tolerated in the treatment of BTCP and provided faster onset of analgesia and attainment of CMPR than IRMS.
The authors acknowledge i3Research, which conducted the study; the technical and editorial support provided by Anita Chadha-Patel at ApotheCom; and the Fentanyl Nasal Spray Study 044 Investigators. This study was sponsored by Archimedes Development, Ltd.
Conflicts of interest Disclosure: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr. Davies has served as a consultant for Archimedes and received support from Archimedes to travel to meetings to present trial data. No other conflicts of interest were reported.
| |
Volume 9, Issue 6, November-December 2011, Pages 224-231
| doi:10.1016/j.suponc.2011.07.004 | How to Cite or Link Using DOI |
| Permissions & Reprints |
Original research
Marie Fallon MB, ChB, MD, FRCP
Received 10 February 2011; Accepted 18 July 2011. Available online 3 November 2011.
Background
Immediate-release morphine sulfate (IRMS) remains the standard treatment for breakthrough cancer pain (BTCP), but its onset of effect does not match the rapid onset and short duration of most BTCP episodes.
Objective
This study will evaluate the efficacy/tolerability of fentanyl pectin nasal spray (FPNS) compared with IRMS for BTCP.
Methods
Patients (n = 110) experiencing one to four BTCP episodes/day while taking ≥60 mg/day oral morphine (or equivalent) for background cancer pain entered a double-blind, double-dummy (DB/DD), multiple-crossover study. Patients completing a titration phase (n = 84) continued to a DB/DD phase: 10 episodes of BTCP were randomly treated with FPNS and oral capsule placebo (five episodes) or IRMS and nasal spray placebo (5 episodes). The primary end point was pain intensity (P < .05 FPNS vs. IRMS) difference from baseline at 15 minutes (PID15). Secondary end points were onset of pain intensity (PI) decrease (≥1-point) and time to clinically meaningful pain relief (CMPR, ≥2-point PI decrease). Safety and tolerability were evaluated by adverse events (AEs) and nasal assessments. By-patient and by-episode analyses were completed.
Results
Compared with IRMS, FPNS significantly improved mean PID15 scores. 57.5% of FPNS-treated episodes significantly demonstrated onset of PI improvement by 5 minutes and 95.7% by 30 minutes. CMPR (≥2-point PI decrease) was seen in 52.4% of episodes by 10 minutes. Only 4.7% of patients withdrew from titration (2.4% in DB/DD phase) because of AEs; no significant nasal effects were reported.
Conclusion
FPNS was efficacious and well tolerated in the treatment of BTCP and provided faster onset of analgesia and attainment of CMPR than IRMS.
The authors acknowledge i3Research, which conducted the study; the technical and editorial support provided by Anita Chadha-Patel at ApotheCom; and the Fentanyl Nasal Spray Study 044 Investigators. This study was sponsored by Archimedes Development, Ltd.
Conflicts of interest Disclosure: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr. Davies has served as a consultant for Archimedes and received support from Archimedes to travel to meetings to present trial data. No other conflicts of interest were reported.
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The impact of bone metastases and skeletal-related events on healthcare costs in prostate cancer patients receiving hormonal therapy
Bone is the most common site of metastases in men with advanced prostate cancer, one of the most prevalent cancers in the United States and the second leading cause of cancer death after lung cancer.1–3 The median survival from diagnosis of bone metastases is 30–40 months.2 During this time, skeletal-related events (SREs), including pathologic fractures, surgery or radiation to the bone, spinal cord compression, or hypercalcemia of malignancy, can occur. SREs are associated with considerable morbidity, impaired health-related quality of life, reduced survival, and increased costs.4–10
Although studies have examined the impact of SREs on costs in patients with advanced cancers and bone metastases,5–9,11 the effects of bone metastases without SREs on healthcare costs in prostate cancer patients have not been studied. The magnitude of these costs may be important in economic evaluations of treatments to prevent or delay bone metastases in prostate cancer patients. The objective of this study was to estimate the effects on healthcare costs of bone metastases in the presence and absence of SREs in men with prostate cancer who were receiving hormonal therapy.
* For a PDF of the full article, click in the link to the left of this introduction.
Bone is the most common site of metastases in men with advanced prostate cancer, one of the most prevalent cancers in the United States and the second leading cause of cancer death after lung cancer.1–3 The median survival from diagnosis of bone metastases is 30–40 months.2 During this time, skeletal-related events (SREs), including pathologic fractures, surgery or radiation to the bone, spinal cord compression, or hypercalcemia of malignancy, can occur. SREs are associated with considerable morbidity, impaired health-related quality of life, reduced survival, and increased costs.4–10
Although studies have examined the impact of SREs on costs in patients with advanced cancers and bone metastases,5–9,11 the effects of bone metastases without SREs on healthcare costs in prostate cancer patients have not been studied. The magnitude of these costs may be important in economic evaluations of treatments to prevent or delay bone metastases in prostate cancer patients. The objective of this study was to estimate the effects on healthcare costs of bone metastases in the presence and absence of SREs in men with prostate cancer who were receiving hormonal therapy.
* For a PDF of the full article, click in the link to the left of this introduction.
Bone is the most common site of metastases in men with advanced prostate cancer, one of the most prevalent cancers in the United States and the second leading cause of cancer death after lung cancer.1–3 The median survival from diagnosis of bone metastases is 30–40 months.2 During this time, skeletal-related events (SREs), including pathologic fractures, surgery or radiation to the bone, spinal cord compression, or hypercalcemia of malignancy, can occur. SREs are associated with considerable morbidity, impaired health-related quality of life, reduced survival, and increased costs.4–10
Although studies have examined the impact of SREs on costs in patients with advanced cancers and bone metastases,5–9,11 the effects of bone metastases without SREs on healthcare costs in prostate cancer patients have not been studied. The magnitude of these costs may be important in economic evaluations of treatments to prevent or delay bone metastases in prostate cancer patients. The objective of this study was to estimate the effects on healthcare costs of bone metastases in the presence and absence of SREs in men with prostate cancer who were receiving hormonal therapy.
* For a PDF of the full article, click in the link to the left of this introduction.
Olanzapine Versus Aprepitant for the Prevention of Chemotherapy-Induced Nausea and Vomiting: A Randomized Phase III Trial
Volume 9, Issue 5, September-October 2011, Pages 188-195
| doi:10.1016/j.suponc.2011.05.002 |
| Permissions & Reprints |
Original Research
Received 8 April 2011; Accepted 19 May 2011. Available online 24 September 2011.
Abstract
Background
The purpose of the study was to compare the effectiveness of olanzapine (OLN) and aprepitant (APR) for the prevention of chemotherapy-induced nausea and vomiting (CINV) in patients receiving highly emetogenic chemotherapy.
Methods
A phase III trial was performed in chemotherapy-naive patients receiving cisplatin ≥70 mg/m2 or cyclophosphamide ≥500 mg/m2 and doxorubicin ≥50 mg/m2, comparing OLN to APR in combination with palonosetron (PAL) and dexamethasone (DEX). The OLN, PAL, DEX (OPD) regimen was 10 mg of oral OLN, 0.25 mg of IV PAL, and 20 mg of IV DEX prechemotherapy, day 1, and 10 mg/day of oral OLN alone on days 2–4 postchemotherapy. The APR, PAL, DEX (APD) regimen was 125 mg of oral APR, 0.25 mg of IV PAL, and 12 mg of IV DEX, day 1, and 80 mg of oral APR, days 2 and 3, and 4 mg of DEX BID, days 2–4. Two hundred fifty-one patients consented to the protocol and were randomized. Two hundred forty-one patients were evaluable.
Results
Complete response (CR) (no emesis, no rescue) was 97% for the acute period (24 hours postchemotherapy), 77% for the delayed period (days 2–5 postchemotherapy), and 77% for the overall period (0–120 hours) for 121 patients receiving the OPD regimen. CR was 87% for the acute period, 73% for the delayed period, and 73% for the overall period in 120 patients receiving the APD regimen. Patients without nausea (0, scale 0–10, MD Anderson Symptom Inventory) were OPD: 87% acute, 69% delayed, and 69% overall; APD: 87% acute, 38% delayed, and 38% overall. There were no grade 3 or 4 toxicities. CR and control of nausea in subsequent chemotherapy cycles were equal to or greater than cycle 1 for both regimens. OPD was comparable to APD in the control of CINV. Nausea was better controlled with OPD.
Discussion
In this study, OLN combined with a single dose of DEX and a single dose of PAL was very effective at controlling acute and delayed CINV in patients receiving highly emetogenic chemotherapy. CR rates were not significantly different from a similar group of patients receiving highly emetogenic chemotherapy and an antiemetic regimen consisting of APR, PAL, and DEX.
Volume 9, Issue 5, September-October 2011, Pages 188-195
Volume 9, Issue 5, September-October 2011, Pages 188-195
| doi:10.1016/j.suponc.2011.05.002 |
| Permissions & Reprints |
Original Research
Received 8 April 2011; Accepted 19 May 2011. Available online 24 September 2011.
Abstract
Background
The purpose of the study was to compare the effectiveness of olanzapine (OLN) and aprepitant (APR) for the prevention of chemotherapy-induced nausea and vomiting (CINV) in patients receiving highly emetogenic chemotherapy.
Methods
A phase III trial was performed in chemotherapy-naive patients receiving cisplatin ≥70 mg/m2 or cyclophosphamide ≥500 mg/m2 and doxorubicin ≥50 mg/m2, comparing OLN to APR in combination with palonosetron (PAL) and dexamethasone (DEX). The OLN, PAL, DEX (OPD) regimen was 10 mg of oral OLN, 0.25 mg of IV PAL, and 20 mg of IV DEX prechemotherapy, day 1, and 10 mg/day of oral OLN alone on days 2–4 postchemotherapy. The APR, PAL, DEX (APD) regimen was 125 mg of oral APR, 0.25 mg of IV PAL, and 12 mg of IV DEX, day 1, and 80 mg of oral APR, days 2 and 3, and 4 mg of DEX BID, days 2–4. Two hundred fifty-one patients consented to the protocol and were randomized. Two hundred forty-one patients were evaluable.
Results
Complete response (CR) (no emesis, no rescue) was 97% for the acute period (24 hours postchemotherapy), 77% for the delayed period (days 2–5 postchemotherapy), and 77% for the overall period (0–120 hours) for 121 patients receiving the OPD regimen. CR was 87% for the acute period, 73% for the delayed period, and 73% for the overall period in 120 patients receiving the APD regimen. Patients without nausea (0, scale 0–10, MD Anderson Symptom Inventory) were OPD: 87% acute, 69% delayed, and 69% overall; APD: 87% acute, 38% delayed, and 38% overall. There were no grade 3 or 4 toxicities. CR and control of nausea in subsequent chemotherapy cycles were equal to or greater than cycle 1 for both regimens. OPD was comparable to APD in the control of CINV. Nausea was better controlled with OPD.
Discussion
In this study, OLN combined with a single dose of DEX and a single dose of PAL was very effective at controlling acute and delayed CINV in patients receiving highly emetogenic chemotherapy. CR rates were not significantly different from a similar group of patients receiving highly emetogenic chemotherapy and an antiemetic regimen consisting of APR, PAL, and DEX.
Volume 9, Issue 5, September-October 2011, Pages 188-195
Volume 9, Issue 5, September-October 2011, Pages 188-195
| doi:10.1016/j.suponc.2011.05.002 |
| Permissions & Reprints |
Original Research
Received 8 April 2011; Accepted 19 May 2011. Available online 24 September 2011.
Abstract
Background
The purpose of the study was to compare the effectiveness of olanzapine (OLN) and aprepitant (APR) for the prevention of chemotherapy-induced nausea and vomiting (CINV) in patients receiving highly emetogenic chemotherapy.
Methods
A phase III trial was performed in chemotherapy-naive patients receiving cisplatin ≥70 mg/m2 or cyclophosphamide ≥500 mg/m2 and doxorubicin ≥50 mg/m2, comparing OLN to APR in combination with palonosetron (PAL) and dexamethasone (DEX). The OLN, PAL, DEX (OPD) regimen was 10 mg of oral OLN, 0.25 mg of IV PAL, and 20 mg of IV DEX prechemotherapy, day 1, and 10 mg/day of oral OLN alone on days 2–4 postchemotherapy. The APR, PAL, DEX (APD) regimen was 125 mg of oral APR, 0.25 mg of IV PAL, and 12 mg of IV DEX, day 1, and 80 mg of oral APR, days 2 and 3, and 4 mg of DEX BID, days 2–4. Two hundred fifty-one patients consented to the protocol and were randomized. Two hundred forty-one patients were evaluable.
Results
Complete response (CR) (no emesis, no rescue) was 97% for the acute period (24 hours postchemotherapy), 77% for the delayed period (days 2–5 postchemotherapy), and 77% for the overall period (0–120 hours) for 121 patients receiving the OPD regimen. CR was 87% for the acute period, 73% for the delayed period, and 73% for the overall period in 120 patients receiving the APD regimen. Patients without nausea (0, scale 0–10, MD Anderson Symptom Inventory) were OPD: 87% acute, 69% delayed, and 69% overall; APD: 87% acute, 38% delayed, and 38% overall. There were no grade 3 or 4 toxicities. CR and control of nausea in subsequent chemotherapy cycles were equal to or greater than cycle 1 for both regimens. OPD was comparable to APD in the control of CINV. Nausea was better controlled with OPD.
Discussion
In this study, OLN combined with a single dose of DEX and a single dose of PAL was very effective at controlling acute and delayed CINV in patients receiving highly emetogenic chemotherapy. CR rates were not significantly different from a similar group of patients receiving highly emetogenic chemotherapy and an antiemetic regimen consisting of APR, PAL, and DEX.
Volume 9, Issue 5, September-October 2011, Pages 188-195
The wearable external cardiac defibrillator for cancer patients at risk for sudden cardiac death
Implantable cardioverter defibrillators (ICDs) are indicated for primary prevention of sudden cardiac death (SCD) in patients with reduced left ventricular function (an ejection fraction of ≤ 35%). ICD therapy is also recommended for secondary prevention of SCD in patients with a life-threatening cardiac arrhythmia, including aborted sudden cardiac death. Contraindications to ICD therapy are life expectancy ≤ 1 year, incessant arrhythmia, significant psychiatric illness, syncope without evidence of inducible ventricular arrhythmia or structural heart disease, ventricular arrhythmia amenable to catheter ablation, ventricular arrhythmia due to a reversible cause, and primary prevention of SCD in patients ineligible for cardiac transplantation or cardiac resynchronization therapy.1 In addition, relative contraindications to ICD therapy include the need for radiation therapy to the thorax, high risk for infection, and high risk for deep venous thrombosis.
A subset of patients with cancer is at risk for SCD due to a variety of cardiac causes, including chemotherapy-induced cardiomyopathy or druginduced long QT syndrome. These patients may benefit from ICD placement. However, the aforementioned relative contraindications for permanent defibrillator implantation often coexist in patients with cancer. Moreover, an individual with acute malignancy may have other contraindications for permanent defibrillator implantation, including the potential reversibility of cardiomyopathy or arrhythmia or an unclear prognosis for 1-year survival. ...
* For a PDF of the full article, click in the link to the left of this introduction.
Implantable cardioverter defibrillators (ICDs) are indicated for primary prevention of sudden cardiac death (SCD) in patients with reduced left ventricular function (an ejection fraction of ≤ 35%). ICD therapy is also recommended for secondary prevention of SCD in patients with a life-threatening cardiac arrhythmia, including aborted sudden cardiac death. Contraindications to ICD therapy are life expectancy ≤ 1 year, incessant arrhythmia, significant psychiatric illness, syncope without evidence of inducible ventricular arrhythmia or structural heart disease, ventricular arrhythmia amenable to catheter ablation, ventricular arrhythmia due to a reversible cause, and primary prevention of SCD in patients ineligible for cardiac transplantation or cardiac resynchronization therapy.1 In addition, relative contraindications to ICD therapy include the need for radiation therapy to the thorax, high risk for infection, and high risk for deep venous thrombosis.
A subset of patients with cancer is at risk for SCD due to a variety of cardiac causes, including chemotherapy-induced cardiomyopathy or druginduced long QT syndrome. These patients may benefit from ICD placement. However, the aforementioned relative contraindications for permanent defibrillator implantation often coexist in patients with cancer. Moreover, an individual with acute malignancy may have other contraindications for permanent defibrillator implantation, including the potential reversibility of cardiomyopathy or arrhythmia or an unclear prognosis for 1-year survival. ...
* For a PDF of the full article, click in the link to the left of this introduction.
Implantable cardioverter defibrillators (ICDs) are indicated for primary prevention of sudden cardiac death (SCD) in patients with reduced left ventricular function (an ejection fraction of ≤ 35%). ICD therapy is also recommended for secondary prevention of SCD in patients with a life-threatening cardiac arrhythmia, including aborted sudden cardiac death. Contraindications to ICD therapy are life expectancy ≤ 1 year, incessant arrhythmia, significant psychiatric illness, syncope without evidence of inducible ventricular arrhythmia or structural heart disease, ventricular arrhythmia amenable to catheter ablation, ventricular arrhythmia due to a reversible cause, and primary prevention of SCD in patients ineligible for cardiac transplantation or cardiac resynchronization therapy.1 In addition, relative contraindications to ICD therapy include the need for radiation therapy to the thorax, high risk for infection, and high risk for deep venous thrombosis.
A subset of patients with cancer is at risk for SCD due to a variety of cardiac causes, including chemotherapy-induced cardiomyopathy or druginduced long QT syndrome. These patients may benefit from ICD placement. However, the aforementioned relative contraindications for permanent defibrillator implantation often coexist in patients with cancer. Moreover, an individual with acute malignancy may have other contraindications for permanent defibrillator implantation, including the potential reversibility of cardiomyopathy or arrhythmia or an unclear prognosis for 1-year survival. ...
* For a PDF of the full article, click in the link to the left of this introduction.
Attitudes toward Vaccination for Pandemic H1N1 and Seasonal Influenza in Patients with Hematologic Malignancies
Original research
Benjamin H. Chin-Yeea, Katherine Monkman MDa, Zafar Hussain MD, FRCP(C)a and Leonard A. Minuk MD, FRCP(C)
Background
Patients with hematologic malignancies are at increased risk of influenza and its complications. Despite current health recommendations and evidence favoring influenza vaccination, vaccination rates remain low in cancer patients.
Objective
The purpose of this study was to determine which factors influenced vaccination rates.
Methods
During the 2009–2010 pandemic H1N1 and seasonal influenza season, we surveyed patients with hematologic malignancies in a Canadian cancer center. Of the patients participating in our study (n = 129), 66% and 57% received the H1N1 pandemic influenza and seasonal influenza vaccines, respectively.
Results
A number of reasons for vaccination refusal were reported, most relating to general skepticism about the safety and efficacy of vaccination. Physician advice was also a factor influencing vaccination rates in patients. The vaccination rate for seasonal influenza was 39% in patients <65 years old, significantly lower than the rate of 73% reported for patients aged ≥65 years (P < 0.0001).
Conclusion
Future education programs should target younger patient populations and health-care workers, focusing on vaccine safety and efficacy in the high-risk cancer population.
Despite the annual development of effective influenza vaccines, influenza remains a significant cause of morbidity and mortality in Canada. In the 2009–2010 influenza season, approximately 40,000 Canadians were infected with seasonal influenza or the pandemic H1N1 influenza virus,1 and influenza has been estimated to cause 4,000–8,000 deaths in Canada each year.2 It is estimated that a severe influenza pandemic could result in a 1% reduction in annual gross domestic product in Canada.3
Patients with hematologic malignancies are known to be at increased risk of influenza and its complications, with estimated mortality rates in the range 5%–27%.[4], [5], [6], [7] and [8] Evidence for the efficacy of the influenza vaccine is limited and contradictory, and many assume that immunocompromised patients will not be able to generate a protective antibody response. Nonetheless, current evidence favors vaccination.9 Pollyea et al10 reported that eight of 15 trials on the efficacy of vaccination in patients with hematologic malignancies concluded that vaccination was beneficial. Both the Centers for Disease Control and Prevention (CDC) and the Public Health Agency of Canada (PHAC) advised that all immunocompromised patients, including those with cancer, receive both the seasonal influenza vaccine and the pandemic H1N1 influenza vaccine in the 2009–2010 influenza season.[11] and [12]
Despite these recommendations, rates of influenza vaccination remain low for the general population and cancer patients in Canada, with rates reported at 40% and 65% respectively.[13] and [14] A recent study by Yee et al15 reported similarly low influenza vaccination rates of 58% in cancer patients in the United States. Vaccination has long been a controversial public health issue, and many people choose not to be vaccinated due to fears that vaccines may not be safe and effective.[16], [17] and [18] Lack of physician recommendation has also been cited as a significant factor in the decision to decline vaccination.16
In this study, we sought to determine what percentage of patients being treated for hematologic malignancies in an Ontario, Canada, cancer center received the H1N1 pandemic influenza vaccine in the 2009–2010 influenza season and to explore the barriers to vaccination in this high-risk population. We also collected information on the percentage of patients who received the seasonal influenza vaccine. It was general practice for physicians at this center to recommend influenza vaccination in accordance with the PHAC recommendations.
Methods
Patients being treated for hematologic malignancies at the London Regional Cancer Program (London, Canada) were invited to complete a survey regarding influenza vaccination (Appendix). The London Regional Cancer Program is a tertiary care center providing specialized cancer care to a population base of 1.2 million in southwestern Ontario. The survey was administered to patients eligible to participate in another study assessing antibody levels pre- and postvaccination with the H1N1 pandemic vaccine. Eligible patients were 18 years or older and being treated or followed for hematological malignancies at the London Regional Cancer Program who attended an appointment between October 28 and November 19, 2009, and returned for a follow-up visit between January 5 and March 26, 2010 (n = 151). Patients were asked if they had received the pandemic H1N1 influenza vaccine and the seasonal influenza vaccine during the 2009–2010 influenza season. Those who had declined vaccination were asked to describe the reasons for their choice. The survey provided a list of six possible reasons for declining vaccination and gave patients the option of writing in their own responses.
The results of the study were analyzed using InStat 3 software (GraphPad, La Jolla, CA). The Mann-Whitney U-test was used to compare continuous variables, and Fisher's exact test was used to compare proportions. The study was approved by the University of Western Ontario's Institutional Research Ethics Board (IRB 16627E).
Results
Of the 151 patients invited to participate, 129 completed the survey, yielding a response rate of 85%. Patient characteristics are shown in Table 1. The respondents ranged in age from 19 to 86 years, 56% were male and 44% were female, and patients aged 65 years or older comprised 52% of the study population. The mean age of the patient group was 62.7 ± 14.8 years. Overall 119 patients (92%) had received chemotherapy at some time during their illness, with 96 patients (76%) actively receiving chemotherapy, defined as treatment within the past 3 months. Diagnoses included acute leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, lymphoma, multiple myeloma, myelodysplastic syndromes, and myeloproliferative neoplasms.
Of the 129 patients surveyed, 85 (66%) reported that they had received the H1N1 pandemic influenza vaccine during the 2009–2010 influenza season. Fifty-seven percent had received the seasonal influenza vaccine, and 50% had received both the seasonal and the H1N1 vaccines. Of the 44 patients who did not receive the H1N1 vaccine, only three planned to receive it. Eight of the 56 patients not vaccinated with the seasonal influenza vaccine planned to receive it.
There were no significant differences in mean age, percentage of patients over 65 years old, gender, or chemotherapy status between patients who received the H1N1 vaccine and those who declined it (Table 1). The mean age of patients who received the seasonal influenza vaccine was significantly higher than that of those who did not (67.8 ±12.1 vs. 56.1 ± 15.5 years, P < 0.0001), and a significantly higher percentage of patients in the vaccinated group were over the age of 65 (67% vs. 33%, P < 0.0001).
Patient-reported reasons for not receiving the H1N1 vaccine are shown in Figure 1. The two most common reasons for declining vaccination were beliefs that “the vaccine is dangerous because of lack of testing” (22%) and “I don't believe in vaccination in general” (18%). The belief that vaccination was dangerous or not effective because of the patient's medical condition represented 16% and 12% of responses, respectively. Six percent responded that receiving the vaccine would have been too inconvenient. No patients reported concerns about pain at the injection site as a reason for avoiding vaccination. In the category of “other,” responses fell into four broad categories: “physician advised against vaccination” (8%), “vaccination is unnecessary” (8%), “previous bad experience from vaccine” (4%), and “vaccine will make me sick” (4%).
Discussion
Our study found that 66% of patients being treated for hematological malignancies at a southwestern Ontario cancer center received the H1N1 vaccine during the 2009–2010 influenza season. This was higher than the rate of H1N1 vaccination in the general Canadian population, which was reported as 41%.14 Canadian cancer patients have been previously shown to have higher rates of participation in vaccination programs. In 2005, 64% of Canadians with cancer received the seasonal influenza vaccine compared with 34% of the overall population.13 This trend may be driven in part by the higher average age of patients receiving cancer treatment as adults 65 years of age or older comprised 52% of the respondents in our study.
Worldwide, Canada ranks among the highest countries in vaccination coverage. The United Kingdom reported a vaccination rate of 28.7% during the 2007–2008 influenza season, which was at the time one of the highest in Europe.19 Other European countries, including Germany, Italy, and France, showed vaccination rates similar to that of the United Kingdom. In all of these countries vaccination coverage increased with age. The United States has vaccination rates most similar to those of Canada, estimated at 40% in the overall population and 68% in the population ≥65 years old during the 2009–2010 influenza season.20
Higher vaccination rates have been reported in the elderly compared to younger adult population,[13] and [14] and our findings prove to be consistent with this reported trend. In this study, the group vaccinated with the seasonal influenza vaccine had a mean age of 67.8 ± 12.1 years compared with the unvaccinated group aged 56.1 ± 15.5 years (P < 0.0001). Interestingly, there was no significant difference in mean age between the vaccinated and unvaccinated groups for the H1N1 pandemic influenza vaccine (P > 0.05). This was not entirely unexpected since public health campaigns during the 2009–2010 influenza season focused on the younger age group due to their increased susceptibility to severe H1N1 disease. Nonetheless, there was a trend toward an increased mean age for those who received the vaccine (64.0 ± 12.5 years) compared to those who did not (60.4 ± 18.4 years), and it is possible that statistical significance was not reached due to the small sample size. Our study reported an alarmingly low 39% vaccination rate for seasonal influenza in cancer patients <65, suggesting that the PHAC's message is not adequately reaching this potentially at-risk group.
Reasons for refusal of vaccination have been well described in previous studies.[16], [17], [18], [21], [22], [23], [24], [25] and [26] We found that the most common reasons for refusal of vaccination by cancer patients were very similar to those reported in healthy individuals. Specifically, concerns about the safety and efficacy of vaccines in general were more common than concerns related to cancer or chemotherapy. The most common reasons for refusal of vaccination were “I think the vaccine will be dangerous for people in general because of lack of testing” (22%) and “I don't believe in vaccination in general” (18%). Despite the publicity, 8% of unvaccinated patients responded that they did not feel that H1N1 influenza was a significant threat. In this study, the belief that the vaccine was dangerous because of lack of testing or a previous medical condition was responsible for 13% of patients not receiving the vaccine. Five percent of patients elected not to be vaccinated because of questions of efficacy. The H1N1 vaccine is an adjuvant with AsO3, which may cause more vaccine reactions, while the seasonal influenza vaccine is not an adjuvant. It is possible that the presence of adjuvant contributed to some patients' safety concerns, though we did not specifically ask if the adjuvant influenced their decision.
Physician advice may have played a significant role in patients' decisions to vaccinate. Eight percent of patients who did not receive the vaccine reported that they were not vaccinated due to advice from a physician. It is our routine institutional policy to recommend vaccination for all cancer patients irrespective of underlying diagnosis or treatment regimen. We do not, however, provide standardized written information to patients or referring physicians, so some patients may have been advised against vaccination by other physicians. Some primary care physicians might not have been familiar with the current PHAC recommendations or the recent literature suggesting the vaccine's potential benefits in this group. Public health campaigns should therefore seek to educate physicians as well as patients regarding the safety and efficacy of the influenza vaccine for cancer patients.
Conclusion
We found that rates of H1N1 and seasonal influenza vaccination in a southwestern Ontario cancer center were higher than those reported for the general population. Nevertheless, despite a large public health education campaign, a significant number of patients declined vaccination due to fear that it would not be safe or effective or due to a belief that vaccination was not necessary. Although the rate of seasonal influenza vaccination was high for those ≥65 years old, it was poor for those aged <65 years, despite vaccination being recommended for all adults with chronic medical conditions. Future education programs should target younger patient populations and health-care workers and focus on vaccine safety and efficacy in immunocompromised patients as well as in other high-risk groups.
References1
1 Public Health Agency of Canada, FluWatch http://www.phac-aspc.gc.ca/fluwatch/09-10/w28_10/index-eng.php Accessed August 5, 2010.
2 Public Health Agency of Canada, Influenza http://www.phac-aspc.gc.ca/influenza/index-eng.php Accessed August 5, 2010.
3 S. James and T. Sargent, The Economic Impact of an Influenza Pandemic, Department of Finance Canada, Ottawa (2006), p. 90.
4 R.F. Chemaly, S. Ghosh, G.P. Bodey, N. Rohatgi, A. Safdar, M.J. Keating, R.E. Champlin, E.A. Aguilera, J.J. Tarrand and I.I. Raad, Respiratory viral infections in adults with hematologic malignancies and human stem cell transplantation recipients: a retrospective study at a major cancer center, Medicine 85 (5) (2006), pp. 278–287. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (66)
5 H.M. Yousuf, J. Englund, R. Couch, K. Rolston, M. Luna, J. Goodrich, V. Lewis, N.Q. Mirza, M. Andreeff, C. Koller, L. Elting, G.P. Bodey and E. Whimbey, Influenza among hospitalized adults with leukemia, Clin Infect Dis 24 (6) (1997), pp. 1095–1099. View Record in Scopus | Cited By in Scopus (55)
6 C.D. Cooksley, E.B. Avritscher, B.N. Bekele, K.V. Rolston, J.M. Geraci and L.S. Elting, Epidemiology and outcomes of serious influenza-related infections in the cancer population, Cancer 104 (3) (2005), pp. 618–628. View Record in Scopus | Cited By in Scopus (24)
7 L.S. Elting, E. Whimbey, W. Lo, R. Couch, M. Andreeff and G.P. Bodey, Epidemiology of influenza A virus infection in patients with acute or chronic leukemia, Support Care Cancer 3 (3) (1995), pp. 198–202. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (32)
8 E. Whimbey, L.S. Elting, R.B. Couch, W. Lo, L. Williams, R.E. Champlin and G.P. Bodey, Influenza A virus infections among hospitalized adult bone marrow transplant recipients, Bone Marrow Transplant 13 (4) (1994), pp. 437–440. View Record in Scopus | Cited By in Scopus (110)
9 M. Tiseo, B. Calatafimi, L. Ferri, A. Menardi and A. Ardizzoni, Efficacy and safety of influenza vaccination during chemotherapy treatment, J Support Oncol 8 (6) (2010), pp. 271–272. Article | 
10 D.A. Pollyea, J.M. Brown and S.J. Horning, Utility of influenza vaccination for oncology patients, J Clin Oncol 28 (14) (2010), pp. 2481–2490. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (8)
11 Public Health Agency of Canada, Guidance Document on the Use of Pandemic Influenza A (H1N1) 2009: Inactivated Monovalent Vaccine, Public Health Agency of Canada, Ottawa (2009).
12 Centers for Disease Control, 2009 H1N1 Vaccination Recommendations http://www.cdc.gov/h1n1flu/vaccination/acip.htm Accessed August 5, 2010.
13 J.C. Kwong, L.C. Rosella and H. Johansen, Trends in influenza vaccination in Canada, 1996/1997 to 2005, Health Rep 18 (4) (2007), pp. 9–19. View Record in Scopus | Cited By in Scopus (14)
14 Statistics Canda, Canadian Community Health Survey: H1N1 Vaccinations http://www.statcan.gc.ca/daily-quotidien/100719/dq100719b-eng.htm Accessed August 5, 2010.
15 S.S. Yee, P.R. Dutta, L.J. Solin, N. Vapiwala and G.D. Kao, Lack of compliance with national vaccination guidelines in oncology patients receiving radiation therapy, J Support Oncol 8 (1) (2010), pp. 28–34. View Record in Scopus | Cited By in Scopus (2)
16 P. Loulergue, O. Mir, J. Alexandre, S. Ropert, F. Goldwasser and O. Launay, Low influenza vaccination rate among patients receiving chemotherapy for cancer, Ann Oncol 19 (9) (2008), p. 1658. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (10)
17 R.K. Zimmerman, T.A. Santibanez, J.E. Janosky, M.J. Fine, M. Raymund, S.A. Wilson, I.J. Bardella, A.R. Medsger and M.P. Nowalk, What affects influenza vaccination rates among older patients?: An analysis from inner-city, suburban, rural, and Veterans Affairs practices, Am J Med 114 (1) (2003), pp. 31–38. Article |
18 M.W. Mah, N.A. Hagen, K. Pauling-Shepard, J.S. Hawthorne, M. Mysak, T. Lye and T.J. Louie, Understanding influenza vaccination attitudes at a Canadian cancer center, Am J Infect Control 33 (4) (2005), pp. 243–250. Article |
19 P.R. Blank, M. Schwenkglenks and T.D. Szucs, Vaccination coverage rates in eleven European countries during two consecutive influenza seasons, J Infect 58 (6) (2009), pp. 446–458. Article |
20 Centers for Disease Control and Prevention, Interim Results: State-Specific Seasonal Influenza Vaccination Coverage—United States, August 2009–January 2010, MMWR Morb Mortal Wkly Rep 59 (16) (2010), pp. 477–484.
21 X. Dedoukou, G. Nikolopoulos, A. Maragos, S. Giannoulidou and H.C. Maltezou, Attitudes towards vaccination against seasonal influenza of health-care workers in primary health-care settings in Greece, Vaccine 28 (37) (2010), pp. 5931–5933. Article |
22 J.N. Kent, C.S. Lea, X. Fang, L.F. Novick and J. Morgan, Seasonal influenza vaccination coverage among local health department personnel in North Carolina, 2007–2008, Am J Prev Med 39 (1) (2010), pp. 74–77. Article |
23 M. Madjid, A. Alfred, A. Sahai, J.L. Conyers and S.W. Casscells, Factors contributing to suboptimal vaccination against influenza: results of a nationwide telephone survey of persons with cardiovascular disease, Tex Heart Inst J 36 (6) (2009), pp. 546–552. View Record in Scopus | Cited By in Scopus (5)
24 K.W. To, S. Lee, T.O. Chan and S.S. Lee, Exploring determinants of acceptance of the pandemic influenza A (H1N1) 2009 vaccination in nurses, Am J Infect Control 38 (8) (2010), pp. 623–630. Article |
25 S.D. Torun and F. Torun, Vaccination against pandemic influenza A/H1N1 among healthcare workers and reasons for refusing vaccination in Istanbul in last pandemic alert phase, Vaccine 28 (35) (2010), pp. 5703–5710. Article |
26 S. Vírseda, M.A. Restrepo, E. Arranz, P. Magán-Tapia, M. Fernández-Ruiz, A.G. de la Cámara, J.M. Aguado and F. López-Medrano, Seasonal and pandemic A (H1N1) 2009 influenza vaccination coverage and attitudes among health-care workers in a Spanish university hospital, Vaccine 28 (30) (2010), pp. 4751–4757. Article |
Appendix
Questionnaire
- a) I do not think it will be effective for me because of my medical condition
b) I am concerned it might be dangerous for me because of my medical condition
c) I am concerned it might be dangerous for people in general because not enough testing has been done
d) Receiving the vaccination would be too inconvenient (long lineups, etc.)
_________________________________________
5) If you are not planning to get the H1N1 vaccine, what best describes your reason for not getting vaccinated? Please circle one.
- a) I do not think it will be effective for me because of my medical condition
b) I am concerned it might be dangerous for me because of my medical condition
c) I am concerned it might be dangerous for people in general because not enough testing has been done
d) Receiving the vaccination would be too inconvenient (long lineups, etc.)
8) If you are not planning to get the seasonal flu vaccine, what best describes your reason for not getting vaccinated? Please circle one.
_____________________________________
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
Original research
Benjamin H. Chin-Yeea, Katherine Monkman MDa, Zafar Hussain MD, FRCP(C)a and Leonard A. Minuk MD, FRCP(C)
Background
Patients with hematologic malignancies are at increased risk of influenza and its complications. Despite current health recommendations and evidence favoring influenza vaccination, vaccination rates remain low in cancer patients.
Objective
The purpose of this study was to determine which factors influenced vaccination rates.
Methods
During the 2009–2010 pandemic H1N1 and seasonal influenza season, we surveyed patients with hematologic malignancies in a Canadian cancer center. Of the patients participating in our study (n = 129), 66% and 57% received the H1N1 pandemic influenza and seasonal influenza vaccines, respectively.
Results
A number of reasons for vaccination refusal were reported, most relating to general skepticism about the safety and efficacy of vaccination. Physician advice was also a factor influencing vaccination rates in patients. The vaccination rate for seasonal influenza was 39% in patients <65 years old, significantly lower than the rate of 73% reported for patients aged ≥65 years (P < 0.0001).
Conclusion
Future education programs should target younger patient populations and health-care workers, focusing on vaccine safety and efficacy in the high-risk cancer population.
Despite the annual development of effective influenza vaccines, influenza remains a significant cause of morbidity and mortality in Canada. In the 2009–2010 influenza season, approximately 40,000 Canadians were infected with seasonal influenza or the pandemic H1N1 influenza virus,1 and influenza has been estimated to cause 4,000–8,000 deaths in Canada each year.2 It is estimated that a severe influenza pandemic could result in a 1% reduction in annual gross domestic product in Canada.3
Patients with hematologic malignancies are known to be at increased risk of influenza and its complications, with estimated mortality rates in the range 5%–27%.[4], [5], [6], [7] and [8] Evidence for the efficacy of the influenza vaccine is limited and contradictory, and many assume that immunocompromised patients will not be able to generate a protective antibody response. Nonetheless, current evidence favors vaccination.9 Pollyea et al10 reported that eight of 15 trials on the efficacy of vaccination in patients with hematologic malignancies concluded that vaccination was beneficial. Both the Centers for Disease Control and Prevention (CDC) and the Public Health Agency of Canada (PHAC) advised that all immunocompromised patients, including those with cancer, receive both the seasonal influenza vaccine and the pandemic H1N1 influenza vaccine in the 2009–2010 influenza season.[11] and [12]
Despite these recommendations, rates of influenza vaccination remain low for the general population and cancer patients in Canada, with rates reported at 40% and 65% respectively.[13] and [14] A recent study by Yee et al15 reported similarly low influenza vaccination rates of 58% in cancer patients in the United States. Vaccination has long been a controversial public health issue, and many people choose not to be vaccinated due to fears that vaccines may not be safe and effective.[16], [17] and [18] Lack of physician recommendation has also been cited as a significant factor in the decision to decline vaccination.16
In this study, we sought to determine what percentage of patients being treated for hematologic malignancies in an Ontario, Canada, cancer center received the H1N1 pandemic influenza vaccine in the 2009–2010 influenza season and to explore the barriers to vaccination in this high-risk population. We also collected information on the percentage of patients who received the seasonal influenza vaccine. It was general practice for physicians at this center to recommend influenza vaccination in accordance with the PHAC recommendations.
Methods
Patients being treated for hematologic malignancies at the London Regional Cancer Program (London, Canada) were invited to complete a survey regarding influenza vaccination (Appendix). The London Regional Cancer Program is a tertiary care center providing specialized cancer care to a population base of 1.2 million in southwestern Ontario. The survey was administered to patients eligible to participate in another study assessing antibody levels pre- and postvaccination with the H1N1 pandemic vaccine. Eligible patients were 18 years or older and being treated or followed for hematological malignancies at the London Regional Cancer Program who attended an appointment between October 28 and November 19, 2009, and returned for a follow-up visit between January 5 and March 26, 2010 (n = 151). Patients were asked if they had received the pandemic H1N1 influenza vaccine and the seasonal influenza vaccine during the 2009–2010 influenza season. Those who had declined vaccination were asked to describe the reasons for their choice. The survey provided a list of six possible reasons for declining vaccination and gave patients the option of writing in their own responses.
The results of the study were analyzed using InStat 3 software (GraphPad, La Jolla, CA). The Mann-Whitney U-test was used to compare continuous variables, and Fisher's exact test was used to compare proportions. The study was approved by the University of Western Ontario's Institutional Research Ethics Board (IRB 16627E).
Results
Of the 151 patients invited to participate, 129 completed the survey, yielding a response rate of 85%. Patient characteristics are shown in Table 1. The respondents ranged in age from 19 to 86 years, 56% were male and 44% were female, and patients aged 65 years or older comprised 52% of the study population. The mean age of the patient group was 62.7 ± 14.8 years. Overall 119 patients (92%) had received chemotherapy at some time during their illness, with 96 patients (76%) actively receiving chemotherapy, defined as treatment within the past 3 months. Diagnoses included acute leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, lymphoma, multiple myeloma, myelodysplastic syndromes, and myeloproliferative neoplasms.
Of the 129 patients surveyed, 85 (66%) reported that they had received the H1N1 pandemic influenza vaccine during the 2009–2010 influenza season. Fifty-seven percent had received the seasonal influenza vaccine, and 50% had received both the seasonal and the H1N1 vaccines. Of the 44 patients who did not receive the H1N1 vaccine, only three planned to receive it. Eight of the 56 patients not vaccinated with the seasonal influenza vaccine planned to receive it.
There were no significant differences in mean age, percentage of patients over 65 years old, gender, or chemotherapy status between patients who received the H1N1 vaccine and those who declined it (Table 1). The mean age of patients who received the seasonal influenza vaccine was significantly higher than that of those who did not (67.8 ±12.1 vs. 56.1 ± 15.5 years, P < 0.0001), and a significantly higher percentage of patients in the vaccinated group were over the age of 65 (67% vs. 33%, P < 0.0001).
Patient-reported reasons for not receiving the H1N1 vaccine are shown in Figure 1. The two most common reasons for declining vaccination were beliefs that “the vaccine is dangerous because of lack of testing” (22%) and “I don't believe in vaccination in general” (18%). The belief that vaccination was dangerous or not effective because of the patient's medical condition represented 16% and 12% of responses, respectively. Six percent responded that receiving the vaccine would have been too inconvenient. No patients reported concerns about pain at the injection site as a reason for avoiding vaccination. In the category of “other,” responses fell into four broad categories: “physician advised against vaccination” (8%), “vaccination is unnecessary” (8%), “previous bad experience from vaccine” (4%), and “vaccine will make me sick” (4%).
Discussion
Our study found that 66% of patients being treated for hematological malignancies at a southwestern Ontario cancer center received the H1N1 vaccine during the 2009–2010 influenza season. This was higher than the rate of H1N1 vaccination in the general Canadian population, which was reported as 41%.14 Canadian cancer patients have been previously shown to have higher rates of participation in vaccination programs. In 2005, 64% of Canadians with cancer received the seasonal influenza vaccine compared with 34% of the overall population.13 This trend may be driven in part by the higher average age of patients receiving cancer treatment as adults 65 years of age or older comprised 52% of the respondents in our study.
Worldwide, Canada ranks among the highest countries in vaccination coverage. The United Kingdom reported a vaccination rate of 28.7% during the 2007–2008 influenza season, which was at the time one of the highest in Europe.19 Other European countries, including Germany, Italy, and France, showed vaccination rates similar to that of the United Kingdom. In all of these countries vaccination coverage increased with age. The United States has vaccination rates most similar to those of Canada, estimated at 40% in the overall population and 68% in the population ≥65 years old during the 2009–2010 influenza season.20
Higher vaccination rates have been reported in the elderly compared to younger adult population,[13] and [14] and our findings prove to be consistent with this reported trend. In this study, the group vaccinated with the seasonal influenza vaccine had a mean age of 67.8 ± 12.1 years compared with the unvaccinated group aged 56.1 ± 15.5 years (P < 0.0001). Interestingly, there was no significant difference in mean age between the vaccinated and unvaccinated groups for the H1N1 pandemic influenza vaccine (P > 0.05). This was not entirely unexpected since public health campaigns during the 2009–2010 influenza season focused on the younger age group due to their increased susceptibility to severe H1N1 disease. Nonetheless, there was a trend toward an increased mean age for those who received the vaccine (64.0 ± 12.5 years) compared to those who did not (60.4 ± 18.4 years), and it is possible that statistical significance was not reached due to the small sample size. Our study reported an alarmingly low 39% vaccination rate for seasonal influenza in cancer patients <65, suggesting that the PHAC's message is not adequately reaching this potentially at-risk group.
Reasons for refusal of vaccination have been well described in previous studies.[16], [17], [18], [21], [22], [23], [24], [25] and [26] We found that the most common reasons for refusal of vaccination by cancer patients were very similar to those reported in healthy individuals. Specifically, concerns about the safety and efficacy of vaccines in general were more common than concerns related to cancer or chemotherapy. The most common reasons for refusal of vaccination were “I think the vaccine will be dangerous for people in general because of lack of testing” (22%) and “I don't believe in vaccination in general” (18%). Despite the publicity, 8% of unvaccinated patients responded that they did not feel that H1N1 influenza was a significant threat. In this study, the belief that the vaccine was dangerous because of lack of testing or a previous medical condition was responsible for 13% of patients not receiving the vaccine. Five percent of patients elected not to be vaccinated because of questions of efficacy. The H1N1 vaccine is an adjuvant with AsO3, which may cause more vaccine reactions, while the seasonal influenza vaccine is not an adjuvant. It is possible that the presence of adjuvant contributed to some patients' safety concerns, though we did not specifically ask if the adjuvant influenced their decision.
Physician advice may have played a significant role in patients' decisions to vaccinate. Eight percent of patients who did not receive the vaccine reported that they were not vaccinated due to advice from a physician. It is our routine institutional policy to recommend vaccination for all cancer patients irrespective of underlying diagnosis or treatment regimen. We do not, however, provide standardized written information to patients or referring physicians, so some patients may have been advised against vaccination by other physicians. Some primary care physicians might not have been familiar with the current PHAC recommendations or the recent literature suggesting the vaccine's potential benefits in this group. Public health campaigns should therefore seek to educate physicians as well as patients regarding the safety and efficacy of the influenza vaccine for cancer patients.
Conclusion
We found that rates of H1N1 and seasonal influenza vaccination in a southwestern Ontario cancer center were higher than those reported for the general population. Nevertheless, despite a large public health education campaign, a significant number of patients declined vaccination due to fear that it would not be safe or effective or due to a belief that vaccination was not necessary. Although the rate of seasonal influenza vaccination was high for those ≥65 years old, it was poor for those aged <65 years, despite vaccination being recommended for all adults with chronic medical conditions. Future education programs should target younger patient populations and health-care workers and focus on vaccine safety and efficacy in immunocompromised patients as well as in other high-risk groups.
References1
1 Public Health Agency of Canada, FluWatch http://www.phac-aspc.gc.ca/fluwatch/09-10/w28_10/index-eng.php Accessed August 5, 2010.
2 Public Health Agency of Canada, Influenza http://www.phac-aspc.gc.ca/influenza/index-eng.php Accessed August 5, 2010.
3 S. James and T. Sargent, The Economic Impact of an Influenza Pandemic, Department of Finance Canada, Ottawa (2006), p. 90.
4 R.F. Chemaly, S. Ghosh, G.P. Bodey, N. Rohatgi, A. Safdar, M.J. Keating, R.E. Champlin, E.A. Aguilera, J.J. Tarrand and I.I. Raad, Respiratory viral infections in adults with hematologic malignancies and human stem cell transplantation recipients: a retrospective study at a major cancer center, Medicine 85 (5) (2006), pp. 278–287. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (66)
5 H.M. Yousuf, J. Englund, R. Couch, K. Rolston, M. Luna, J. Goodrich, V. Lewis, N.Q. Mirza, M. Andreeff, C. Koller, L. Elting, G.P. Bodey and E. Whimbey, Influenza among hospitalized adults with leukemia, Clin Infect Dis 24 (6) (1997), pp. 1095–1099. View Record in Scopus | Cited By in Scopus (55)
6 C.D. Cooksley, E.B. Avritscher, B.N. Bekele, K.V. Rolston, J.M. Geraci and L.S. Elting, Epidemiology and outcomes of serious influenza-related infections in the cancer population, Cancer 104 (3) (2005), pp. 618–628. View Record in Scopus | Cited By in Scopus (24)
7 L.S. Elting, E. Whimbey, W. Lo, R. Couch, M. Andreeff and G.P. Bodey, Epidemiology of influenza A virus infection in patients with acute or chronic leukemia, Support Care Cancer 3 (3) (1995), pp. 198–202. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (32)
8 E. Whimbey, L.S. Elting, R.B. Couch, W. Lo, L. Williams, R.E. Champlin and G.P. Bodey, Influenza A virus infections among hospitalized adult bone marrow transplant recipients, Bone Marrow Transplant 13 (4) (1994), pp. 437–440. View Record in Scopus | Cited By in Scopus (110)
9 M. Tiseo, B. Calatafimi, L. Ferri, A. Menardi and A. Ardizzoni, Efficacy and safety of influenza vaccination during chemotherapy treatment, J Support Oncol 8 (6) (2010), pp. 271–272. Article |
10 D.A. Pollyea, J.M. Brown and S.J. Horning, Utility of influenza vaccination for oncology patients, J Clin Oncol 28 (14) (2010), pp. 2481–2490. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (8)
11 Public Health Agency of Canada, Guidance Document on the Use of Pandemic Influenza A (H1N1) 2009: Inactivated Monovalent Vaccine, Public Health Agency of Canada, Ottawa (2009).
12 Centers for Disease Control, 2009 H1N1 Vaccination Recommendations http://www.cdc.gov/h1n1flu/vaccination/acip.htm Accessed August 5, 2010.
13 J.C. Kwong, L.C. Rosella and H. Johansen, Trends in influenza vaccination in Canada, 1996/1997 to 2005, Health Rep 18 (4) (2007), pp. 9–19. View Record in Scopus | Cited By in Scopus (14)
14 Statistics Canda, Canadian Community Health Survey: H1N1 Vaccinations http://www.statcan.gc.ca/daily-quotidien/100719/dq100719b-eng.htm Accessed August 5, 2010.
15 S.S. Yee, P.R. Dutta, L.J. Solin, N. Vapiwala and G.D. Kao, Lack of compliance with national vaccination guidelines in oncology patients receiving radiation therapy, J Support Oncol 8 (1) (2010), pp. 28–34. View Record in Scopus | Cited By in Scopus (2)
16 P. Loulergue, O. Mir, J. Alexandre, S. Ropert, F. Goldwasser and O. Launay, Low influenza vaccination rate among patients receiving chemotherapy for cancer, Ann Oncol 19 (9) (2008), p. 1658. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (10)
17 R.K. Zimmerman, T.A. Santibanez, J.E. Janosky, M.J. Fine, M. Raymund, S.A. Wilson, I.J. Bardella, A.R. Medsger and M.P. Nowalk, What affects influenza vaccination rates among older patients?: An analysis from inner-city, suburban, rural, and Veterans Affairs practices, Am J Med 114 (1) (2003), pp. 31–38. Article |
18 M.W. Mah, N.A. Hagen, K. Pauling-Shepard, J.S. Hawthorne, M. Mysak, T. Lye and T.J. Louie, Understanding influenza vaccination attitudes at a Canadian cancer center, Am J Infect Control 33 (4) (2005), pp. 243–250. Article |
19 P.R. Blank, M. Schwenkglenks and T.D. Szucs, Vaccination coverage rates in eleven European countries during two consecutive influenza seasons, J Infect 58 (6) (2009), pp. 446–458. Article |
20 Centers for Disease Control and Prevention, Interim Results: State-Specific Seasonal Influenza Vaccination Coverage—United States, August 2009–January 2010, MMWR Morb Mortal Wkly Rep 59 (16) (2010), pp. 477–484.
21 X. Dedoukou, G. Nikolopoulos, A. Maragos, S. Giannoulidou and H.C. Maltezou, Attitudes towards vaccination against seasonal influenza of health-care workers in primary health-care settings in Greece, Vaccine 28 (37) (2010), pp. 5931–5933. Article |
22 J.N. Kent, C.S. Lea, X. Fang, L.F. Novick and J. Morgan, Seasonal influenza vaccination coverage among local health department personnel in North Carolina, 2007–2008, Am J Prev Med 39 (1) (2010), pp. 74–77. Article |
23 M. Madjid, A. Alfred, A. Sahai, J.L. Conyers and S.W. Casscells, Factors contributing to suboptimal vaccination against influenza: results of a nationwide telephone survey of persons with cardiovascular disease, Tex Heart Inst J 36 (6) (2009), pp. 546–552. View Record in Scopus | Cited By in Scopus (5)
24 K.W. To, S. Lee, T.O. Chan and S.S. Lee, Exploring determinants of acceptance of the pandemic influenza A (H1N1) 2009 vaccination in nurses, Am J Infect Control 38 (8) (2010), pp. 623–630. Article |
25 S.D. Torun and F. Torun, Vaccination against pandemic influenza A/H1N1 among healthcare workers and reasons for refusing vaccination in Istanbul in last pandemic alert phase, Vaccine 28 (35) (2010), pp. 5703–5710. Article |
26 S. Vírseda, M.A. Restrepo, E. Arranz, P. Magán-Tapia, M. Fernández-Ruiz, A.G. de la Cámara, J.M. Aguado and F. López-Medrano, Seasonal and pandemic A (H1N1) 2009 influenza vaccination coverage and attitudes among health-care workers in a Spanish university hospital, Vaccine 28 (30) (2010), pp. 4751–4757. Article |
Appendix
Questionnaire
- a) I do not think it will be effective for me because of my medical condition
b) I am concerned it might be dangerous for me because of my medical condition
c) I am concerned it might be dangerous for people in general because not enough testing has been done
d) Receiving the vaccination would be too inconvenient (long lineups, etc.)
_________________________________________
5) If you are not planning to get the H1N1 vaccine, what best describes your reason for not getting vaccinated? Please circle one.
- a) I do not think it will be effective for me because of my medical condition
b) I am concerned it might be dangerous for me because of my medical condition
c) I am concerned it might be dangerous for people in general because not enough testing has been done
d) Receiving the vaccination would be too inconvenient (long lineups, etc.)
8) If you are not planning to get the seasonal flu vaccine, what best describes your reason for not getting vaccinated? Please circle one.
_____________________________________
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
Original research
Benjamin H. Chin-Yeea, Katherine Monkman MDa, Zafar Hussain MD, FRCP(C)a and Leonard A. Minuk MD, FRCP(C)
Background
Patients with hematologic malignancies are at increased risk of influenza and its complications. Despite current health recommendations and evidence favoring influenza vaccination, vaccination rates remain low in cancer patients.
Objective
The purpose of this study was to determine which factors influenced vaccination rates.
Methods
During the 2009–2010 pandemic H1N1 and seasonal influenza season, we surveyed patients with hematologic malignancies in a Canadian cancer center. Of the patients participating in our study (n = 129), 66% and 57% received the H1N1 pandemic influenza and seasonal influenza vaccines, respectively.
Results
A number of reasons for vaccination refusal were reported, most relating to general skepticism about the safety and efficacy of vaccination. Physician advice was also a factor influencing vaccination rates in patients. The vaccination rate for seasonal influenza was 39% in patients <65 years old, significantly lower than the rate of 73% reported for patients aged ≥65 years (P < 0.0001).
Conclusion
Future education programs should target younger patient populations and health-care workers, focusing on vaccine safety and efficacy in the high-risk cancer population.
Despite the annual development of effective influenza vaccines, influenza remains a significant cause of morbidity and mortality in Canada. In the 2009–2010 influenza season, approximately 40,000 Canadians were infected with seasonal influenza or the pandemic H1N1 influenza virus,1 and influenza has been estimated to cause 4,000–8,000 deaths in Canada each year.2 It is estimated that a severe influenza pandemic could result in a 1% reduction in annual gross domestic product in Canada.3
Patients with hematologic malignancies are known to be at increased risk of influenza and its complications, with estimated mortality rates in the range 5%–27%.[4], [5], [6], [7] and [8] Evidence for the efficacy of the influenza vaccine is limited and contradictory, and many assume that immunocompromised patients will not be able to generate a protective antibody response. Nonetheless, current evidence favors vaccination.9 Pollyea et al10 reported that eight of 15 trials on the efficacy of vaccination in patients with hematologic malignancies concluded that vaccination was beneficial. Both the Centers for Disease Control and Prevention (CDC) and the Public Health Agency of Canada (PHAC) advised that all immunocompromised patients, including those with cancer, receive both the seasonal influenza vaccine and the pandemic H1N1 influenza vaccine in the 2009–2010 influenza season.[11] and [12]
Despite these recommendations, rates of influenza vaccination remain low for the general population and cancer patients in Canada, with rates reported at 40% and 65% respectively.[13] and [14] A recent study by Yee et al15 reported similarly low influenza vaccination rates of 58% in cancer patients in the United States. Vaccination has long been a controversial public health issue, and many people choose not to be vaccinated due to fears that vaccines may not be safe and effective.[16], [17] and [18] Lack of physician recommendation has also been cited as a significant factor in the decision to decline vaccination.16
In this study, we sought to determine what percentage of patients being treated for hematologic malignancies in an Ontario, Canada, cancer center received the H1N1 pandemic influenza vaccine in the 2009–2010 influenza season and to explore the barriers to vaccination in this high-risk population. We also collected information on the percentage of patients who received the seasonal influenza vaccine. It was general practice for physicians at this center to recommend influenza vaccination in accordance with the PHAC recommendations.
Methods
Patients being treated for hematologic malignancies at the London Regional Cancer Program (London, Canada) were invited to complete a survey regarding influenza vaccination (Appendix). The London Regional Cancer Program is a tertiary care center providing specialized cancer care to a population base of 1.2 million in southwestern Ontario. The survey was administered to patients eligible to participate in another study assessing antibody levels pre- and postvaccination with the H1N1 pandemic vaccine. Eligible patients were 18 years or older and being treated or followed for hematological malignancies at the London Regional Cancer Program who attended an appointment between October 28 and November 19, 2009, and returned for a follow-up visit between January 5 and March 26, 2010 (n = 151). Patients were asked if they had received the pandemic H1N1 influenza vaccine and the seasonal influenza vaccine during the 2009–2010 influenza season. Those who had declined vaccination were asked to describe the reasons for their choice. The survey provided a list of six possible reasons for declining vaccination and gave patients the option of writing in their own responses.
The results of the study were analyzed using InStat 3 software (GraphPad, La Jolla, CA). The Mann-Whitney U-test was used to compare continuous variables, and Fisher's exact test was used to compare proportions. The study was approved by the University of Western Ontario's Institutional Research Ethics Board (IRB 16627E).
Results
Of the 151 patients invited to participate, 129 completed the survey, yielding a response rate of 85%. Patient characteristics are shown in Table 1. The respondents ranged in age from 19 to 86 years, 56% were male and 44% were female, and patients aged 65 years or older comprised 52% of the study population. The mean age of the patient group was 62.7 ± 14.8 years. Overall 119 patients (92%) had received chemotherapy at some time during their illness, with 96 patients (76%) actively receiving chemotherapy, defined as treatment within the past 3 months. Diagnoses included acute leukemia, chronic lymphocytic leukemia, chronic myeloid leukemia, lymphoma, multiple myeloma, myelodysplastic syndromes, and myeloproliferative neoplasms.
Of the 129 patients surveyed, 85 (66%) reported that they had received the H1N1 pandemic influenza vaccine during the 2009–2010 influenza season. Fifty-seven percent had received the seasonal influenza vaccine, and 50% had received both the seasonal and the H1N1 vaccines. Of the 44 patients who did not receive the H1N1 vaccine, only three planned to receive it. Eight of the 56 patients not vaccinated with the seasonal influenza vaccine planned to receive it.
There were no significant differences in mean age, percentage of patients over 65 years old, gender, or chemotherapy status between patients who received the H1N1 vaccine and those who declined it (Table 1). The mean age of patients who received the seasonal influenza vaccine was significantly higher than that of those who did not (67.8 ±12.1 vs. 56.1 ± 15.5 years, P < 0.0001), and a significantly higher percentage of patients in the vaccinated group were over the age of 65 (67% vs. 33%, P < 0.0001).
Patient-reported reasons for not receiving the H1N1 vaccine are shown in Figure 1. The two most common reasons for declining vaccination were beliefs that “the vaccine is dangerous because of lack of testing” (22%) and “I don't believe in vaccination in general” (18%). The belief that vaccination was dangerous or not effective because of the patient's medical condition represented 16% and 12% of responses, respectively. Six percent responded that receiving the vaccine would have been too inconvenient. No patients reported concerns about pain at the injection site as a reason for avoiding vaccination. In the category of “other,” responses fell into four broad categories: “physician advised against vaccination” (8%), “vaccination is unnecessary” (8%), “previous bad experience from vaccine” (4%), and “vaccine will make me sick” (4%).
Discussion
Our study found that 66% of patients being treated for hematological malignancies at a southwestern Ontario cancer center received the H1N1 vaccine during the 2009–2010 influenza season. This was higher than the rate of H1N1 vaccination in the general Canadian population, which was reported as 41%.14 Canadian cancer patients have been previously shown to have higher rates of participation in vaccination programs. In 2005, 64% of Canadians with cancer received the seasonal influenza vaccine compared with 34% of the overall population.13 This trend may be driven in part by the higher average age of patients receiving cancer treatment as adults 65 years of age or older comprised 52% of the respondents in our study.
Worldwide, Canada ranks among the highest countries in vaccination coverage. The United Kingdom reported a vaccination rate of 28.7% during the 2007–2008 influenza season, which was at the time one of the highest in Europe.19 Other European countries, including Germany, Italy, and France, showed vaccination rates similar to that of the United Kingdom. In all of these countries vaccination coverage increased with age. The United States has vaccination rates most similar to those of Canada, estimated at 40% in the overall population and 68% in the population ≥65 years old during the 2009–2010 influenza season.20
Higher vaccination rates have been reported in the elderly compared to younger adult population,[13] and [14] and our findings prove to be consistent with this reported trend. In this study, the group vaccinated with the seasonal influenza vaccine had a mean age of 67.8 ± 12.1 years compared with the unvaccinated group aged 56.1 ± 15.5 years (P < 0.0001). Interestingly, there was no significant difference in mean age between the vaccinated and unvaccinated groups for the H1N1 pandemic influenza vaccine (P > 0.05). This was not entirely unexpected since public health campaigns during the 2009–2010 influenza season focused on the younger age group due to their increased susceptibility to severe H1N1 disease. Nonetheless, there was a trend toward an increased mean age for those who received the vaccine (64.0 ± 12.5 years) compared to those who did not (60.4 ± 18.4 years), and it is possible that statistical significance was not reached due to the small sample size. Our study reported an alarmingly low 39% vaccination rate for seasonal influenza in cancer patients <65, suggesting that the PHAC's message is not adequately reaching this potentially at-risk group.
Reasons for refusal of vaccination have been well described in previous studies.[16], [17], [18], [21], [22], [23], [24], [25] and [26] We found that the most common reasons for refusal of vaccination by cancer patients were very similar to those reported in healthy individuals. Specifically, concerns about the safety and efficacy of vaccines in general were more common than concerns related to cancer or chemotherapy. The most common reasons for refusal of vaccination were “I think the vaccine will be dangerous for people in general because of lack of testing” (22%) and “I don't believe in vaccination in general” (18%). Despite the publicity, 8% of unvaccinated patients responded that they did not feel that H1N1 influenza was a significant threat. In this study, the belief that the vaccine was dangerous because of lack of testing or a previous medical condition was responsible for 13% of patients not receiving the vaccine. Five percent of patients elected not to be vaccinated because of questions of efficacy. The H1N1 vaccine is an adjuvant with AsO3, which may cause more vaccine reactions, while the seasonal influenza vaccine is not an adjuvant. It is possible that the presence of adjuvant contributed to some patients' safety concerns, though we did not specifically ask if the adjuvant influenced their decision.
Physician advice may have played a significant role in patients' decisions to vaccinate. Eight percent of patients who did not receive the vaccine reported that they were not vaccinated due to advice from a physician. It is our routine institutional policy to recommend vaccination for all cancer patients irrespective of underlying diagnosis or treatment regimen. We do not, however, provide standardized written information to patients or referring physicians, so some patients may have been advised against vaccination by other physicians. Some primary care physicians might not have been familiar with the current PHAC recommendations or the recent literature suggesting the vaccine's potential benefits in this group. Public health campaigns should therefore seek to educate physicians as well as patients regarding the safety and efficacy of the influenza vaccine for cancer patients.
Conclusion
We found that rates of H1N1 and seasonal influenza vaccination in a southwestern Ontario cancer center were higher than those reported for the general population. Nevertheless, despite a large public health education campaign, a significant number of patients declined vaccination due to fear that it would not be safe or effective or due to a belief that vaccination was not necessary. Although the rate of seasonal influenza vaccination was high for those ≥65 years old, it was poor for those aged <65 years, despite vaccination being recommended for all adults with chronic medical conditions. Future education programs should target younger patient populations and health-care workers and focus on vaccine safety and efficacy in immunocompromised patients as well as in other high-risk groups.
References1
1 Public Health Agency of Canada, FluWatch http://www.phac-aspc.gc.ca/fluwatch/09-10/w28_10/index-eng.php Accessed August 5, 2010.
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10 D.A. Pollyea, J.M. Brown and S.J. Horning, Utility of influenza vaccination for oncology patients, J Clin Oncol 28 (14) (2010), pp. 2481–2490. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (8)
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12 Centers for Disease Control, 2009 H1N1 Vaccination Recommendations http://www.cdc.gov/h1n1flu/vaccination/acip.htm Accessed August 5, 2010.
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18 M.W. Mah, N.A. Hagen, K. Pauling-Shepard, J.S. Hawthorne, M. Mysak, T. Lye and T.J. Louie, Understanding influenza vaccination attitudes at a Canadian cancer center, Am J Infect Control 33 (4) (2005), pp. 243–250. Article |
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21 X. Dedoukou, G. Nikolopoulos, A. Maragos, S. Giannoulidou and H.C. Maltezou, Attitudes towards vaccination against seasonal influenza of health-care workers in primary health-care settings in Greece, Vaccine 28 (37) (2010), pp. 5931–5933. Article |
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Appendix
Questionnaire
- a) I do not think it will be effective for me because of my medical condition
b) I am concerned it might be dangerous for me because of my medical condition
c) I am concerned it might be dangerous for people in general because not enough testing has been done
d) Receiving the vaccination would be too inconvenient (long lineups, etc.)
_________________________________________
5) If you are not planning to get the H1N1 vaccine, what best describes your reason for not getting vaccinated? Please circle one.
- a) I do not think it will be effective for me because of my medical condition
b) I am concerned it might be dangerous for me because of my medical condition
c) I am concerned it might be dangerous for people in general because not enough testing has been done
d) Receiving the vaccination would be too inconvenient (long lineups, etc.)
8) If you are not planning to get the seasonal flu vaccine, what best describes your reason for not getting vaccinated? Please circle one.
_____________________________________
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
Spirituality, patients' worry, and follow-up health-care utilization among cancer survivors
Background Spirituality may aid cancer survivors as they attempt to interpret the meaning of their experience.
Objective We examined the relationship between spirituality, patient-rated worry, and health-care utilization among 551 cancer survivors with different malignancies, who were evaluated prospectively.
Methods Baseline spirituality scores were categorized into low and high spirituality groups. Patient-rated worries regarding disease recurrence/progression, developing new cancer, and developing complications from treatment were collected at baseline and at 6 and 12 months. Follow-up health-care utilization was also examined at 6 and 12 months.
Results Among the survivors, 271 (49%) reported low spirituality and 280 (51%) reported high spirituality. Of the cohort, 59% had some kind of worry regarding disease recurrence/progression, development of new cancers, and treatment complications. Highly spiritual survivors were less likely to have high levels of worries at both 6 and 12 months. Highly worried survivors were significantly more likely to place phone calls to their follow-up providers and had more frequent follow-up visits at 6 and 12 months. No interactions between spirituality and level of worry were noted to affect follow-up health-care utilization.
Conclusion Given spirituality's effect on anxiety, spirituality-based intervention may have a role in addressing cancer survivors' worries but may not improve health-care utilization.
Article Outline
- Results
- Study Participation
- Characteristics of Study Participants
- Prevalence of Spirituality and Patient Worry
- Relationship Between Spirituality and Patient Worry
- Relationship Between Patient Worry and Follow-Up Health-Care Utilization
- Relationship Between Spirituality and Health-Care Utilization
- Interaction Between Spirituality and Patient Worry With Health-Care Utilization
Receiving a diagnosis of cancer is a life-changing event. Patients commonly seek understanding of not only the medical aspects of their disease but also how the diagnosis will affect their lives. Often, this quest to understand the meaning behind the unfortunate circumstance of disease is aided by spirituality. Spirituality motivates an individual to find meaning or purpose in his or her life experience.1 Most studies indicate that spirituality gives meaningful insight to an individual's existence and aids in the interpretation of events and relationships.[2], [3], [4], [5], [6], [7], [8] and [9]
Spiritual beliefs are widespread among cancer patients. Studies have shown that a better quality of life (QOL) is achieved in patients who practice spirituality or have those needs met by their health-care providers. They require less health care as well as experience less anxiety and a greater sense of well-being.[10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] and [21] One may conclude that spirituality helps patients understand the meaning of their disease and provides the catalyst for significant improvement in health-related outcomes.
Vast amounts of literature affirm spirituality's positive effects on health outcomes for advanced-stage/terminally ill patients. However, very little is known about how spirituality affects the common concerns of cancer survivors. It can be inferred that spirituality continues to aid cancer survivors as they attempt to interpret the meaning of their experience during follow-up care. After completing various cancer treatments, survivors may experience worries of cancer recurrence or progression, worries of developing a different cancer, and worries of developing complications from their initial treatment.22 We explored the relationship between spirituality, patient-rated cancer-related worry, and cancer survivors' follow-up health-care utilization (follow-up doctor visits, phone calls to follow-up providers regarding medical inquiries, and emergency room visits).
Participants and Methods
Subject Selection and Eligibility
Data for this study were obtained from CANCER CARE, an observational cohort study using a self-administered questionnaire designed to evaluate follow-up care among cancer survivors.23 Participants were seen at the University of Nebraska Medical Center (UNMC) and consented to participate in a data-collection protocol (ONCOBASE) since March 2006. ONCOBASE has a 90% consent rate. To be eligible for the study, participants were at least 19 years of age (age of majority in Nebraska) and completed their cancer treatment at UNMC. Participants varied in time since completion of last cancer treatment. From a list of 5,500 eligible subjects, 2,500 were screened. The list was sorted by date of consent, and the first 2,000 subjects received the study questionnaire. Survey forms were mailed in August 2008 (baseline) and follow-up surveys were mailed in February (month 6) and August 2009 (month 12). Participants were not paid for study participation but were told that a donation to a charitable institution was made on their behalf as an altruistic incentive.23 The study was approved by the Institutional Review Board at UNMC.
Variables Analyzed
We analyzed the participants' spirituality from baseline surveys using the Functional Assessment of Cancer Therapy–Spirituality Scale (FACT-SP).24 Total spirituality scores were computed for each participant using instrument standard calculations. The cohort was categorized into two groups, consisting of low or high spirituality based on the median calculated score (<47 vs. ≥47) for the entire population. Other variables included in the analyses are shown in Table 1. Patient-rated worry pertaining to (1) disease recurrence/progression, (2) development of a new malignancy, and (3) complications related to treatment were evaluated at baseline and at 6 and 12 months. Respondents were asked to rate their level of worry for each of the above three items using a five-point Likert scale (none at all, little of the time, some of the time, most of the time, and all of the time). Each worry item was categorized as low (none at all to a little of the time) vs. high (some of the time, most of the time, all of the time). Follow-up health-care utilization was assessed at 6 and 12 months and consisted of (1) follow-up clinic visits (low, defined as none or one follow-up visit per year, vs. high, more than one follow-up visit per year), (2) phone calls to follow-up providers for medical issues (no vs. yes), and (3) emergency room visits (no vs. yes). These indices of health-care utilization were selected on the basis of whether they are discretionary (patient-driven) or nondiscretionary (physician-driven).[25] and [26] For example, follow-up clinic visits are mainly nondiscretionary in the sense that the follow-up provider primarily determines the frequency at which they are conducted, while phone calls made to follow-up providers and emergency room visits are inherently discretionary. We also evaluated the relationships between spirituality and QOL (Short Form 12 [SF-12]),27 social support,28 and religiosity (with the survey question [data not shown] “Overall, how much would you say that religious beliefs have influenced your life in the past two months?”), to establish the external validity of our spirituality cut-off score since these constructs have been associated with spirituality.[10], [15], [17], [19], [29], [30] and [31] Our analyses showed a high correlation between our categorization of low or high spirituality with QOL, social support, and religiosity.
| EVALUABLE (N) | LOW SPIRITUALITY | HIGH SPIRITUALITY | P | |||
|---|---|---|---|---|---|---|
| FREQUENCY | PERCENT | FREQUENCY | PERCENT | |||
| n | 551 | 271 | 49 | 280 | 51 | |
| Median age (range) | 59 (19–85) | 59 (22–83) | 0.99 | |||
| ≤40 | 551 | 17 | 6 | 21 | 8 | 0.78 |
| 41–60 | 137 | 51 | 135 | 48 | ||
| >60 | 117 | 43 | 124 | 44 | ||
| Sex | ||||||
| Female | 551 | 112 | 41 | 89 | 32 | 0.02 |
| Male | 159 | 59 | 191 | 68 | ||
| Race/ethnicity | ||||||
| White | 551 | 256 | 94 | 272 | 97 | 0.21 |
| Hispanic | 6 | 2 | 2 | 1 | ||
| African American | 3 | 1 | 4 | 1 | ||
| Other | 6 | 2 | 2 | 1 | ||
| Marital status | ||||||
| Single/never married | 551 | 14 | 5 | 19 | 7 | 0.67 |
| Married | 219 | 81 | 219 | 78 | ||
| Divorced/widowed | 38 | 14 | 42 | 15 | ||
| Education | ||||||
| High school | 551 | 90 | 33 | 83 | 30 | 0.49 |
| College | 105 | 39 | 122 | 44 | ||
| Postgraduate | 76 | 28 | 75 | 27 | ||
| Religion | ||||||
| Protestant | 551 | 121 | 45 | 161 | 58 | <0.01 |
| Catholic | 101 | 37 | 80 | 29 | ||
| Other | 36 | 13 | 35 | 13 | ||
| None/atheist | 13 | 5 | 4 | 1 | ||
| Income (US$) | ||||||
| <25,000 | 551 | 37 | 14 | 37 | 13 | 0.71 |
| 25,000–49,999 | 64 | 24 | 61 | 22 | ||
| 50,000–74,999 | 59 | 22 | 54 | 19 | ||
| 75,000–100,000 | 35 | 13 | 44 | 16 | ||
| >100,000 | 57 | 21 | 56 | 20 | ||
| Missing | 19 | 7 | 28 | 10 | ||
| Place of residence | ||||||
| Urban | 551 | 194 | 72 | 201 | 72 | 0.96 |
| Rural | 77 | 28 | 79 | 28 | ||
| Distance (miles) | ||||||
| ≤15 | 551 | 108 | 40 | 98 | 35 | 0.32 |
| 15–100 | 83 | 31 | 94 | 34 | ||
| 100–250 | 44 | 16 | 58 | 21 | ||
| >250 | 36 | 13 | 30 | 11 | ||
| Employment status | ||||||
| Full time | 551 | 160 | 59 | 163 | 58 | 0.93 |
| Part time | 22 | 8 | 27 | 10 | ||
| Homemaker | 25 | 9 | 26 | 9 | ||
| Student | 3 | 1 | 4 | 1 | ||
| Retired | 48 | 18 | 51 | 18 | ||
| Other | 13 | 5 | 9 | 3 | ||
| Patient is the primary income provider | 551 | 137 | 51 | 132 | 47 | 0.42 |
| Insurance | ||||||
| Employer-based | 551 | 149 | 55 | 153 | 55 | 0.95 |
| Individual-based | 47 | 17 | 48 | 17 | ||
| Medicare/Medicaid | 56 | 21 | 59 | 21 | ||
| Other | 17 | 6 | 16 | 6 | ||
| None | 2 | 1 | 4 | 1 | ||
| Prescription insurance | 551 | 239 | 88 | 242 | 86 | 0.53 |
| Type of malignancy | ||||||
| Leukemia, lymphoma, multiple myeloma | 551 | 136 | 50 | 147 | 53 | 0.86 |
| Breast, colon, prostate | 101 | 37 | 100 | 36 | ||
| Lung, pancreatic | 34 | 13 | 33 | 12 | ||
| Median time from diagnosis to study enrollment in years (range) | 4.5 (0.5–26.6) | 4.2 (0.6–26.6) | 0.28 | |||
| 0–2 years | 551 | 56 | 21 | 62 | 22 | 0.09 |
| 2–4 years | 70 | 26 | 76 | 27 | ||
| 4–8 years | 74 | 27 | 93 | 33 | ||
| >8 years | 71 | 26 | 49 | 18 | ||
| Median time from last treatment to study enrollment in years (range) | 3.6 (0.1–13.6) | 3.6 (0.4–18.7) | 0.87 | |||
| 0–2 years | 551 | 97 | 36 | 99 | 35 | 0.84 |
| 2–5 years | 83 | 31 | 92 | 33 | ||
| >5 years | 91 | 34 | 89 | 32 | ||
| Affiliation of follow-up provider | ||||||
| University-based | 551 | 193 | 71 | 190 | 68 | 0.16 |
| Community-based | 28 | 10 | 31 | 11 | ||
| Both | 50 | 18 | 54 | 19 | ||
| Missing | 0 | 0 | 5 | 2 | ||
| Treatment received | ||||||
| Chemotherapy only | 551 | 82 | 30 | 89 | 32 | 0.93 |
| Chemo + surgery + radiation | 125 | 46 | 126 | 45 | ||
| Stem cell transplantation | 64 | 24 | 65 | 23 | ||
| Prior treatment outside university | 551 | 116 | 43 | 126 | 45 | 0.60 |
Statistical Analysis
Participant characteristics were compared according to level of spirituality using a chi-square test for categorical data and the Wilcoxon test for continuous data (Table 1). Multivariate logistic regression models were fitted to evaluate separately the relationship between (1) spirituality with patient-rated worry as the outcome, (2) spirituality with follow-up health-care utilization as the outcome, and (3) patient-rated worry with follow-up health-care utilization as the outcome. In the above models, the following covariates were forced into each model: age, sex, cancer type, time from last cancer-related treatment to study start time, income, and type of medical insurance. These models were also fitted using outcomes ascertained at both 6 and 12 months. Interaction models between patient-rated worry and level of spirituality were also evaluated for an association with follow-up health-care utilization at 12 months to explore the role of spirituality in the relationship between patient-rated worry and health-care utilization. A P value of at least 0.05 was considered statistically significant.
Results
Study Participation
Of the 2,000 participants invited, 1,881 were deemed eligible (minus those who died or had wrong addresses). Baseline questionnaires were returned by 939 participants (baseline response rate of 50%). Seventeen wanted to participate only in the baseline survey. Of the 922 baseline participants, 691 returned the 6-month survey at the time of the analysis for this study, for a response rate of 76% when adjusted for deaths (182 no response, 18 deaths, 25 declined, 12 returned with wrong address). At 1 year, 691 surveys were mailed, with 588 surveys returned (58 no response, 17 deaths, 14 declined, 13 returned with wrong address, and one in hospice); a response rate of 87% was achieved after adjusting for deaths. Thirty-seven participants had missing information on spirituality, leaving a total of 551 included in this study. No differences in age, sex, and type of cancer were noted between patients included and excluded in the current analysis.
Characteristics of Study Participants
Demographic characteristics of the 551 study participants included in this study are shown in Table 1. We found that cancer survivors with low or high spirituality were more similar than different in all but two characteristics: highly spiritual survivors were more likely to be Protestant and male.
Prevalence of Spirituality and Patient Worry
Within our population, 271 (49%) survivors reported low spirituality and 280 (51%) reported high spirituality (Table 1). Also, at baseline, 277 (51%) survivors reported high levels of recurrence/progression-related worry, 190 survivors (35%) reported high levels of new malignancy–related worry, and 178 survivors (33%) reported high levels of treatment-related complication worry. As some participants may have reported one or more types of worry, this translates to 322 (59%) reporting any type of worry. Highly spiritual survivors reported significantly lower levels of high worry concerning recurrence/progression (6-month 27% vs. 38%, P < 0.01; 12-month 21% vs. 38%, P < 0.01), development of a different type of cancer (6-month 22% vs. 31%, P = 0.03; 12-month 15% vs. 26%, P < 0.01), and complications from treatment (6-month 17% vs. 30%, P < 0.01; 12-month 16% vs. 26%, P < 0.01). Highly spiritual survivors reported significantly lower levels for any type of worry at both 6 and 12 months (6 months 37% vs. 54%, P <0.01; 12 months 28% vs. 47%, P < 0.01) (Table 2).
| BASELINE | 6-MONTH | 12-MONTH | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| LOW SPIRITUALITY, N (%) | HIGH SPIRITUALITY, N (%) | P | LOW SPIRITUALITY, N (%) | HIGH SPIRITUALITY, N (%) | P | LOW SPIRITUALITY, N (%) | HIGH SPIRITUALITY, N (%) | P | ||
| Recurrence/progression-related worry | Low | 106 (40) | 160 (58) | <0.01 | 154 (62) | 184 (73) | <0.01 | 166 (62) | 218 (79) | <0.01 |
| High | 160 (60) | 117 (42) | 95 (38) | 69 (27) | 103 (38) | 59 (21) | ||||
| New primary–related worry | Low | 158 (59) | 200 (72) | <0.01 | 172 (69) | 202 (78) | 0.03 | 199 (74) | 235 (85) | <0.01 |
| High | 111 (41) | 79 (28) | 76 (31) | 58 (22) | 71 (26) | 42 (15) | ||||
| Complication-related worry | Low | 166 (61) | 203 (73) | <0.01 | 175 (70) | 214 (83) | <0.01 | 200 (74) | 232 (84) | <0.01 |
| High | 104 (39) | 74 (27) | 74 (30) | 45 (17) | 69 (26) | 44 (16) | ||||
| Any worry | Low | 85 (32) | 138 (50) | <0.01 | 120 (46) | 165 (63) | <0.01 | 142 (53) | 198 (72) | <0.01 |
| High | 182 (68) | 140 (50) | 139 (54) | 97 (37) | 128 (47) | 78 (28) | ||||
Relationship Between Spirituality and Patient Worry
At the 6- and 12-month time points, after adjusting for covariates, highly spiritual survivors were significantly less likely to have worries than survivors who reported lower spirituality regarding disease recurrence/progression at 6 months (odds ratio [OR] = 0.61, 95% confidence interval [CI] 0.42–0.89, P < 0.01) and at 12 months (OR = 0.43, 95% CI 0.29–0.63, P < 0.01), complications from treatment at 6 months (OR = 0.50, 95% CI 0.33–0.76, P < 0.01) and at 12 months (OR = 0.54, 95% CI 0.35–0.83, P < 0.01), and development of a different type of cancer at 6 months (OR = 0.65, 95% CI 0.44–0.97, P = 0.04) and at 12 months (OR = 0.50, 95% CI 0.33–0.77, P < 0.01) (Table 3A).
| A | N | 6-MONTH | 12-MONTH | |||||
|---|---|---|---|---|---|---|---|---|
| LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P | N | LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P | ||
| Outcome | ||||||||
| Recurrence/progression-related worry | 502 | 1.00 | 0.61 (0.42–0.89) | 0.01 | 546 | 1.00 | 0.43 (0.29–0.63) | <0.01 |
| New primary–related worry | 508 | 1.00 | 0.65 (0.44–0.97) | 0.04 | 547 | 1.00 | 0.50 (0.33–0.77) | <0.01 |
| Complication-related worry | 508 | 1.00 | 0.50 (0.33–0.76) | <0.01 | 545 | 1.00 | 0.54 (0.35–0.83) | <0.01 |
| B | N | LOW WORRY, OR (95% CI) | HIGH WORRY, OR (95% CI) | P | N | LOW WORRY, OR (95% CI) | HIGH WORRY,OR (95% CI) | P |
| Outcome | ||||||||
| Follow-up frequency | 485 | 1.00 | 1.81 (1.04–3.12) | 0.03 | 534 | 1.00 | 1.49 (1.00–2.22) | 0.05 |
| Phone call to follow-up clinic | 504 | 1.00 | 2.21 (1.48–3.31) | <0.01 | 543 | 1.00 | 1.74 (1.20–2.53) | 0.01 |
| Emergency room visit | 503 | 1.00 | 1.75 (0.90–3.43) | 0.10 | 549 | 1.00 | 0.88 (0.52–1.51) | 0.65 |
| C | N | LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P | N | LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P |
| Outcome | ||||||||
| Follow-up frequency | 487 | 1.00 | 0.63 (0.37–1.10) | 0.11 | 536 | 1.00 | 0.88 (0.60–1.30) | 0.52 |
| Phone call to follow-up clinic | 506 | 1.00 | 0.77 (0.53–1.12) | 0.17 | 545 | 1.00 | 0.70 (0.49–1.00) | 0.04 |
| Emergency room visit | 505 | 1.00 | 0.56 (0.30–1.05) | 0.07 | 551 | 1.00 | 0.84 (0.50–1.41) | 0.50 |
Models adjusted for age, sex, cancer type, income, type of insurance, and time from last treatment
Relationship Between Patient Worry and Follow-Up Health-Care Utilization
Survivors who were highly worried about disease recurrence/progression, development of another type of cancer, and/or complications from treatment were more likely to visit their providers for follow-up care when compared with survivors who were less worried at 6 months (OR = 1.81, 95% CI 1.04–3.12, P = 0.03) and at 12 months (OR = 1.49, 95% CI 1.00–2.22, P = 0.05). Similarly, survivors who were highly worried were also more likely to place phone calls to their follow-up providers for medical inquiries than survivors who were less worried at 6 months (OR = 2.21, 95% CI 1.48–3.31, P < 0.01) and at 12 months (OR = 1.74, 95% CI 1.20–2.53, P = 0.01). We did not observe differences in emergency room visits between survivors with low and those with high rates of worrying at both 6 and 12 months (Table 3B).
Relationship Between Spirituality and Health-Care Utilization
No significant differences were noted for the frequency of follow-up visits, changes in follow-up providers, and emergency room visits between the levels of spirituality at both 6 and 12 months. However, at 12 months, highly spiritual survivors were less likely to call their follow-up providers for medical inquiries compared to survivors with low spirituality scores (OR = 0.70, 95% CI 0.49–1.00, P = 0.04) (Table 3C).
Interaction Between Spirituality and Patient Worry With Health-Care Utilization
Interaction between patient-rated worry and level of spirituality as it relates to health-care utilization was not statistically significant (data not shown). This suggests that spirituality does not modify the effect of patient worry in producing change in follow-up health-care utilization.
Discussion
Our study examined the relationships between spirituality, patient-rated worry, and follow-up health-care utilization among cancer survivors and found that individuals who possess higher levels of spirituality tend to have less worry of disease recurrence/progression, development of treatment-related complications, and development of new cancers. These findings are consistent with previous research among patients with advanced or terminal cancers that consistently showed such correlations between spirituality and general measures of anxiety.[10], [15], [17], [19], [30] and [31] Additionally, our study showed that a higher degree of worry about common concerns of cancer survivors is associated with more follow-up visits and calls to health-care providers. However, our data also showed that spirituality by itself is for the most part not associated with follow-up health-care utilization.
It has been documented that psychosocial factors like anxiety and spirituality can influence behaviors.[32], [33], [34], [35] and [36] Our analysis showed that both discretionary and nondiscretionary indices of health-care utilization increased significantly among highly worried cancer survivors. However, these increases are independent of one's level of spirituality. These results suggest that cancer survivors with a high degree of worry about disease recurrence/progression, development of treatment-related complications, or development of a new cancer produce a change in care-seeking behavior and may concomitantly alter the health provider's need to see the patient. Our results also suggest that while spirituality has an impact on one's level of worry, being less spiritual does not necessarily alter a cancer survivor's care-seeking behavior.
Worried patients present a potential problem for clinicians in that they may need more attention during clinic visits,37 may result in requests for more ancillary/diagnostic tests including imaging modalities,[38] and [39] or may use more medications[40] and [41] or resort to other alternative therapies[42], [43], [44] and [45] available to reduce their worries. Given that cancer patients already receive many chemotherapeutic agents for their treatment, many of them are more inclined to undergo alternative therapies.[16], [43], [46], [47] and [48] Spirituality-based interventions shown to be effective at reducing anxiety and increasing QOL may therefore have a role among cancer survivors. And because spirituality and religiosity are closely linked,29 faith-based interventions may also benefit the patient.
Our study has several implications in the assessment of cancer survivors in multidisciplinary survivorship clinics. While much attention about assessing depression, anxiety, and QOL has been given to cancer survivors, our study shows that the evaluation of one's spirituality may have some merit as well. Participants with low spirituality and a high degree of worry may benefit from activities that enhance spirituality (e.g., yoga, meditation). Because of the increasing number of cancer survivors,[32] and [49] development of clinic-based spiritual interventions to address common worries of cancer survivors may be appropriate. In addition to the implications for clinical practice, our study has implications for future research. While the literature has shown a correlation between spirituality and religiosity,29 these two concepts are not the same.[1], [2], [50], [51] and [52] It would have been interesting to compare outcomes by level of spirituality and religiosity, but our data revealed a high degree of correlation between these two concepts. Over 90% of individuals who are spiritual are also religious.[28], [53] and [54] This may be the reason that some spirituality-based interventions have enhancement of religious activities as main approaches to improve spirituality.[28] and [53]
While our study has the strengths associated with a prospective study in a relatively large number of cancer survivors treated in a single medical center, it has several limitations. Our participation rate at baseline was only 50%, although our retention rates at 6 and 12 months were on average 80%. Another limitation of our study is that the baseline surveys were conducted at different time intervals from last treatment, although this limitation also allowed us to include all kinds of cancer survivors in terms of disease and time interval from last cancer treatment. Analysis confined to patients who received treatment within the last 5 years (n = 371) showed essentially the same results. We also compared the baseline spirituality scores of the study participants according to time from last treatment to study participation (0–2, 2–5, >5) and showed no statistically significant differences. Additionally, we adjusted for time from last treatment to study participation in the multivariate analyses. Combining all the participants into one analysis allowed for our exploratory analyses to have stronger statistical power. Another limitation of our study is the crude measurement of patient worry. However, in the absence of validated instruments to measure these worries, we felt the measures reflected subjective ratings of common worries by cancer survivors. Health-care utilization would have been ideally measured continuously to better quantify the medical services utilized. However, because we included a heterogeneous group of cancer patients, this measure would be highly variable and depend on the type of disease and treatment received by the patient. Thus, type of disease and time period from last treatment were adjusted for in the multivariate analyses.
In summary, cancer survivors who possess higher levels of spirituality tend to have a lesser degree of worry over disease recurrence/progression, development of treatment complications, and development of new cancers. A higher degree of worry about the common concerns of cancer survivors is associated with more follow-up visits and calls to health providers. However, our data showed that, for the most part, spirituality is not associated with follow-up health-care utilization.
Acknowledgments
The authors thank Linda Bauer, Garrett Frost, and Gregory McFadden for their help in coordinating the study and processing the data. This work was supported by the University of Nebraska Medical Center–Eppley Cancer Center (Support Grant P30 CA 036727) and the Medical Student Research Program. The funding source had no role in the design, collection, analysis, and interpretation of the data or in the writing of the article.
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Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
Background Spirituality may aid cancer survivors as they attempt to interpret the meaning of their experience.
Objective We examined the relationship between spirituality, patient-rated worry, and health-care utilization among 551 cancer survivors with different malignancies, who were evaluated prospectively.
Methods Baseline spirituality scores were categorized into low and high spirituality groups. Patient-rated worries regarding disease recurrence/progression, developing new cancer, and developing complications from treatment were collected at baseline and at 6 and 12 months. Follow-up health-care utilization was also examined at 6 and 12 months.
Results Among the survivors, 271 (49%) reported low spirituality and 280 (51%) reported high spirituality. Of the cohort, 59% had some kind of worry regarding disease recurrence/progression, development of new cancers, and treatment complications. Highly spiritual survivors were less likely to have high levels of worries at both 6 and 12 months. Highly worried survivors were significantly more likely to place phone calls to their follow-up providers and had more frequent follow-up visits at 6 and 12 months. No interactions between spirituality and level of worry were noted to affect follow-up health-care utilization.
Conclusion Given spirituality's effect on anxiety, spirituality-based intervention may have a role in addressing cancer survivors' worries but may not improve health-care utilization.
Article Outline
- Results
- Study Participation
- Characteristics of Study Participants
- Prevalence of Spirituality and Patient Worry
- Relationship Between Spirituality and Patient Worry
- Relationship Between Patient Worry and Follow-Up Health-Care Utilization
- Relationship Between Spirituality and Health-Care Utilization
- Interaction Between Spirituality and Patient Worry With Health-Care Utilization
Receiving a diagnosis of cancer is a life-changing event. Patients commonly seek understanding of not only the medical aspects of their disease but also how the diagnosis will affect their lives. Often, this quest to understand the meaning behind the unfortunate circumstance of disease is aided by spirituality. Spirituality motivates an individual to find meaning or purpose in his or her life experience.1 Most studies indicate that spirituality gives meaningful insight to an individual's existence and aids in the interpretation of events and relationships.[2], [3], [4], [5], [6], [7], [8] and [9]
Spiritual beliefs are widespread among cancer patients. Studies have shown that a better quality of life (QOL) is achieved in patients who practice spirituality or have those needs met by their health-care providers. They require less health care as well as experience less anxiety and a greater sense of well-being.[10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] and [21] One may conclude that spirituality helps patients understand the meaning of their disease and provides the catalyst for significant improvement in health-related outcomes.
Vast amounts of literature affirm spirituality's positive effects on health outcomes for advanced-stage/terminally ill patients. However, very little is known about how spirituality affects the common concerns of cancer survivors. It can be inferred that spirituality continues to aid cancer survivors as they attempt to interpret the meaning of their experience during follow-up care. After completing various cancer treatments, survivors may experience worries of cancer recurrence or progression, worries of developing a different cancer, and worries of developing complications from their initial treatment.22 We explored the relationship between spirituality, patient-rated cancer-related worry, and cancer survivors' follow-up health-care utilization (follow-up doctor visits, phone calls to follow-up providers regarding medical inquiries, and emergency room visits).
Participants and Methods
Subject Selection and Eligibility
Data for this study were obtained from CANCER CARE, an observational cohort study using a self-administered questionnaire designed to evaluate follow-up care among cancer survivors.23 Participants were seen at the University of Nebraska Medical Center (UNMC) and consented to participate in a data-collection protocol (ONCOBASE) since March 2006. ONCOBASE has a 90% consent rate. To be eligible for the study, participants were at least 19 years of age (age of majority in Nebraska) and completed their cancer treatment at UNMC. Participants varied in time since completion of last cancer treatment. From a list of 5,500 eligible subjects, 2,500 were screened. The list was sorted by date of consent, and the first 2,000 subjects received the study questionnaire. Survey forms were mailed in August 2008 (baseline) and follow-up surveys were mailed in February (month 6) and August 2009 (month 12). Participants were not paid for study participation but were told that a donation to a charitable institution was made on their behalf as an altruistic incentive.23 The study was approved by the Institutional Review Board at UNMC.
Variables Analyzed
We analyzed the participants' spirituality from baseline surveys using the Functional Assessment of Cancer Therapy–Spirituality Scale (FACT-SP).24 Total spirituality scores were computed for each participant using instrument standard calculations. The cohort was categorized into two groups, consisting of low or high spirituality based on the median calculated score (<47 vs. ≥47) for the entire population. Other variables included in the analyses are shown in Table 1. Patient-rated worry pertaining to (1) disease recurrence/progression, (2) development of a new malignancy, and (3) complications related to treatment were evaluated at baseline and at 6 and 12 months. Respondents were asked to rate their level of worry for each of the above three items using a five-point Likert scale (none at all, little of the time, some of the time, most of the time, and all of the time). Each worry item was categorized as low (none at all to a little of the time) vs. high (some of the time, most of the time, all of the time). Follow-up health-care utilization was assessed at 6 and 12 months and consisted of (1) follow-up clinic visits (low, defined as none or one follow-up visit per year, vs. high, more than one follow-up visit per year), (2) phone calls to follow-up providers for medical issues (no vs. yes), and (3) emergency room visits (no vs. yes). These indices of health-care utilization were selected on the basis of whether they are discretionary (patient-driven) or nondiscretionary (physician-driven).[25] and [26] For example, follow-up clinic visits are mainly nondiscretionary in the sense that the follow-up provider primarily determines the frequency at which they are conducted, while phone calls made to follow-up providers and emergency room visits are inherently discretionary. We also evaluated the relationships between spirituality and QOL (Short Form 12 [SF-12]),27 social support,28 and religiosity (with the survey question [data not shown] “Overall, how much would you say that religious beliefs have influenced your life in the past two months?”), to establish the external validity of our spirituality cut-off score since these constructs have been associated with spirituality.[10], [15], [17], [19], [29], [30] and [31] Our analyses showed a high correlation between our categorization of low or high spirituality with QOL, social support, and religiosity.
| EVALUABLE (N) | LOW SPIRITUALITY | HIGH SPIRITUALITY | P | |||
|---|---|---|---|---|---|---|
| FREQUENCY | PERCENT | FREQUENCY | PERCENT | |||
| n | 551 | 271 | 49 | 280 | 51 | |
| Median age (range) | 59 (19–85) | 59 (22–83) | 0.99 | |||
| ≤40 | 551 | 17 | 6 | 21 | 8 | 0.78 |
| 41–60 | 137 | 51 | 135 | 48 | ||
| >60 | 117 | 43 | 124 | 44 | ||
| Sex | ||||||
| Female | 551 | 112 | 41 | 89 | 32 | 0.02 |
| Male | 159 | 59 | 191 | 68 | ||
| Race/ethnicity | ||||||
| White | 551 | 256 | 94 | 272 | 97 | 0.21 |
| Hispanic | 6 | 2 | 2 | 1 | ||
| African American | 3 | 1 | 4 | 1 | ||
| Other | 6 | 2 | 2 | 1 | ||
| Marital status | ||||||
| Single/never married | 551 | 14 | 5 | 19 | 7 | 0.67 |
| Married | 219 | 81 | 219 | 78 | ||
| Divorced/widowed | 38 | 14 | 42 | 15 | ||
| Education | ||||||
| High school | 551 | 90 | 33 | 83 | 30 | 0.49 |
| College | 105 | 39 | 122 | 44 | ||
| Postgraduate | 76 | 28 | 75 | 27 | ||
| Religion | ||||||
| Protestant | 551 | 121 | 45 | 161 | 58 | <0.01 |
| Catholic | 101 | 37 | 80 | 29 | ||
| Other | 36 | 13 | 35 | 13 | ||
| None/atheist | 13 | 5 | 4 | 1 | ||
| Income (US$) | ||||||
| <25,000 | 551 | 37 | 14 | 37 | 13 | 0.71 |
| 25,000–49,999 | 64 | 24 | 61 | 22 | ||
| 50,000–74,999 | 59 | 22 | 54 | 19 | ||
| 75,000–100,000 | 35 | 13 | 44 | 16 | ||
| >100,000 | 57 | 21 | 56 | 20 | ||
| Missing | 19 | 7 | 28 | 10 | ||
| Place of residence | ||||||
| Urban | 551 | 194 | 72 | 201 | 72 | 0.96 |
| Rural | 77 | 28 | 79 | 28 | ||
| Distance (miles) | ||||||
| ≤15 | 551 | 108 | 40 | 98 | 35 | 0.32 |
| 15–100 | 83 | 31 | 94 | 34 | ||
| 100–250 | 44 | 16 | 58 | 21 | ||
| >250 | 36 | 13 | 30 | 11 | ||
| Employment status | ||||||
| Full time | 551 | 160 | 59 | 163 | 58 | 0.93 |
| Part time | 22 | 8 | 27 | 10 | ||
| Homemaker | 25 | 9 | 26 | 9 | ||
| Student | 3 | 1 | 4 | 1 | ||
| Retired | 48 | 18 | 51 | 18 | ||
| Other | 13 | 5 | 9 | 3 | ||
| Patient is the primary income provider | 551 | 137 | 51 | 132 | 47 | 0.42 |
| Insurance | ||||||
| Employer-based | 551 | 149 | 55 | 153 | 55 | 0.95 |
| Individual-based | 47 | 17 | 48 | 17 | ||
| Medicare/Medicaid | 56 | 21 | 59 | 21 | ||
| Other | 17 | 6 | 16 | 6 | ||
| None | 2 | 1 | 4 | 1 | ||
| Prescription insurance | 551 | 239 | 88 | 242 | 86 | 0.53 |
| Type of malignancy | ||||||
| Leukemia, lymphoma, multiple myeloma | 551 | 136 | 50 | 147 | 53 | 0.86 |
| Breast, colon, prostate | 101 | 37 | 100 | 36 | ||
| Lung, pancreatic | 34 | 13 | 33 | 12 | ||
| Median time from diagnosis to study enrollment in years (range) | 4.5 (0.5–26.6) | 4.2 (0.6–26.6) | 0.28 | |||
| 0–2 years | 551 | 56 | 21 | 62 | 22 | 0.09 |
| 2–4 years | 70 | 26 | 76 | 27 | ||
| 4–8 years | 74 | 27 | 93 | 33 | ||
| >8 years | 71 | 26 | 49 | 18 | ||
| Median time from last treatment to study enrollment in years (range) | 3.6 (0.1–13.6) | 3.6 (0.4–18.7) | 0.87 | |||
| 0–2 years | 551 | 97 | 36 | 99 | 35 | 0.84 |
| 2–5 years | 83 | 31 | 92 | 33 | ||
| >5 years | 91 | 34 | 89 | 32 | ||
| Affiliation of follow-up provider | ||||||
| University-based | 551 | 193 | 71 | 190 | 68 | 0.16 |
| Community-based | 28 | 10 | 31 | 11 | ||
| Both | 50 | 18 | 54 | 19 | ||
| Missing | 0 | 0 | 5 | 2 | ||
| Treatment received | ||||||
| Chemotherapy only | 551 | 82 | 30 | 89 | 32 | 0.93 |
| Chemo + surgery + radiation | 125 | 46 | 126 | 45 | ||
| Stem cell transplantation | 64 | 24 | 65 | 23 | ||
| Prior treatment outside university | 551 | 116 | 43 | 126 | 45 | 0.60 |
Statistical Analysis
Participant characteristics were compared according to level of spirituality using a chi-square test for categorical data and the Wilcoxon test for continuous data (Table 1). Multivariate logistic regression models were fitted to evaluate separately the relationship between (1) spirituality with patient-rated worry as the outcome, (2) spirituality with follow-up health-care utilization as the outcome, and (3) patient-rated worry with follow-up health-care utilization as the outcome. In the above models, the following covariates were forced into each model: age, sex, cancer type, time from last cancer-related treatment to study start time, income, and type of medical insurance. These models were also fitted using outcomes ascertained at both 6 and 12 months. Interaction models between patient-rated worry and level of spirituality were also evaluated for an association with follow-up health-care utilization at 12 months to explore the role of spirituality in the relationship between patient-rated worry and health-care utilization. A P value of at least 0.05 was considered statistically significant.
Results
Study Participation
Of the 2,000 participants invited, 1,881 were deemed eligible (minus those who died or had wrong addresses). Baseline questionnaires were returned by 939 participants (baseline response rate of 50%). Seventeen wanted to participate only in the baseline survey. Of the 922 baseline participants, 691 returned the 6-month survey at the time of the analysis for this study, for a response rate of 76% when adjusted for deaths (182 no response, 18 deaths, 25 declined, 12 returned with wrong address). At 1 year, 691 surveys were mailed, with 588 surveys returned (58 no response, 17 deaths, 14 declined, 13 returned with wrong address, and one in hospice); a response rate of 87% was achieved after adjusting for deaths. Thirty-seven participants had missing information on spirituality, leaving a total of 551 included in this study. No differences in age, sex, and type of cancer were noted between patients included and excluded in the current analysis.
Characteristics of Study Participants
Demographic characteristics of the 551 study participants included in this study are shown in Table 1. We found that cancer survivors with low or high spirituality were more similar than different in all but two characteristics: highly spiritual survivors were more likely to be Protestant and male.
Prevalence of Spirituality and Patient Worry
Within our population, 271 (49%) survivors reported low spirituality and 280 (51%) reported high spirituality (Table 1). Also, at baseline, 277 (51%) survivors reported high levels of recurrence/progression-related worry, 190 survivors (35%) reported high levels of new malignancy–related worry, and 178 survivors (33%) reported high levels of treatment-related complication worry. As some participants may have reported one or more types of worry, this translates to 322 (59%) reporting any type of worry. Highly spiritual survivors reported significantly lower levels of high worry concerning recurrence/progression (6-month 27% vs. 38%, P < 0.01; 12-month 21% vs. 38%, P < 0.01), development of a different type of cancer (6-month 22% vs. 31%, P = 0.03; 12-month 15% vs. 26%, P < 0.01), and complications from treatment (6-month 17% vs. 30%, P < 0.01; 12-month 16% vs. 26%, P < 0.01). Highly spiritual survivors reported significantly lower levels for any type of worry at both 6 and 12 months (6 months 37% vs. 54%, P <0.01; 12 months 28% vs. 47%, P < 0.01) (Table 2).
| BASELINE | 6-MONTH | 12-MONTH | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| LOW SPIRITUALITY, N (%) | HIGH SPIRITUALITY, N (%) | P | LOW SPIRITUALITY, N (%) | HIGH SPIRITUALITY, N (%) | P | LOW SPIRITUALITY, N (%) | HIGH SPIRITUALITY, N (%) | P | ||
| Recurrence/progression-related worry | Low | 106 (40) | 160 (58) | <0.01 | 154 (62) | 184 (73) | <0.01 | 166 (62) | 218 (79) | <0.01 |
| High | 160 (60) | 117 (42) | 95 (38) | 69 (27) | 103 (38) | 59 (21) | ||||
| New primary–related worry | Low | 158 (59) | 200 (72) | <0.01 | 172 (69) | 202 (78) | 0.03 | 199 (74) | 235 (85) | <0.01 |
| High | 111 (41) | 79 (28) | 76 (31) | 58 (22) | 71 (26) | 42 (15) | ||||
| Complication-related worry | Low | 166 (61) | 203 (73) | <0.01 | 175 (70) | 214 (83) | <0.01 | 200 (74) | 232 (84) | <0.01 |
| High | 104 (39) | 74 (27) | 74 (30) | 45 (17) | 69 (26) | 44 (16) | ||||
| Any worry | Low | 85 (32) | 138 (50) | <0.01 | 120 (46) | 165 (63) | <0.01 | 142 (53) | 198 (72) | <0.01 |
| High | 182 (68) | 140 (50) | 139 (54) | 97 (37) | 128 (47) | 78 (28) | ||||
Relationship Between Spirituality and Patient Worry
At the 6- and 12-month time points, after adjusting for covariates, highly spiritual survivors were significantly less likely to have worries than survivors who reported lower spirituality regarding disease recurrence/progression at 6 months (odds ratio [OR] = 0.61, 95% confidence interval [CI] 0.42–0.89, P < 0.01) and at 12 months (OR = 0.43, 95% CI 0.29–0.63, P < 0.01), complications from treatment at 6 months (OR = 0.50, 95% CI 0.33–0.76, P < 0.01) and at 12 months (OR = 0.54, 95% CI 0.35–0.83, P < 0.01), and development of a different type of cancer at 6 months (OR = 0.65, 95% CI 0.44–0.97, P = 0.04) and at 12 months (OR = 0.50, 95% CI 0.33–0.77, P < 0.01) (Table 3A).
| A | N | 6-MONTH | 12-MONTH | |||||
|---|---|---|---|---|---|---|---|---|
| LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P | N | LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P | ||
| Outcome | ||||||||
| Recurrence/progression-related worry | 502 | 1.00 | 0.61 (0.42–0.89) | 0.01 | 546 | 1.00 | 0.43 (0.29–0.63) | <0.01 |
| New primary–related worry | 508 | 1.00 | 0.65 (0.44–0.97) | 0.04 | 547 | 1.00 | 0.50 (0.33–0.77) | <0.01 |
| Complication-related worry | 508 | 1.00 | 0.50 (0.33–0.76) | <0.01 | 545 | 1.00 | 0.54 (0.35–0.83) | <0.01 |
| B | N | LOW WORRY, OR (95% CI) | HIGH WORRY, OR (95% CI) | P | N | LOW WORRY, OR (95% CI) | HIGH WORRY,OR (95% CI) | P |
| Outcome | ||||||||
| Follow-up frequency | 485 | 1.00 | 1.81 (1.04–3.12) | 0.03 | 534 | 1.00 | 1.49 (1.00–2.22) | 0.05 |
| Phone call to follow-up clinic | 504 | 1.00 | 2.21 (1.48–3.31) | <0.01 | 543 | 1.00 | 1.74 (1.20–2.53) | 0.01 |
| Emergency room visit | 503 | 1.00 | 1.75 (0.90–3.43) | 0.10 | 549 | 1.00 | 0.88 (0.52–1.51) | 0.65 |
| C | N | LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P | N | LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P |
| Outcome | ||||||||
| Follow-up frequency | 487 | 1.00 | 0.63 (0.37–1.10) | 0.11 | 536 | 1.00 | 0.88 (0.60–1.30) | 0.52 |
| Phone call to follow-up clinic | 506 | 1.00 | 0.77 (0.53–1.12) | 0.17 | 545 | 1.00 | 0.70 (0.49–1.00) | 0.04 |
| Emergency room visit | 505 | 1.00 | 0.56 (0.30–1.05) | 0.07 | 551 | 1.00 | 0.84 (0.50–1.41) | 0.50 |
Models adjusted for age, sex, cancer type, income, type of insurance, and time from last treatment
Relationship Between Patient Worry and Follow-Up Health-Care Utilization
Survivors who were highly worried about disease recurrence/progression, development of another type of cancer, and/or complications from treatment were more likely to visit their providers for follow-up care when compared with survivors who were less worried at 6 months (OR = 1.81, 95% CI 1.04–3.12, P = 0.03) and at 12 months (OR = 1.49, 95% CI 1.00–2.22, P = 0.05). Similarly, survivors who were highly worried were also more likely to place phone calls to their follow-up providers for medical inquiries than survivors who were less worried at 6 months (OR = 2.21, 95% CI 1.48–3.31, P < 0.01) and at 12 months (OR = 1.74, 95% CI 1.20–2.53, P = 0.01). We did not observe differences in emergency room visits between survivors with low and those with high rates of worrying at both 6 and 12 months (Table 3B).
Relationship Between Spirituality and Health-Care Utilization
No significant differences were noted for the frequency of follow-up visits, changes in follow-up providers, and emergency room visits between the levels of spirituality at both 6 and 12 months. However, at 12 months, highly spiritual survivors were less likely to call their follow-up providers for medical inquiries compared to survivors with low spirituality scores (OR = 0.70, 95% CI 0.49–1.00, P = 0.04) (Table 3C).
Interaction Between Spirituality and Patient Worry With Health-Care Utilization
Interaction between patient-rated worry and level of spirituality as it relates to health-care utilization was not statistically significant (data not shown). This suggests that spirituality does not modify the effect of patient worry in producing change in follow-up health-care utilization.
Discussion
Our study examined the relationships between spirituality, patient-rated worry, and follow-up health-care utilization among cancer survivors and found that individuals who possess higher levels of spirituality tend to have less worry of disease recurrence/progression, development of treatment-related complications, and development of new cancers. These findings are consistent with previous research among patients with advanced or terminal cancers that consistently showed such correlations between spirituality and general measures of anxiety.[10], [15], [17], [19], [30] and [31] Additionally, our study showed that a higher degree of worry about common concerns of cancer survivors is associated with more follow-up visits and calls to health-care providers. However, our data also showed that spirituality by itself is for the most part not associated with follow-up health-care utilization.
It has been documented that psychosocial factors like anxiety and spirituality can influence behaviors.[32], [33], [34], [35] and [36] Our analysis showed that both discretionary and nondiscretionary indices of health-care utilization increased significantly among highly worried cancer survivors. However, these increases are independent of one's level of spirituality. These results suggest that cancer survivors with a high degree of worry about disease recurrence/progression, development of treatment-related complications, or development of a new cancer produce a change in care-seeking behavior and may concomitantly alter the health provider's need to see the patient. Our results also suggest that while spirituality has an impact on one's level of worry, being less spiritual does not necessarily alter a cancer survivor's care-seeking behavior.
Worried patients present a potential problem for clinicians in that they may need more attention during clinic visits,37 may result in requests for more ancillary/diagnostic tests including imaging modalities,[38] and [39] or may use more medications[40] and [41] or resort to other alternative therapies[42], [43], [44] and [45] available to reduce their worries. Given that cancer patients already receive many chemotherapeutic agents for their treatment, many of them are more inclined to undergo alternative therapies.[16], [43], [46], [47] and [48] Spirituality-based interventions shown to be effective at reducing anxiety and increasing QOL may therefore have a role among cancer survivors. And because spirituality and religiosity are closely linked,29 faith-based interventions may also benefit the patient.
Our study has several implications in the assessment of cancer survivors in multidisciplinary survivorship clinics. While much attention about assessing depression, anxiety, and QOL has been given to cancer survivors, our study shows that the evaluation of one's spirituality may have some merit as well. Participants with low spirituality and a high degree of worry may benefit from activities that enhance spirituality (e.g., yoga, meditation). Because of the increasing number of cancer survivors,[32] and [49] development of clinic-based spiritual interventions to address common worries of cancer survivors may be appropriate. In addition to the implications for clinical practice, our study has implications for future research. While the literature has shown a correlation between spirituality and religiosity,29 these two concepts are not the same.[1], [2], [50], [51] and [52] It would have been interesting to compare outcomes by level of spirituality and religiosity, but our data revealed a high degree of correlation between these two concepts. Over 90% of individuals who are spiritual are also religious.[28], [53] and [54] This may be the reason that some spirituality-based interventions have enhancement of religious activities as main approaches to improve spirituality.[28] and [53]
While our study has the strengths associated with a prospective study in a relatively large number of cancer survivors treated in a single medical center, it has several limitations. Our participation rate at baseline was only 50%, although our retention rates at 6 and 12 months were on average 80%. Another limitation of our study is that the baseline surveys were conducted at different time intervals from last treatment, although this limitation also allowed us to include all kinds of cancer survivors in terms of disease and time interval from last cancer treatment. Analysis confined to patients who received treatment within the last 5 years (n = 371) showed essentially the same results. We also compared the baseline spirituality scores of the study participants according to time from last treatment to study participation (0–2, 2–5, >5) and showed no statistically significant differences. Additionally, we adjusted for time from last treatment to study participation in the multivariate analyses. Combining all the participants into one analysis allowed for our exploratory analyses to have stronger statistical power. Another limitation of our study is the crude measurement of patient worry. However, in the absence of validated instruments to measure these worries, we felt the measures reflected subjective ratings of common worries by cancer survivors. Health-care utilization would have been ideally measured continuously to better quantify the medical services utilized. However, because we included a heterogeneous group of cancer patients, this measure would be highly variable and depend on the type of disease and treatment received by the patient. Thus, type of disease and time period from last treatment were adjusted for in the multivariate analyses.
In summary, cancer survivors who possess higher levels of spirituality tend to have a lesser degree of worry over disease recurrence/progression, development of treatment complications, and development of new cancers. A higher degree of worry about the common concerns of cancer survivors is associated with more follow-up visits and calls to health providers. However, our data showed that, for the most part, spirituality is not associated with follow-up health-care utilization.
Acknowledgments
The authors thank Linda Bauer, Garrett Frost, and Gregory McFadden for their help in coordinating the study and processing the data. This work was supported by the University of Nebraska Medical Center–Eppley Cancer Center (Support Grant P30 CA 036727) and the Medical Student Research Program. The funding source had no role in the design, collection, analysis, and interpretation of the data or in the writing of the article.
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Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
Background Spirituality may aid cancer survivors as they attempt to interpret the meaning of their experience.
Objective We examined the relationship between spirituality, patient-rated worry, and health-care utilization among 551 cancer survivors with different malignancies, who were evaluated prospectively.
Methods Baseline spirituality scores were categorized into low and high spirituality groups. Patient-rated worries regarding disease recurrence/progression, developing new cancer, and developing complications from treatment were collected at baseline and at 6 and 12 months. Follow-up health-care utilization was also examined at 6 and 12 months.
Results Among the survivors, 271 (49%) reported low spirituality and 280 (51%) reported high spirituality. Of the cohort, 59% had some kind of worry regarding disease recurrence/progression, development of new cancers, and treatment complications. Highly spiritual survivors were less likely to have high levels of worries at both 6 and 12 months. Highly worried survivors were significantly more likely to place phone calls to their follow-up providers and had more frequent follow-up visits at 6 and 12 months. No interactions between spirituality and level of worry were noted to affect follow-up health-care utilization.
Conclusion Given spirituality's effect on anxiety, spirituality-based intervention may have a role in addressing cancer survivors' worries but may not improve health-care utilization.
Article Outline
- Results
- Study Participation
- Characteristics of Study Participants
- Prevalence of Spirituality and Patient Worry
- Relationship Between Spirituality and Patient Worry
- Relationship Between Patient Worry and Follow-Up Health-Care Utilization
- Relationship Between Spirituality and Health-Care Utilization
- Interaction Between Spirituality and Patient Worry With Health-Care Utilization
Receiving a diagnosis of cancer is a life-changing event. Patients commonly seek understanding of not only the medical aspects of their disease but also how the diagnosis will affect their lives. Often, this quest to understand the meaning behind the unfortunate circumstance of disease is aided by spirituality. Spirituality motivates an individual to find meaning or purpose in his or her life experience.1 Most studies indicate that spirituality gives meaningful insight to an individual's existence and aids in the interpretation of events and relationships.[2], [3], [4], [5], [6], [7], [8] and [9]
Spiritual beliefs are widespread among cancer patients. Studies have shown that a better quality of life (QOL) is achieved in patients who practice spirituality or have those needs met by their health-care providers. They require less health care as well as experience less anxiety and a greater sense of well-being.[10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] and [21] One may conclude that spirituality helps patients understand the meaning of their disease and provides the catalyst for significant improvement in health-related outcomes.
Vast amounts of literature affirm spirituality's positive effects on health outcomes for advanced-stage/terminally ill patients. However, very little is known about how spirituality affects the common concerns of cancer survivors. It can be inferred that spirituality continues to aid cancer survivors as they attempt to interpret the meaning of their experience during follow-up care. After completing various cancer treatments, survivors may experience worries of cancer recurrence or progression, worries of developing a different cancer, and worries of developing complications from their initial treatment.22 We explored the relationship between spirituality, patient-rated cancer-related worry, and cancer survivors' follow-up health-care utilization (follow-up doctor visits, phone calls to follow-up providers regarding medical inquiries, and emergency room visits).
Participants and Methods
Subject Selection and Eligibility
Data for this study were obtained from CANCER CARE, an observational cohort study using a self-administered questionnaire designed to evaluate follow-up care among cancer survivors.23 Participants were seen at the University of Nebraska Medical Center (UNMC) and consented to participate in a data-collection protocol (ONCOBASE) since March 2006. ONCOBASE has a 90% consent rate. To be eligible for the study, participants were at least 19 years of age (age of majority in Nebraska) and completed their cancer treatment at UNMC. Participants varied in time since completion of last cancer treatment. From a list of 5,500 eligible subjects, 2,500 were screened. The list was sorted by date of consent, and the first 2,000 subjects received the study questionnaire. Survey forms were mailed in August 2008 (baseline) and follow-up surveys were mailed in February (month 6) and August 2009 (month 12). Participants were not paid for study participation but were told that a donation to a charitable institution was made on their behalf as an altruistic incentive.23 The study was approved by the Institutional Review Board at UNMC.
Variables Analyzed
We analyzed the participants' spirituality from baseline surveys using the Functional Assessment of Cancer Therapy–Spirituality Scale (FACT-SP).24 Total spirituality scores were computed for each participant using instrument standard calculations. The cohort was categorized into two groups, consisting of low or high spirituality based on the median calculated score (<47 vs. ≥47) for the entire population. Other variables included in the analyses are shown in Table 1. Patient-rated worry pertaining to (1) disease recurrence/progression, (2) development of a new malignancy, and (3) complications related to treatment were evaluated at baseline and at 6 and 12 months. Respondents were asked to rate their level of worry for each of the above three items using a five-point Likert scale (none at all, little of the time, some of the time, most of the time, and all of the time). Each worry item was categorized as low (none at all to a little of the time) vs. high (some of the time, most of the time, all of the time). Follow-up health-care utilization was assessed at 6 and 12 months and consisted of (1) follow-up clinic visits (low, defined as none or one follow-up visit per year, vs. high, more than one follow-up visit per year), (2) phone calls to follow-up providers for medical issues (no vs. yes), and (3) emergency room visits (no vs. yes). These indices of health-care utilization were selected on the basis of whether they are discretionary (patient-driven) or nondiscretionary (physician-driven).[25] and [26] For example, follow-up clinic visits are mainly nondiscretionary in the sense that the follow-up provider primarily determines the frequency at which they are conducted, while phone calls made to follow-up providers and emergency room visits are inherently discretionary. We also evaluated the relationships between spirituality and QOL (Short Form 12 [SF-12]),27 social support,28 and religiosity (with the survey question [data not shown] “Overall, how much would you say that religious beliefs have influenced your life in the past two months?”), to establish the external validity of our spirituality cut-off score since these constructs have been associated with spirituality.[10], [15], [17], [19], [29], [30] and [31] Our analyses showed a high correlation between our categorization of low or high spirituality with QOL, social support, and religiosity.
| EVALUABLE (N) | LOW SPIRITUALITY | HIGH SPIRITUALITY | P | |||
|---|---|---|---|---|---|---|
| FREQUENCY | PERCENT | FREQUENCY | PERCENT | |||
| n | 551 | 271 | 49 | 280 | 51 | |
| Median age (range) | 59 (19–85) | 59 (22–83) | 0.99 | |||
| ≤40 | 551 | 17 | 6 | 21 | 8 | 0.78 |
| 41–60 | 137 | 51 | 135 | 48 | ||
| >60 | 117 | 43 | 124 | 44 | ||
| Sex | ||||||
| Female | 551 | 112 | 41 | 89 | 32 | 0.02 |
| Male | 159 | 59 | 191 | 68 | ||
| Race/ethnicity | ||||||
| White | 551 | 256 | 94 | 272 | 97 | 0.21 |
| Hispanic | 6 | 2 | 2 | 1 | ||
| African American | 3 | 1 | 4 | 1 | ||
| Other | 6 | 2 | 2 | 1 | ||
| Marital status | ||||||
| Single/never married | 551 | 14 | 5 | 19 | 7 | 0.67 |
| Married | 219 | 81 | 219 | 78 | ||
| Divorced/widowed | 38 | 14 | 42 | 15 | ||
| Education | ||||||
| High school | 551 | 90 | 33 | 83 | 30 | 0.49 |
| College | 105 | 39 | 122 | 44 | ||
| Postgraduate | 76 | 28 | 75 | 27 | ||
| Religion | ||||||
| Protestant | 551 | 121 | 45 | 161 | 58 | <0.01 |
| Catholic | 101 | 37 | 80 | 29 | ||
| Other | 36 | 13 | 35 | 13 | ||
| None/atheist | 13 | 5 | 4 | 1 | ||
| Income (US$) | ||||||
| <25,000 | 551 | 37 | 14 | 37 | 13 | 0.71 |
| 25,000–49,999 | 64 | 24 | 61 | 22 | ||
| 50,000–74,999 | 59 | 22 | 54 | 19 | ||
| 75,000–100,000 | 35 | 13 | 44 | 16 | ||
| >100,000 | 57 | 21 | 56 | 20 | ||
| Missing | 19 | 7 | 28 | 10 | ||
| Place of residence | ||||||
| Urban | 551 | 194 | 72 | 201 | 72 | 0.96 |
| Rural | 77 | 28 | 79 | 28 | ||
| Distance (miles) | ||||||
| ≤15 | 551 | 108 | 40 | 98 | 35 | 0.32 |
| 15–100 | 83 | 31 | 94 | 34 | ||
| 100–250 | 44 | 16 | 58 | 21 | ||
| >250 | 36 | 13 | 30 | 11 | ||
| Employment status | ||||||
| Full time | 551 | 160 | 59 | 163 | 58 | 0.93 |
| Part time | 22 | 8 | 27 | 10 | ||
| Homemaker | 25 | 9 | 26 | 9 | ||
| Student | 3 | 1 | 4 | 1 | ||
| Retired | 48 | 18 | 51 | 18 | ||
| Other | 13 | 5 | 9 | 3 | ||
| Patient is the primary income provider | 551 | 137 | 51 | 132 | 47 | 0.42 |
| Insurance | ||||||
| Employer-based | 551 | 149 | 55 | 153 | 55 | 0.95 |
| Individual-based | 47 | 17 | 48 | 17 | ||
| Medicare/Medicaid | 56 | 21 | 59 | 21 | ||
| Other | 17 | 6 | 16 | 6 | ||
| None | 2 | 1 | 4 | 1 | ||
| Prescription insurance | 551 | 239 | 88 | 242 | 86 | 0.53 |
| Type of malignancy | ||||||
| Leukemia, lymphoma, multiple myeloma | 551 | 136 | 50 | 147 | 53 | 0.86 |
| Breast, colon, prostate | 101 | 37 | 100 | 36 | ||
| Lung, pancreatic | 34 | 13 | 33 | 12 | ||
| Median time from diagnosis to study enrollment in years (range) | 4.5 (0.5–26.6) | 4.2 (0.6–26.6) | 0.28 | |||
| 0–2 years | 551 | 56 | 21 | 62 | 22 | 0.09 |
| 2–4 years | 70 | 26 | 76 | 27 | ||
| 4–8 years | 74 | 27 | 93 | 33 | ||
| >8 years | 71 | 26 | 49 | 18 | ||
| Median time from last treatment to study enrollment in years (range) | 3.6 (0.1–13.6) | 3.6 (0.4–18.7) | 0.87 | |||
| 0–2 years | 551 | 97 | 36 | 99 | 35 | 0.84 |
| 2–5 years | 83 | 31 | 92 | 33 | ||
| >5 years | 91 | 34 | 89 | 32 | ||
| Affiliation of follow-up provider | ||||||
| University-based | 551 | 193 | 71 | 190 | 68 | 0.16 |
| Community-based | 28 | 10 | 31 | 11 | ||
| Both | 50 | 18 | 54 | 19 | ||
| Missing | 0 | 0 | 5 | 2 | ||
| Treatment received | ||||||
| Chemotherapy only | 551 | 82 | 30 | 89 | 32 | 0.93 |
| Chemo + surgery + radiation | 125 | 46 | 126 | 45 | ||
| Stem cell transplantation | 64 | 24 | 65 | 23 | ||
| Prior treatment outside university | 551 | 116 | 43 | 126 | 45 | 0.60 |
Statistical Analysis
Participant characteristics were compared according to level of spirituality using a chi-square test for categorical data and the Wilcoxon test for continuous data (Table 1). Multivariate logistic regression models were fitted to evaluate separately the relationship between (1) spirituality with patient-rated worry as the outcome, (2) spirituality with follow-up health-care utilization as the outcome, and (3) patient-rated worry with follow-up health-care utilization as the outcome. In the above models, the following covariates were forced into each model: age, sex, cancer type, time from last cancer-related treatment to study start time, income, and type of medical insurance. These models were also fitted using outcomes ascertained at both 6 and 12 months. Interaction models between patient-rated worry and level of spirituality were also evaluated for an association with follow-up health-care utilization at 12 months to explore the role of spirituality in the relationship between patient-rated worry and health-care utilization. A P value of at least 0.05 was considered statistically significant.
Results
Study Participation
Of the 2,000 participants invited, 1,881 were deemed eligible (minus those who died or had wrong addresses). Baseline questionnaires were returned by 939 participants (baseline response rate of 50%). Seventeen wanted to participate only in the baseline survey. Of the 922 baseline participants, 691 returned the 6-month survey at the time of the analysis for this study, for a response rate of 76% when adjusted for deaths (182 no response, 18 deaths, 25 declined, 12 returned with wrong address). At 1 year, 691 surveys were mailed, with 588 surveys returned (58 no response, 17 deaths, 14 declined, 13 returned with wrong address, and one in hospice); a response rate of 87% was achieved after adjusting for deaths. Thirty-seven participants had missing information on spirituality, leaving a total of 551 included in this study. No differences in age, sex, and type of cancer were noted between patients included and excluded in the current analysis.
Characteristics of Study Participants
Demographic characteristics of the 551 study participants included in this study are shown in Table 1. We found that cancer survivors with low or high spirituality were more similar than different in all but two characteristics: highly spiritual survivors were more likely to be Protestant and male.
Prevalence of Spirituality and Patient Worry
Within our population, 271 (49%) survivors reported low spirituality and 280 (51%) reported high spirituality (Table 1). Also, at baseline, 277 (51%) survivors reported high levels of recurrence/progression-related worry, 190 survivors (35%) reported high levels of new malignancy–related worry, and 178 survivors (33%) reported high levels of treatment-related complication worry. As some participants may have reported one or more types of worry, this translates to 322 (59%) reporting any type of worry. Highly spiritual survivors reported significantly lower levels of high worry concerning recurrence/progression (6-month 27% vs. 38%, P < 0.01; 12-month 21% vs. 38%, P < 0.01), development of a different type of cancer (6-month 22% vs. 31%, P = 0.03; 12-month 15% vs. 26%, P < 0.01), and complications from treatment (6-month 17% vs. 30%, P < 0.01; 12-month 16% vs. 26%, P < 0.01). Highly spiritual survivors reported significantly lower levels for any type of worry at both 6 and 12 months (6 months 37% vs. 54%, P <0.01; 12 months 28% vs. 47%, P < 0.01) (Table 2).
| BASELINE | 6-MONTH | 12-MONTH | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| LOW SPIRITUALITY, N (%) | HIGH SPIRITUALITY, N (%) | P | LOW SPIRITUALITY, N (%) | HIGH SPIRITUALITY, N (%) | P | LOW SPIRITUALITY, N (%) | HIGH SPIRITUALITY, N (%) | P | ||
| Recurrence/progression-related worry | Low | 106 (40) | 160 (58) | <0.01 | 154 (62) | 184 (73) | <0.01 | 166 (62) | 218 (79) | <0.01 |
| High | 160 (60) | 117 (42) | 95 (38) | 69 (27) | 103 (38) | 59 (21) | ||||
| New primary–related worry | Low | 158 (59) | 200 (72) | <0.01 | 172 (69) | 202 (78) | 0.03 | 199 (74) | 235 (85) | <0.01 |
| High | 111 (41) | 79 (28) | 76 (31) | 58 (22) | 71 (26) | 42 (15) | ||||
| Complication-related worry | Low | 166 (61) | 203 (73) | <0.01 | 175 (70) | 214 (83) | <0.01 | 200 (74) | 232 (84) | <0.01 |
| High | 104 (39) | 74 (27) | 74 (30) | 45 (17) | 69 (26) | 44 (16) | ||||
| Any worry | Low | 85 (32) | 138 (50) | <0.01 | 120 (46) | 165 (63) | <0.01 | 142 (53) | 198 (72) | <0.01 |
| High | 182 (68) | 140 (50) | 139 (54) | 97 (37) | 128 (47) | 78 (28) | ||||
Relationship Between Spirituality and Patient Worry
At the 6- and 12-month time points, after adjusting for covariates, highly spiritual survivors were significantly less likely to have worries than survivors who reported lower spirituality regarding disease recurrence/progression at 6 months (odds ratio [OR] = 0.61, 95% confidence interval [CI] 0.42–0.89, P < 0.01) and at 12 months (OR = 0.43, 95% CI 0.29–0.63, P < 0.01), complications from treatment at 6 months (OR = 0.50, 95% CI 0.33–0.76, P < 0.01) and at 12 months (OR = 0.54, 95% CI 0.35–0.83, P < 0.01), and development of a different type of cancer at 6 months (OR = 0.65, 95% CI 0.44–0.97, P = 0.04) and at 12 months (OR = 0.50, 95% CI 0.33–0.77, P < 0.01) (Table 3A).
| A | N | 6-MONTH | 12-MONTH | |||||
|---|---|---|---|---|---|---|---|---|
| LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P | N | LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P | ||
| Outcome | ||||||||
| Recurrence/progression-related worry | 502 | 1.00 | 0.61 (0.42–0.89) | 0.01 | 546 | 1.00 | 0.43 (0.29–0.63) | <0.01 |
| New primary–related worry | 508 | 1.00 | 0.65 (0.44–0.97) | 0.04 | 547 | 1.00 | 0.50 (0.33–0.77) | <0.01 |
| Complication-related worry | 508 | 1.00 | 0.50 (0.33–0.76) | <0.01 | 545 | 1.00 | 0.54 (0.35–0.83) | <0.01 |
| B | N | LOW WORRY, OR (95% CI) | HIGH WORRY, OR (95% CI) | P | N | LOW WORRY, OR (95% CI) | HIGH WORRY,OR (95% CI) | P |
| Outcome | ||||||||
| Follow-up frequency | 485 | 1.00 | 1.81 (1.04–3.12) | 0.03 | 534 | 1.00 | 1.49 (1.00–2.22) | 0.05 |
| Phone call to follow-up clinic | 504 | 1.00 | 2.21 (1.48–3.31) | <0.01 | 543 | 1.00 | 1.74 (1.20–2.53) | 0.01 |
| Emergency room visit | 503 | 1.00 | 1.75 (0.90–3.43) | 0.10 | 549 | 1.00 | 0.88 (0.52–1.51) | 0.65 |
| C | N | LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P | N | LOW SPIRITUALITY, OR (95% CI) | HIGH SPIRITUALITY, OR (95% CI) | P |
| Outcome | ||||||||
| Follow-up frequency | 487 | 1.00 | 0.63 (0.37–1.10) | 0.11 | 536 | 1.00 | 0.88 (0.60–1.30) | 0.52 |
| Phone call to follow-up clinic | 506 | 1.00 | 0.77 (0.53–1.12) | 0.17 | 545 | 1.00 | 0.70 (0.49–1.00) | 0.04 |
| Emergency room visit | 505 | 1.00 | 0.56 (0.30–1.05) | 0.07 | 551 | 1.00 | 0.84 (0.50–1.41) | 0.50 |
Models adjusted for age, sex, cancer type, income, type of insurance, and time from last treatment
Relationship Between Patient Worry and Follow-Up Health-Care Utilization
Survivors who were highly worried about disease recurrence/progression, development of another type of cancer, and/or complications from treatment were more likely to visit their providers for follow-up care when compared with survivors who were less worried at 6 months (OR = 1.81, 95% CI 1.04–3.12, P = 0.03) and at 12 months (OR = 1.49, 95% CI 1.00–2.22, P = 0.05). Similarly, survivors who were highly worried were also more likely to place phone calls to their follow-up providers for medical inquiries than survivors who were less worried at 6 months (OR = 2.21, 95% CI 1.48–3.31, P < 0.01) and at 12 months (OR = 1.74, 95% CI 1.20–2.53, P = 0.01). We did not observe differences in emergency room visits between survivors with low and those with high rates of worrying at both 6 and 12 months (Table 3B).
Relationship Between Spirituality and Health-Care Utilization
No significant differences were noted for the frequency of follow-up visits, changes in follow-up providers, and emergency room visits between the levels of spirituality at both 6 and 12 months. However, at 12 months, highly spiritual survivors were less likely to call their follow-up providers for medical inquiries compared to survivors with low spirituality scores (OR = 0.70, 95% CI 0.49–1.00, P = 0.04) (Table 3C).
Interaction Between Spirituality and Patient Worry With Health-Care Utilization
Interaction between patient-rated worry and level of spirituality as it relates to health-care utilization was not statistically significant (data not shown). This suggests that spirituality does not modify the effect of patient worry in producing change in follow-up health-care utilization.
Discussion
Our study examined the relationships between spirituality, patient-rated worry, and follow-up health-care utilization among cancer survivors and found that individuals who possess higher levels of spirituality tend to have less worry of disease recurrence/progression, development of treatment-related complications, and development of new cancers. These findings are consistent with previous research among patients with advanced or terminal cancers that consistently showed such correlations between spirituality and general measures of anxiety.[10], [15], [17], [19], [30] and [31] Additionally, our study showed that a higher degree of worry about common concerns of cancer survivors is associated with more follow-up visits and calls to health-care providers. However, our data also showed that spirituality by itself is for the most part not associated with follow-up health-care utilization.
It has been documented that psychosocial factors like anxiety and spirituality can influence behaviors.[32], [33], [34], [35] and [36] Our analysis showed that both discretionary and nondiscretionary indices of health-care utilization increased significantly among highly worried cancer survivors. However, these increases are independent of one's level of spirituality. These results suggest that cancer survivors with a high degree of worry about disease recurrence/progression, development of treatment-related complications, or development of a new cancer produce a change in care-seeking behavior and may concomitantly alter the health provider's need to see the patient. Our results also suggest that while spirituality has an impact on one's level of worry, being less spiritual does not necessarily alter a cancer survivor's care-seeking behavior.
Worried patients present a potential problem for clinicians in that they may need more attention during clinic visits,37 may result in requests for more ancillary/diagnostic tests including imaging modalities,[38] and [39] or may use more medications[40] and [41] or resort to other alternative therapies[42], [43], [44] and [45] available to reduce their worries. Given that cancer patients already receive many chemotherapeutic agents for their treatment, many of them are more inclined to undergo alternative therapies.[16], [43], [46], [47] and [48] Spirituality-based interventions shown to be effective at reducing anxiety and increasing QOL may therefore have a role among cancer survivors. And because spirituality and religiosity are closely linked,29 faith-based interventions may also benefit the patient.
Our study has several implications in the assessment of cancer survivors in multidisciplinary survivorship clinics. While much attention about assessing depression, anxiety, and QOL has been given to cancer survivors, our study shows that the evaluation of one's spirituality may have some merit as well. Participants with low spirituality and a high degree of worry may benefit from activities that enhance spirituality (e.g., yoga, meditation). Because of the increasing number of cancer survivors,[32] and [49] development of clinic-based spiritual interventions to address common worries of cancer survivors may be appropriate. In addition to the implications for clinical practice, our study has implications for future research. While the literature has shown a correlation between spirituality and religiosity,29 these two concepts are not the same.[1], [2], [50], [51] and [52] It would have been interesting to compare outcomes by level of spirituality and religiosity, but our data revealed a high degree of correlation between these two concepts. Over 90% of individuals who are spiritual are also religious.[28], [53] and [54] This may be the reason that some spirituality-based interventions have enhancement of religious activities as main approaches to improve spirituality.[28] and [53]
While our study has the strengths associated with a prospective study in a relatively large number of cancer survivors treated in a single medical center, it has several limitations. Our participation rate at baseline was only 50%, although our retention rates at 6 and 12 months were on average 80%. Another limitation of our study is that the baseline surveys were conducted at different time intervals from last treatment, although this limitation also allowed us to include all kinds of cancer survivors in terms of disease and time interval from last cancer treatment. Analysis confined to patients who received treatment within the last 5 years (n = 371) showed essentially the same results. We also compared the baseline spirituality scores of the study participants according to time from last treatment to study participation (0–2, 2–5, >5) and showed no statistically significant differences. Additionally, we adjusted for time from last treatment to study participation in the multivariate analyses. Combining all the participants into one analysis allowed for our exploratory analyses to have stronger statistical power. Another limitation of our study is the crude measurement of patient worry. However, in the absence of validated instruments to measure these worries, we felt the measures reflected subjective ratings of common worries by cancer survivors. Health-care utilization would have been ideally measured continuously to better quantify the medical services utilized. However, because we included a heterogeneous group of cancer patients, this measure would be highly variable and depend on the type of disease and treatment received by the patient. Thus, type of disease and time period from last treatment were adjusted for in the multivariate analyses.
In summary, cancer survivors who possess higher levels of spirituality tend to have a lesser degree of worry over disease recurrence/progression, development of treatment complications, and development of new cancers. A higher degree of worry about the common concerns of cancer survivors is associated with more follow-up visits and calls to health providers. However, our data showed that, for the most part, spirituality is not associated with follow-up health-care utilization.
Acknowledgments
The authors thank Linda Bauer, Garrett Frost, and Gregory McFadden for their help in coordinating the study and processing the data. This work was supported by the University of Nebraska Medical Center–Eppley Cancer Center (Support Grant P30 CA 036727) and the Medical Student Research Program. The funding source had no role in the design, collection, analysis, and interpretation of the data or in the writing of the article.
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Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
Costs and Outcomes of Acute Kidney Injury in Critically Ill Patients with Cancer
Volume 9, Issue 4, July-August 2011, Pages 149-155
Original research
Amit Lahoti MDa,
Background
Acute kidney injury (AKI) is a common complication in critically ill patients with cancer. The RIFLE criteria define three levels of AKI based on the percent increase in serum creatinine (Scr) from baseline: risk (≥50%), injury (≥100%), and failure (≥200% or requiring dialysis). The utility of the RIFLE criteria in critically ill patients with cancer is not known.
Objective
To examine the incidence, outcomes, and costs associated with AKI in critically ill patients with cancer.
Methods
We retrospectively analyzed all patients admitted to a single-center ICU over a 13-month period with a baseline Scr ≤1.5 mg/dL (n = 2,398). Kaplan-Meier estimates for survival by RIFLE category were calculated. Logistic regression was used to determine the association of AKI on 60-day mortality. A log-linear regression model was used for economic analysis. Costs were assessed by hospital charges from the provider's perspective.
Results
For the risk, injury, and failure categories of AKI, incidence rates were 6%, 2.8%, and 3.7%; 60-day survival estimates were 62%, 45%, and 14%; and adjusted odds ratios for 60-day mortality were 2.3, 3, and 14.3, respectively (P ≤ 0.001 compared to patients without AKI). Hematologic malignancy and hematopoietic cell transplant were not associated with mortality in the adjusted analysis. Hospital cost increased by 0.16% per 1% increase in creatinine and by 21% for patients requiring dialysis.
Limitations
Retrospective analysis. Single-center study. No adjustment by cost-to-charge ratios.
Conclusions
AKI is associated with higher mortality and costs in critically ill patients with cancer.
Over the past several years, important advances have occurred in the treatment and supportive care of critically ill patients with cancer.[1] However, acute kidney injury (AKI) remains a familiar complication and is a negative prognostic factor for overall survival.[2] and [3] The development of AKI can limit further cancer treatment, increase toxicity of chemotherapy and reduce its delivery, and exclude patients from clinical trials. Further, patients with AKI have been shown to have longer hospitalizations and increased hospital costs.[4] and [5] Recognized causes of AKI include acute tubular necrosis from medications or sepsis, volume depletion, tumor lysis syndrome, abdominal compartment syndrome, and obstruction from tumor or lymphadenopathy. Elevations in serum creatinine of as little as 0.3 mg/dL, which were previously considered insignificant, have been associated with a higher mortality rate in hospitalized patients.[4] However, few of the numerous definitions of AKI used in the cancer literature incorporate these subtle declines in kidney function.
An increase in serum creatinine has traditionally been used as a reflection of AKI. However, it is well known that elevation in serum creatinine is a relatively late marker of kidney injury.[6] In addition, patients with cancer often have decreased creatinine production secondary to cachexia, which may limit the sensitivity of creatinine as a marker of kidney injury. Other variables including total body volume, ethnicity, medications, and protein intake may also vary the serum creatinine level independent of renal function. Recent studies have demonstrated that a significant number of patients with cancer and normal serum creatinine have underlying chronic kidney disease (CKD) when renal function is estimated by the Cockcroft-Gault equation.[7] and [8] Therefore, using an arbitrarily defined level of serum creatinine as an indicator of AKI (i.e. >1.5 or 2.0 mg/dL) may not be suitable.
What may be a more accurate measure of kidney injury is a classification system based on the percent increase in serum creatinine relative to baseline. One such model is the Risk, Injury, Failure, Loss, and End-Stage Kidney (RIFLE) classification, which defines three levels of severity of AKI (risk, injury, and failure).[9] Previously, over 35 different definitions of AKI were used in the literature, which has made cross-comparisons between studies difficult.[10] The RIFLE classification provides a uniform definition of AKI and has been validated in numerous studies.[11], [12], [13], [14], [15], [16], [17] and [18] The aim of this analysis was to estimate the incidence, outcomes, and costs associated with AKI as defined by the RIFLE classification in critically ill patients with cancer.
Materials and Methods
The study included all patients ≥18 years of age who were admitted to the intensive care unit (ICU) at the University of Texas M.D. Anderson Cancer Center from December 2005 through December 2006. Patients with a baseline serum creatinine >1.5 mg/dL were excluded from the analysis. The protocol was approved by the institutional review board. Demographic and clinical data were obtained from the Department of Critical Care database, the Department of Pharmacy database, and the global institutional database (Enterprise Information Warehouse). The data were incorporated into a single spreadsheet using Excel 12.2 for Mac (Microsoft, Redmond, WA).
RIFLE categories for AKI were defined by the percent increase in serum creatinine from the time of ICU admission to the maximum creatinine at any point during the ICU stay: risk (≥50% rise in serum creatinine), injury (≥100% rise in serum creatinine), and failure (≥200% rise in serum creatinine). Consistent with the Acute Kidney Injury Network modifications of the original criteria, patients who required dialysis were classified into the RIFLE failure category, irrespective of the percent rise in serum creatinine.[19] The modality for continuous renal replacement therapy used at our institution is continuous slow low-efficiency dialysis (c-SLED), which has been described previously.[20] For patients who did not have an initial creatinine available within 24 hours after ICU admission, the most recent prior creatinine within the previous 48 hours was used.
Statistics
Descriptive data are presented as medians with interquartile ranges for continuous variables and absolute numbers with percentages for categorical variables. Survival of patients with AKI as defined by the RIFLE criteria was estimated by the Kaplan-Meier method. Patients were censored at death or last known follow-up, as determined by the clinical record. Statistical significance was determined by the log-rank test.
The primary end point for logistic regression was death at 60 days after ICU admission. Two separate models were developed, examining AKI as a categorical variable (RIFLE categories) and as a continuous variable (percent increase in creatinine from baseline). The variable “age” was significantly associated in a linear fashion with log odds of death but was dichotomized to provide a more meaningful odds ratio for the reader. Correlated data were assessed by correlation coefficients, and no variables were significantly correlated >0.6. Model reduction was achieved by variable elimination using the likelihood ratio test between nested models. Predictive ability and goodness-of-fit statistics were calculated, and the model was internally validated. No significant interactions were identified in either logistic regression model.
Lastly, a multivariate log linear regression model was developed to assess the relationship of AKI and dialysis with hospital cost. Cost was defined as hospital charges from the provider perspective. Log transformation of “cost” was used to account for skewness and heteroskedasticity. Coefficients in this model were multiplied by a factor of 100 to estimate a percent change in the dependent variable (cost) associated with a unit change in the independent variable.[21]
A two-tailed P < 0.05 was considered statistically significant. No patients were excluded from the analysis because of missing data. Statistical analysis was performed with Stata 10 for Mac (StataCorp, College Station, TX).
Results
The data set included 2,398 patients. Patient characteristics are listed in Table 1. The median age was 59 years. The cohort was predominantly Caucasian (75%) and relatively balanced with respect to gender. The majority of patients on a medical service were admitted to the hospital from the emergency room (76%), compared to only 10% of patients on a surgical service. Sepsis was diagnosed in 23% of patients on a medical service vs. only 4% of patients on a surgical service. This is consistent with the large number of patients at our institution who were admitted to the ICU for routine monitoring after elective surgeries. A significant number of patients had underlying hypertension and diabetes (54% and 18%, respectively). One-third of patients had advanced malignancy by Surveillance, Epidemiology, and End Results (SEER) stage on initial presentation to our institution.
b Included if patient received therapy at any time from ICU admission to date of maximum creatinine.
The absolute number of patients developing AKI or requiring dialysis by hospital service is depicted in Figure 1. The incidence of AKI was higher among patients on a medical vs. a surgical service (21% vs. 6.6%). Patients with hematologic malignancies (leukemia, lymphoma, and myeloma) had the highest incidence of AKI and need for dialysis (28% and 9.3%, respectively). Among patients on a medical service, the odds for developing AKI or requiring dialysis were increased 1.9-fold and 5.4-fold, respectively, for patients with an underlying hematologic malignancy.
Figure 1.
Number of Patients with AKI or Needing Dialysis by Hospital Service
AKI, defined as a minimum 50% increase in serum creatinine from baseline, occurred in 301 patients (12.6%), of whom 56 (2.3%) required dialysis. By further defining AKI by the RIFLE criteria, we classified 6%, 3%, and 4% of patients into the RIFLE risk, injury, and failure categories, respectively. The median elevations in creatinine from baseline were 0.6, 1.1, and 2 mg/dL, respectively. The median time to maximum creatinine was two days for all patients with AKI. There was a stepwise decrease in estimated survival associated with each RIFLE category (Figure 2). Among patients in the RIFLE failure group, the estimated survival was similar between those who required dialysis and those who did not (P = 0.99, log-rank). Although survival for patients requiring dialysis was dismal overall, it was significantly worse for patients with underlying hematological malignancy vs. solid tumor (3% vs. 20%, respectively).
The results of the logistic regression model for predictors of death at 60 days after ICU admission is presented in Table 2. Race and gender were not significant on univariate or multivariate analyses. Although significant on univariate analysis, hematologic malignancy, prior hematopoietic cell transplant (HCT), baseline comorbidities (hypertension, diabetes, heart failure, liver disease), and sepsis were also eliminated during model reduction. After adjusting for the remaining covariates, the RIFLE risk, injury, and failure categories remained significantly associated with 60-day mortality with odds ratios of 2.3, 3.0, and 14, respectively.
| VARIABLE | UNIVARIATE | MULTIVARIATE | |||
|---|---|---|---|---|---|
| OR | P | OR | 95% CI | P | |
| Age ≥55 years | 1.2 | 0.08 | 1.5 | 1.1–1.9 | 0.007 |
| Male vs. female | 0.997 | 0.98 | |||
| Ethnicity | |||||
| Black vs. white | 2.0 | <0.001 | |||
| Hispanic vs. white | 1.1 | 0.39 | |||
| Other vs. white | 0.8 | 0.46 | |||
| Hypertension | 1.3 | 0.02 | |||
| Diabetes | 1.6 | <0.001 | |||
| Heart failure | 2.5 | <0.001 | |||
| Chronic liver disease | 1.8 | 0.02 | |||
| RIFLE category | |||||
| Risk vs. no AKI | 4.1 | <0.001 | 2.3 | 1.5–3.6 | <0.001 |
| Injury vs. no AKI | 8.1 | <0.001 | 3.0 | 1.6–5.8 | 0.001 |
| Failure vs. no AKI | 35 | <0.001 | 14.3 | 7.2–29.0 | <0.001 |
| Amphotericin | 10.9 | <0.001 | 1.9 | 1.1–3.3 | 0.03 |
| Vasopressors | 6.3 | <0.001 | 2.0 | 1.4–2.6 | <0.001 |
| Mechanical ventilation | 2.1 | <0.001 | 1.9 | 1.4–2.5 | <0.001 |
| IV diuretics | 3.8 | <0.001 | 1.4 | 1.1–1.9 | 0.015 |
| Sepsis | 5.7 | <0.001 | |||
| Medical vs. surgical service | 9.9 | <0.001 | 2.2 | 1.5–3.1 | <0.001 |
| Liquid vs. solid tumor | 5.5 | <0.001 | |||
| Prior HCT | |||||
| Autologous | 1.7 | 0.23 | |||
| Allogeneic | 6.0 | <0.001 | |||
| Advanced vs. locoregional stage (SEER) | 4.4 | <0.001 | 2.1 | 1.6–2.6 | <0.001 |
| ER admission | 11.3 | <0.001 | 5.3 | 3.7–7.6 | <0.001 |
| Pre-ICU length of stay | 1.06 | <0.001 | 1.02 | 1.0–1.03 | 0.02 |
Likelihood ratio x2(12) = 818 (P < 0.001), positive predictive value 72%, negative predictive value 88%; area under the receiver operating curve = 0.88, Hosmer-Lemeshow x2(8) = 6.8 (P = 0.56).
OR, odds ratio; AKI, acute kidney injury; HCT, hematopoietic cell transplant; ER, emergency room; ICU, intensive care unit.
To further assess the relationship between serum creatinine and mortality, a separate logistic regression was performed using “percent rise in creatinine” as a continuous predictor variable (Table 3). Need for dialysis was also included as an independent variable. Aside from “percent rise in creatinine” and dialysis, model reduction yielded the same covariates as in the initial model. Dialysis had the largest effect on the odds of 60-day mortality (odds ratio = 6.2). After adjusting for dialysis, “percent rise in creatinine” remained significantly associated with 60-day mortality. For example, a 10% rise in creatinine increased the odds of mortality by 8%. The predictive capabilities of both logistic regression models were similar.
| VARIABLE | OR | 95% CI | P |
|---|---|---|---|
| Age ≥55 years | 1.4 | 1.1–1.9 | <0.001 |
| Percent increase in creatinine | 1.008 | 1.005–1.01 | <0.001 |
| ER admission | 5.4 | 3.8–7.7 | <0.001 |
| Pre-ICU length of stay (days) | 1.02 | 1.00–1.04 | 0.016 |
| SEER stage (distant vs. other) | 2.0 | 1.6–2.7 | <0.001 |
| Medical vs. surgical service | 2.2 | 1.5–3.2 | <0.001 |
| Vasopressors | 2.0 | 1.5–2.7 | <0.001 |
| Mechanical ventilation | 1.8 | 1.4–2.5 | <0.001 |
| Amphotericin | 1.8 | 1.1–3.2 | 0.031 |
| IV diuretics | 1.4 | 1.0–1.8 | 0.024 |
| Dialysis | 6.2 | 2.3–16.5 | <0.001 |
Likelihood ratio x2(11) = 815 (P < 0.001), positive predictive value 72%, negative predictive value 88%, area under the receiver operating curve = 0.88.
OR, odds ratio; ICU, intensive care unit; AKI, acute kidney injury; ER, emergency room.
We included AKI as a continuous variable in a multivariate regression to determine the relationship of AKI and dialysis with hospital cost (Table 4). The model was adjusted for numerous clinical and demographic variables. Age, gender, race, autologous transplant, tumor grade, diabetes, and liver disease were not significant predictors of hospital cost in the final model. The need for dialysis was associated with a 21% increase in hospital cost. Each percent increase in serum creatinine was associated with a 0.16% increase in cost. An interaction was identified between mechanical ventilation and sepsis (25% increase in hospital cost).
| VARIABLE | β | SE | P |
|---|---|---|---|
| Increase in creatinine (per 1%) | 0.00156 | 0.000257 | <0.001 |
| Dialysis | 0.213 | 0.0994 | 0.032 |
| Diuretics | 0.0831 | 0.0180 | <0.001 |
| Mechanical ventilation | 0.561 | 0.0299 | <0.001 |
| Allotransplant | 0.538 | 0.0960 | <0.001 |
| Medical vs. surgical service | 0.259 | 0.0381 | <0.001 |
| Liquid vs. solid tumor | 0.227 | 0.0433 | <0.001 |
| Distant vs. locoregional stage | 0.0717 | 0.0314 | 0.023 |
| Sepsis | 0.151 | 0.0622 | 0.015 |
| ER admission | −0.246 | 0.038 | <0.001 |
| Heart failure | 0.107 | 0.0469 | 0.023 |
| Hypertension | 0.0647 | 0.0271 | 0.017 |
| Mechanical ventilation × sepsis | 0.251 | 0.0853 | 0.003 |
| Constant | 10.8 | 0.0254 | <0.001 |
R2 = 0.32.
Discussion
The incidence of AKI in our study was 12.6% of all patients admitted to the ICU, and there was a progressive decrease in survival associated with worsening kidney injury. This association remained even after adjusting for covariates. AKI and the need for dialysis were also associated with increased hospital costs. To our knowledge, this is the largest single-center study to examine the RIFLE criteria for AKI in a critically-ill population with cancer.
A striking finding in our study is the significant effect that small elevations in serum creatinine may have on survival. An increase of 0.6 mg/dL in the RIFLE risk category increased the odds for mortality by a factor of 2.3 compared to patients without AKI. The median maximum creatinine in this group was only 1.3 mg/dL, which is still within the “normal” range for males in our institution. Criteria that define mild renal toxicity as a serum greater than “1.5 × the upper limit of normal” would exclude a significant number of patients in the RIFLE risk and injury categories, although their risk of mortality was significantly increased.[22] Other criteria that define AKI by glomerular filtration rate (GFR) are also problematic as estimating equations for GFR require serum creatinine to be in steady state. This is a false assumption to make in the setting of AKI, where serum creatinine may fluctuate daily. Serum creatinine is an insensitive marker of renal injury in patients with cancer, and more sensitive and specific biomarkers of AKI are currently under development.[23], [24] and [25] Until these markers are routinely available, renal injury in oncology practice and clinical trials may be better defined as a percentage rise in serum creatinine relative to baseline, similar to the RIFLE criteria.
Out of all variables examined, it is interesting that the need for dialysis had the greatest association with 60-day mortality (Table 3). Although we adjusted for other risk factors, there may still be residual confounding to explain the strong association of dialysis with mortality. However, it is also recognized that dialysis may promote a proinflammatory state[26] and that AKI, in itself, may lead to injury of distant organs via systemic cytokine release.[27] and [28] These deleterious effects may be amplified in patients with cancer, who frequently are neutropenic and have chronic inflammation (e.g. capillary leak syndrome, diffuse alveolar hemorrhage, graft-vs.-host disease). It is known that the need for dialysis after a stem cell transplant is associated with >70% mortality.[29] Although dialysis remains pivotal for volume and metabolic clearance, a true “therapy” for AKI has unfortunately remained elusive thus far.
Our overall incidence of 12.6% for AKI is lower than the reported incidence of 13%–42% in other studies of critically ill patients with cancer.[2], [30] and [31] We excluded patients who had a serum creatinine >1.5 mg/dL on admission to the ICU as we were interested in the development of AKI after ICU admission. This likely excluded patients who already had AKI on presentation, which may have contributed to the lower incidence of AKI and the need for dialysis in our study. Unlike previous studies, our cohort included a large number of patients on a surgical service who were electively admitted to the ICU for routine postoperative care and, therefore, were at lower risk of developing AKI. However, when limited to patients on a medical service, our incidence of 21% is consistent with the results of previous studies of patients in medical ICUs.
The prognosis of patients requiring dialysis was dismal, with an estimated 89% 60-day mortality. This is somewhat higher than the reported mortality of 66%–88% in previous studies.[32], [33], [34], [35] and [36] Given that our institution also serves as a referral cancer center for patients who have had progressive disease on standard therapy, it is possible that our patient population may have been more predisposed to complications from cancer therapy. Patients with hematological malignancies had a higher incidence of AKI and need for dialysis. However, underlying hematological malignancy and HCT were no longer significantly associated with 60-day mortality in the adjusted analysis. Similar to the findings of others, this would suggest that it is not the underlying malignancy itself but rather the complications of treatment and prolonged immunosuppression that lead to decreased survival in these patients.[37] and [38] Early goal-directed intensive life support should be considered for most patients,[39] but continuation of dialysis may not be of benefit, in terms of both survival and cost, in patients with hematologic malignancy who demonstrate minimal improvement.
Our study had certain limitations. Given the retrospective design, we cannot rule out selection bias or residual confounding. We were able to adjust for several variables specific to cancer and critical care as well as pre-ICU length of stay, which may be a surrogate marker for comorbidities and functional status. Nonetheless, our conclusions should be interpreted as hypothesis-generating. Second, our study is based on a single-center experience, which may limit its generalizability. Nonetheless, our study had a large sample size that was subjected to fairly uniform management. Third, we did not have data on end-of-life decisions, which may have impacted mortality and need for dialysis. Lastly, we were unable to obtain cost-to-charge ratios, which may limit the generalizability of our findings to other institutions. However, we reported on percent increases in cost as opposed to absolute dollar figures, which may adjust for some of this variation.
Conclusions
AKI as defined by the RIFLE criteria may be predictive of short-term mortality in critically ill patients with cancer. We have demonstrated that relatively small changes in serum creatinine are associated with higher mortality and that the need for dialysis entails a very poor prognosis. The mechanism behind the increased mortality in patients with hematological malignancies appears to be secondary to the associated complications of therapy, as opposed to the underlying cancer itself. We hypothesize that strategies to prevent the development of AKI and progression to dialysis dependence may improve survival. Whether the prevention of AKI translates to cost savings is also of interest.
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Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
Volume 9, Issue 4, July-August 2011, Pages 149-155
Original research
Amit Lahoti MDa,
Background
Acute kidney injury (AKI) is a common complication in critically ill patients with cancer. The RIFLE criteria define three levels of AKI based on the percent increase in serum creatinine (Scr) from baseline: risk (≥50%), injury (≥100%), and failure (≥200% or requiring dialysis). The utility of the RIFLE criteria in critically ill patients with cancer is not known.
Objective
To examine the incidence, outcomes, and costs associated with AKI in critically ill patients with cancer.
Methods
We retrospectively analyzed all patients admitted to a single-center ICU over a 13-month period with a baseline Scr ≤1.5 mg/dL (n = 2,398). Kaplan-Meier estimates for survival by RIFLE category were calculated. Logistic regression was used to determine the association of AKI on 60-day mortality. A log-linear regression model was used for economic analysis. Costs were assessed by hospital charges from the provider's perspective.
Results
For the risk, injury, and failure categories of AKI, incidence rates were 6%, 2.8%, and 3.7%; 60-day survival estimates were 62%, 45%, and 14%; and adjusted odds ratios for 60-day mortality were 2.3, 3, and 14.3, respectively (P ≤ 0.001 compared to patients without AKI). Hematologic malignancy and hematopoietic cell transplant were not associated with mortality in the adjusted analysis. Hospital cost increased by 0.16% per 1% increase in creatinine and by 21% for patients requiring dialysis.
Limitations
Retrospective analysis. Single-center study. No adjustment by cost-to-charge ratios.
Conclusions
AKI is associated with higher mortality and costs in critically ill patients with cancer.
Over the past several years, important advances have occurred in the treatment and supportive care of critically ill patients with cancer.[1] However, acute kidney injury (AKI) remains a familiar complication and is a negative prognostic factor for overall survival.[2] and [3] The development of AKI can limit further cancer treatment, increase toxicity of chemotherapy and reduce its delivery, and exclude patients from clinical trials. Further, patients with AKI have been shown to have longer hospitalizations and increased hospital costs.[4] and [5] Recognized causes of AKI include acute tubular necrosis from medications or sepsis, volume depletion, tumor lysis syndrome, abdominal compartment syndrome, and obstruction from tumor or lymphadenopathy. Elevations in serum creatinine of as little as 0.3 mg/dL, which were previously considered insignificant, have been associated with a higher mortality rate in hospitalized patients.[4] However, few of the numerous definitions of AKI used in the cancer literature incorporate these subtle declines in kidney function.
An increase in serum creatinine has traditionally been used as a reflection of AKI. However, it is well known that elevation in serum creatinine is a relatively late marker of kidney injury.[6] In addition, patients with cancer often have decreased creatinine production secondary to cachexia, which may limit the sensitivity of creatinine as a marker of kidney injury. Other variables including total body volume, ethnicity, medications, and protein intake may also vary the serum creatinine level independent of renal function. Recent studies have demonstrated that a significant number of patients with cancer and normal serum creatinine have underlying chronic kidney disease (CKD) when renal function is estimated by the Cockcroft-Gault equation.[7] and [8] Therefore, using an arbitrarily defined level of serum creatinine as an indicator of AKI (i.e. >1.5 or 2.0 mg/dL) may not be suitable.
What may be a more accurate measure of kidney injury is a classification system based on the percent increase in serum creatinine relative to baseline. One such model is the Risk, Injury, Failure, Loss, and End-Stage Kidney (RIFLE) classification, which defines three levels of severity of AKI (risk, injury, and failure).[9] Previously, over 35 different definitions of AKI were used in the literature, which has made cross-comparisons between studies difficult.[10] The RIFLE classification provides a uniform definition of AKI and has been validated in numerous studies.[11], [12], [13], [14], [15], [16], [17] and [18] The aim of this analysis was to estimate the incidence, outcomes, and costs associated with AKI as defined by the RIFLE classification in critically ill patients with cancer.
Materials and Methods
The study included all patients ≥18 years of age who were admitted to the intensive care unit (ICU) at the University of Texas M.D. Anderson Cancer Center from December 2005 through December 2006. Patients with a baseline serum creatinine >1.5 mg/dL were excluded from the analysis. The protocol was approved by the institutional review board. Demographic and clinical data were obtained from the Department of Critical Care database, the Department of Pharmacy database, and the global institutional database (Enterprise Information Warehouse). The data were incorporated into a single spreadsheet using Excel 12.2 for Mac (Microsoft, Redmond, WA).
RIFLE categories for AKI were defined by the percent increase in serum creatinine from the time of ICU admission to the maximum creatinine at any point during the ICU stay: risk (≥50% rise in serum creatinine), injury (≥100% rise in serum creatinine), and failure (≥200% rise in serum creatinine). Consistent with the Acute Kidney Injury Network modifications of the original criteria, patients who required dialysis were classified into the RIFLE failure category, irrespective of the percent rise in serum creatinine.[19] The modality for continuous renal replacement therapy used at our institution is continuous slow low-efficiency dialysis (c-SLED), which has been described previously.[20] For patients who did not have an initial creatinine available within 24 hours after ICU admission, the most recent prior creatinine within the previous 48 hours was used.
Statistics
Descriptive data are presented as medians with interquartile ranges for continuous variables and absolute numbers with percentages for categorical variables. Survival of patients with AKI as defined by the RIFLE criteria was estimated by the Kaplan-Meier method. Patients were censored at death or last known follow-up, as determined by the clinical record. Statistical significance was determined by the log-rank test.
The primary end point for logistic regression was death at 60 days after ICU admission. Two separate models were developed, examining AKI as a categorical variable (RIFLE categories) and as a continuous variable (percent increase in creatinine from baseline). The variable “age” was significantly associated in a linear fashion with log odds of death but was dichotomized to provide a more meaningful odds ratio for the reader. Correlated data were assessed by correlation coefficients, and no variables were significantly correlated >0.6. Model reduction was achieved by variable elimination using the likelihood ratio test between nested models. Predictive ability and goodness-of-fit statistics were calculated, and the model was internally validated. No significant interactions were identified in either logistic regression model.
Lastly, a multivariate log linear regression model was developed to assess the relationship of AKI and dialysis with hospital cost. Cost was defined as hospital charges from the provider perspective. Log transformation of “cost” was used to account for skewness and heteroskedasticity. Coefficients in this model were multiplied by a factor of 100 to estimate a percent change in the dependent variable (cost) associated with a unit change in the independent variable.[21]
A two-tailed P < 0.05 was considered statistically significant. No patients were excluded from the analysis because of missing data. Statistical analysis was performed with Stata 10 for Mac (StataCorp, College Station, TX).
Results
The data set included 2,398 patients. Patient characteristics are listed in Table 1. The median age was 59 years. The cohort was predominantly Caucasian (75%) and relatively balanced with respect to gender. The majority of patients on a medical service were admitted to the hospital from the emergency room (76%), compared to only 10% of patients on a surgical service. Sepsis was diagnosed in 23% of patients on a medical service vs. only 4% of patients on a surgical service. This is consistent with the large number of patients at our institution who were admitted to the ICU for routine monitoring after elective surgeries. A significant number of patients had underlying hypertension and diabetes (54% and 18%, respectively). One-third of patients had advanced malignancy by Surveillance, Epidemiology, and End Results (SEER) stage on initial presentation to our institution.
b Included if patient received therapy at any time from ICU admission to date of maximum creatinine.
The absolute number of patients developing AKI or requiring dialysis by hospital service is depicted in Figure 1. The incidence of AKI was higher among patients on a medical vs. a surgical service (21% vs. 6.6%). Patients with hematologic malignancies (leukemia, lymphoma, and myeloma) had the highest incidence of AKI and need for dialysis (28% and 9.3%, respectively). Among patients on a medical service, the odds for developing AKI or requiring dialysis were increased 1.9-fold and 5.4-fold, respectively, for patients with an underlying hematologic malignancy.
Figure 1.
Number of Patients with AKI or Needing Dialysis by Hospital Service
AKI, defined as a minimum 50% increase in serum creatinine from baseline, occurred in 301 patients (12.6%), of whom 56 (2.3%) required dialysis. By further defining AKI by the RIFLE criteria, we classified 6%, 3%, and 4% of patients into the RIFLE risk, injury, and failure categories, respectively. The median elevations in creatinine from baseline were 0.6, 1.1, and 2 mg/dL, respectively. The median time to maximum creatinine was two days for all patients with AKI. There was a stepwise decrease in estimated survival associated with each RIFLE category (Figure 2). Among patients in the RIFLE failure group, the estimated survival was similar between those who required dialysis and those who did not (P = 0.99, log-rank). Although survival for patients requiring dialysis was dismal overall, it was significantly worse for patients with underlying hematological malignancy vs. solid tumor (3% vs. 20%, respectively).
The results of the logistic regression model for predictors of death at 60 days after ICU admission is presented in Table 2. Race and gender were not significant on univariate or multivariate analyses. Although significant on univariate analysis, hematologic malignancy, prior hematopoietic cell transplant (HCT), baseline comorbidities (hypertension, diabetes, heart failure, liver disease), and sepsis were also eliminated during model reduction. After adjusting for the remaining covariates, the RIFLE risk, injury, and failure categories remained significantly associated with 60-day mortality with odds ratios of 2.3, 3.0, and 14, respectively.
| VARIABLE | UNIVARIATE | MULTIVARIATE | |||
|---|---|---|---|---|---|
| OR | P | OR | 95% CI | P | |
| Age ≥55 years | 1.2 | 0.08 | 1.5 | 1.1–1.9 | 0.007 |
| Male vs. female | 0.997 | 0.98 | |||
| Ethnicity | |||||
| Black vs. white | 2.0 | <0.001 | |||
| Hispanic vs. white | 1.1 | 0.39 | |||
| Other vs. white | 0.8 | 0.46 | |||
| Hypertension | 1.3 | 0.02 | |||
| Diabetes | 1.6 | <0.001 | |||
| Heart failure | 2.5 | <0.001 | |||
| Chronic liver disease | 1.8 | 0.02 | |||
| RIFLE category | |||||
| Risk vs. no AKI | 4.1 | <0.001 | 2.3 | 1.5–3.6 | <0.001 |
| Injury vs. no AKI | 8.1 | <0.001 | 3.0 | 1.6–5.8 | 0.001 |
| Failure vs. no AKI | 35 | <0.001 | 14.3 | 7.2–29.0 | <0.001 |
| Amphotericin | 10.9 | <0.001 | 1.9 | 1.1–3.3 | 0.03 |
| Vasopressors | 6.3 | <0.001 | 2.0 | 1.4–2.6 | <0.001 |
| Mechanical ventilation | 2.1 | <0.001 | 1.9 | 1.4–2.5 | <0.001 |
| IV diuretics | 3.8 | <0.001 | 1.4 | 1.1–1.9 | 0.015 |
| Sepsis | 5.7 | <0.001 | |||
| Medical vs. surgical service | 9.9 | <0.001 | 2.2 | 1.5–3.1 | <0.001 |
| Liquid vs. solid tumor | 5.5 | <0.001 | |||
| Prior HCT | |||||
| Autologous | 1.7 | 0.23 | |||
| Allogeneic | 6.0 | <0.001 | |||
| Advanced vs. locoregional stage (SEER) | 4.4 | <0.001 | 2.1 | 1.6–2.6 | <0.001 |
| ER admission | 11.3 | <0.001 | 5.3 | 3.7–7.6 | <0.001 |
| Pre-ICU length of stay | 1.06 | <0.001 | 1.02 | 1.0–1.03 | 0.02 |
Likelihood ratio x2(12) = 818 (P < 0.001), positive predictive value 72%, negative predictive value 88%; area under the receiver operating curve = 0.88, Hosmer-Lemeshow x2(8) = 6.8 (P = 0.56).
OR, odds ratio; AKI, acute kidney injury; HCT, hematopoietic cell transplant; ER, emergency room; ICU, intensive care unit.
To further assess the relationship between serum creatinine and mortality, a separate logistic regression was performed using “percent rise in creatinine” as a continuous predictor variable (Table 3). Need for dialysis was also included as an independent variable. Aside from “percent rise in creatinine” and dialysis, model reduction yielded the same covariates as in the initial model. Dialysis had the largest effect on the odds of 60-day mortality (odds ratio = 6.2). After adjusting for dialysis, “percent rise in creatinine” remained significantly associated with 60-day mortality. For example, a 10% rise in creatinine increased the odds of mortality by 8%. The predictive capabilities of both logistic regression models were similar.
| VARIABLE | OR | 95% CI | P |
|---|---|---|---|
| Age ≥55 years | 1.4 | 1.1–1.9 | <0.001 |
| Percent increase in creatinine | 1.008 | 1.005–1.01 | <0.001 |
| ER admission | 5.4 | 3.8–7.7 | <0.001 |
| Pre-ICU length of stay (days) | 1.02 | 1.00–1.04 | 0.016 |
| SEER stage (distant vs. other) | 2.0 | 1.6–2.7 | <0.001 |
| Medical vs. surgical service | 2.2 | 1.5–3.2 | <0.001 |
| Vasopressors | 2.0 | 1.5–2.7 | <0.001 |
| Mechanical ventilation | 1.8 | 1.4–2.5 | <0.001 |
| Amphotericin | 1.8 | 1.1–3.2 | 0.031 |
| IV diuretics | 1.4 | 1.0–1.8 | 0.024 |
| Dialysis | 6.2 | 2.3–16.5 | <0.001 |
Likelihood ratio x2(11) = 815 (P < 0.001), positive predictive value 72%, negative predictive value 88%, area under the receiver operating curve = 0.88.
OR, odds ratio; ICU, intensive care unit; AKI, acute kidney injury; ER, emergency room.
We included AKI as a continuous variable in a multivariate regression to determine the relationship of AKI and dialysis with hospital cost (Table 4). The model was adjusted for numerous clinical and demographic variables. Age, gender, race, autologous transplant, tumor grade, diabetes, and liver disease were not significant predictors of hospital cost in the final model. The need for dialysis was associated with a 21% increase in hospital cost. Each percent increase in serum creatinine was associated with a 0.16% increase in cost. An interaction was identified between mechanical ventilation and sepsis (25% increase in hospital cost).
| VARIABLE | β | SE | P |
|---|---|---|---|
| Increase in creatinine (per 1%) | 0.00156 | 0.000257 | <0.001 |
| Dialysis | 0.213 | 0.0994 | 0.032 |
| Diuretics | 0.0831 | 0.0180 | <0.001 |
| Mechanical ventilation | 0.561 | 0.0299 | <0.001 |
| Allotransplant | 0.538 | 0.0960 | <0.001 |
| Medical vs. surgical service | 0.259 | 0.0381 | <0.001 |
| Liquid vs. solid tumor | 0.227 | 0.0433 | <0.001 |
| Distant vs. locoregional stage | 0.0717 | 0.0314 | 0.023 |
| Sepsis | 0.151 | 0.0622 | 0.015 |
| ER admission | −0.246 | 0.038 | <0.001 |
| Heart failure | 0.107 | 0.0469 | 0.023 |
| Hypertension | 0.0647 | 0.0271 | 0.017 |
| Mechanical ventilation × sepsis | 0.251 | 0.0853 | 0.003 |
| Constant | 10.8 | 0.0254 | <0.001 |
R2 = 0.32.
Discussion
The incidence of AKI in our study was 12.6% of all patients admitted to the ICU, and there was a progressive decrease in survival associated with worsening kidney injury. This association remained even after adjusting for covariates. AKI and the need for dialysis were also associated with increased hospital costs. To our knowledge, this is the largest single-center study to examine the RIFLE criteria for AKI in a critically-ill population with cancer.
A striking finding in our study is the significant effect that small elevations in serum creatinine may have on survival. An increase of 0.6 mg/dL in the RIFLE risk category increased the odds for mortality by a factor of 2.3 compared to patients without AKI. The median maximum creatinine in this group was only 1.3 mg/dL, which is still within the “normal” range for males in our institution. Criteria that define mild renal toxicity as a serum greater than “1.5 × the upper limit of normal” would exclude a significant number of patients in the RIFLE risk and injury categories, although their risk of mortality was significantly increased.[22] Other criteria that define AKI by glomerular filtration rate (GFR) are also problematic as estimating equations for GFR require serum creatinine to be in steady state. This is a false assumption to make in the setting of AKI, where serum creatinine may fluctuate daily. Serum creatinine is an insensitive marker of renal injury in patients with cancer, and more sensitive and specific biomarkers of AKI are currently under development.[23], [24] and [25] Until these markers are routinely available, renal injury in oncology practice and clinical trials may be better defined as a percentage rise in serum creatinine relative to baseline, similar to the RIFLE criteria.
Out of all variables examined, it is interesting that the need for dialysis had the greatest association with 60-day mortality (Table 3). Although we adjusted for other risk factors, there may still be residual confounding to explain the strong association of dialysis with mortality. However, it is also recognized that dialysis may promote a proinflammatory state[26] and that AKI, in itself, may lead to injury of distant organs via systemic cytokine release.[27] and [28] These deleterious effects may be amplified in patients with cancer, who frequently are neutropenic and have chronic inflammation (e.g. capillary leak syndrome, diffuse alveolar hemorrhage, graft-vs.-host disease). It is known that the need for dialysis after a stem cell transplant is associated with >70% mortality.[29] Although dialysis remains pivotal for volume and metabolic clearance, a true “therapy” for AKI has unfortunately remained elusive thus far.
Our overall incidence of 12.6% for AKI is lower than the reported incidence of 13%–42% in other studies of critically ill patients with cancer.[2], [30] and [31] We excluded patients who had a serum creatinine >1.5 mg/dL on admission to the ICU as we were interested in the development of AKI after ICU admission. This likely excluded patients who already had AKI on presentation, which may have contributed to the lower incidence of AKI and the need for dialysis in our study. Unlike previous studies, our cohort included a large number of patients on a surgical service who were electively admitted to the ICU for routine postoperative care and, therefore, were at lower risk of developing AKI. However, when limited to patients on a medical service, our incidence of 21% is consistent with the results of previous studies of patients in medical ICUs.
The prognosis of patients requiring dialysis was dismal, with an estimated 89% 60-day mortality. This is somewhat higher than the reported mortality of 66%–88% in previous studies.[32], [33], [34], [35] and [36] Given that our institution also serves as a referral cancer center for patients who have had progressive disease on standard therapy, it is possible that our patient population may have been more predisposed to complications from cancer therapy. Patients with hematological malignancies had a higher incidence of AKI and need for dialysis. However, underlying hematological malignancy and HCT were no longer significantly associated with 60-day mortality in the adjusted analysis. Similar to the findings of others, this would suggest that it is not the underlying malignancy itself but rather the complications of treatment and prolonged immunosuppression that lead to decreased survival in these patients.[37] and [38] Early goal-directed intensive life support should be considered for most patients,[39] but continuation of dialysis may not be of benefit, in terms of both survival and cost, in patients with hematologic malignancy who demonstrate minimal improvement.
Our study had certain limitations. Given the retrospective design, we cannot rule out selection bias or residual confounding. We were able to adjust for several variables specific to cancer and critical care as well as pre-ICU length of stay, which may be a surrogate marker for comorbidities and functional status. Nonetheless, our conclusions should be interpreted as hypothesis-generating. Second, our study is based on a single-center experience, which may limit its generalizability. Nonetheless, our study had a large sample size that was subjected to fairly uniform management. Third, we did not have data on end-of-life decisions, which may have impacted mortality and need for dialysis. Lastly, we were unable to obtain cost-to-charge ratios, which may limit the generalizability of our findings to other institutions. However, we reported on percent increases in cost as opposed to absolute dollar figures, which may adjust for some of this variation.
Conclusions
AKI as defined by the RIFLE criteria may be predictive of short-term mortality in critically ill patients with cancer. We have demonstrated that relatively small changes in serum creatinine are associated with higher mortality and that the need for dialysis entails a very poor prognosis. The mechanism behind the increased mortality in patients with hematological malignancies appears to be secondary to the associated complications of therapy, as opposed to the underlying cancer itself. We hypothesize that strategies to prevent the development of AKI and progression to dialysis dependence may improve survival. Whether the prevention of AKI translates to cost savings is also of interest.
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Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
Volume 9, Issue 4, July-August 2011, Pages 149-155
Original research
Amit Lahoti MDa,
Background
Acute kidney injury (AKI) is a common complication in critically ill patients with cancer. The RIFLE criteria define three levels of AKI based on the percent increase in serum creatinine (Scr) from baseline: risk (≥50%), injury (≥100%), and failure (≥200% or requiring dialysis). The utility of the RIFLE criteria in critically ill patients with cancer is not known.
Objective
To examine the incidence, outcomes, and costs associated with AKI in critically ill patients with cancer.
Methods
We retrospectively analyzed all patients admitted to a single-center ICU over a 13-month period with a baseline Scr ≤1.5 mg/dL (n = 2,398). Kaplan-Meier estimates for survival by RIFLE category were calculated. Logistic regression was used to determine the association of AKI on 60-day mortality. A log-linear regression model was used for economic analysis. Costs were assessed by hospital charges from the provider's perspective.
Results
For the risk, injury, and failure categories of AKI, incidence rates were 6%, 2.8%, and 3.7%; 60-day survival estimates were 62%, 45%, and 14%; and adjusted odds ratios for 60-day mortality were 2.3, 3, and 14.3, respectively (P ≤ 0.001 compared to patients without AKI). Hematologic malignancy and hematopoietic cell transplant were not associated with mortality in the adjusted analysis. Hospital cost increased by 0.16% per 1% increase in creatinine and by 21% for patients requiring dialysis.
Limitations
Retrospective analysis. Single-center study. No adjustment by cost-to-charge ratios.
Conclusions
AKI is associated with higher mortality and costs in critically ill patients with cancer.
Over the past several years, important advances have occurred in the treatment and supportive care of critically ill patients with cancer.[1] However, acute kidney injury (AKI) remains a familiar complication and is a negative prognostic factor for overall survival.[2] and [3] The development of AKI can limit further cancer treatment, increase toxicity of chemotherapy and reduce its delivery, and exclude patients from clinical trials. Further, patients with AKI have been shown to have longer hospitalizations and increased hospital costs.[4] and [5] Recognized causes of AKI include acute tubular necrosis from medications or sepsis, volume depletion, tumor lysis syndrome, abdominal compartment syndrome, and obstruction from tumor or lymphadenopathy. Elevations in serum creatinine of as little as 0.3 mg/dL, which were previously considered insignificant, have been associated with a higher mortality rate in hospitalized patients.[4] However, few of the numerous definitions of AKI used in the cancer literature incorporate these subtle declines in kidney function.
An increase in serum creatinine has traditionally been used as a reflection of AKI. However, it is well known that elevation in serum creatinine is a relatively late marker of kidney injury.[6] In addition, patients with cancer often have decreased creatinine production secondary to cachexia, which may limit the sensitivity of creatinine as a marker of kidney injury. Other variables including total body volume, ethnicity, medications, and protein intake may also vary the serum creatinine level independent of renal function. Recent studies have demonstrated that a significant number of patients with cancer and normal serum creatinine have underlying chronic kidney disease (CKD) when renal function is estimated by the Cockcroft-Gault equation.[7] and [8] Therefore, using an arbitrarily defined level of serum creatinine as an indicator of AKI (i.e. >1.5 or 2.0 mg/dL) may not be suitable.
What may be a more accurate measure of kidney injury is a classification system based on the percent increase in serum creatinine relative to baseline. One such model is the Risk, Injury, Failure, Loss, and End-Stage Kidney (RIFLE) classification, which defines three levels of severity of AKI (risk, injury, and failure).[9] Previously, over 35 different definitions of AKI were used in the literature, which has made cross-comparisons between studies difficult.[10] The RIFLE classification provides a uniform definition of AKI and has been validated in numerous studies.[11], [12], [13], [14], [15], [16], [17] and [18] The aim of this analysis was to estimate the incidence, outcomes, and costs associated with AKI as defined by the RIFLE classification in critically ill patients with cancer.
Materials and Methods
The study included all patients ≥18 years of age who were admitted to the intensive care unit (ICU) at the University of Texas M.D. Anderson Cancer Center from December 2005 through December 2006. Patients with a baseline serum creatinine >1.5 mg/dL were excluded from the analysis. The protocol was approved by the institutional review board. Demographic and clinical data were obtained from the Department of Critical Care database, the Department of Pharmacy database, and the global institutional database (Enterprise Information Warehouse). The data were incorporated into a single spreadsheet using Excel 12.2 for Mac (Microsoft, Redmond, WA).
RIFLE categories for AKI were defined by the percent increase in serum creatinine from the time of ICU admission to the maximum creatinine at any point during the ICU stay: risk (≥50% rise in serum creatinine), injury (≥100% rise in serum creatinine), and failure (≥200% rise in serum creatinine). Consistent with the Acute Kidney Injury Network modifications of the original criteria, patients who required dialysis were classified into the RIFLE failure category, irrespective of the percent rise in serum creatinine.[19] The modality for continuous renal replacement therapy used at our institution is continuous slow low-efficiency dialysis (c-SLED), which has been described previously.[20] For patients who did not have an initial creatinine available within 24 hours after ICU admission, the most recent prior creatinine within the previous 48 hours was used.
Statistics
Descriptive data are presented as medians with interquartile ranges for continuous variables and absolute numbers with percentages for categorical variables. Survival of patients with AKI as defined by the RIFLE criteria was estimated by the Kaplan-Meier method. Patients were censored at death or last known follow-up, as determined by the clinical record. Statistical significance was determined by the log-rank test.
The primary end point for logistic regression was death at 60 days after ICU admission. Two separate models were developed, examining AKI as a categorical variable (RIFLE categories) and as a continuous variable (percent increase in creatinine from baseline). The variable “age” was significantly associated in a linear fashion with log odds of death but was dichotomized to provide a more meaningful odds ratio for the reader. Correlated data were assessed by correlation coefficients, and no variables were significantly correlated >0.6. Model reduction was achieved by variable elimination using the likelihood ratio test between nested models. Predictive ability and goodness-of-fit statistics were calculated, and the model was internally validated. No significant interactions were identified in either logistic regression model.
Lastly, a multivariate log linear regression model was developed to assess the relationship of AKI and dialysis with hospital cost. Cost was defined as hospital charges from the provider perspective. Log transformation of “cost” was used to account for skewness and heteroskedasticity. Coefficients in this model were multiplied by a factor of 100 to estimate a percent change in the dependent variable (cost) associated with a unit change in the independent variable.[21]
A two-tailed P < 0.05 was considered statistically significant. No patients were excluded from the analysis because of missing data. Statistical analysis was performed with Stata 10 for Mac (StataCorp, College Station, TX).
Results
The data set included 2,398 patients. Patient characteristics are listed in Table 1. The median age was 59 years. The cohort was predominantly Caucasian (75%) and relatively balanced with respect to gender. The majority of patients on a medical service were admitted to the hospital from the emergency room (76%), compared to only 10% of patients on a surgical service. Sepsis was diagnosed in 23% of patients on a medical service vs. only 4% of patients on a surgical service. This is consistent with the large number of patients at our institution who were admitted to the ICU for routine monitoring after elective surgeries. A significant number of patients had underlying hypertension and diabetes (54% and 18%, respectively). One-third of patients had advanced malignancy by Surveillance, Epidemiology, and End Results (SEER) stage on initial presentation to our institution.
b Included if patient received therapy at any time from ICU admission to date of maximum creatinine.
The absolute number of patients developing AKI or requiring dialysis by hospital service is depicted in Figure 1. The incidence of AKI was higher among patients on a medical vs. a surgical service (21% vs. 6.6%). Patients with hematologic malignancies (leukemia, lymphoma, and myeloma) had the highest incidence of AKI and need for dialysis (28% and 9.3%, respectively). Among patients on a medical service, the odds for developing AKI or requiring dialysis were increased 1.9-fold and 5.4-fold, respectively, for patients with an underlying hematologic malignancy.
Figure 1.
Number of Patients with AKI or Needing Dialysis by Hospital Service
AKI, defined as a minimum 50% increase in serum creatinine from baseline, occurred in 301 patients (12.6%), of whom 56 (2.3%) required dialysis. By further defining AKI by the RIFLE criteria, we classified 6%, 3%, and 4% of patients into the RIFLE risk, injury, and failure categories, respectively. The median elevations in creatinine from baseline were 0.6, 1.1, and 2 mg/dL, respectively. The median time to maximum creatinine was two days for all patients with AKI. There was a stepwise decrease in estimated survival associated with each RIFLE category (Figure 2). Among patients in the RIFLE failure group, the estimated survival was similar between those who required dialysis and those who did not (P = 0.99, log-rank). Although survival for patients requiring dialysis was dismal overall, it was significantly worse for patients with underlying hematological malignancy vs. solid tumor (3% vs. 20%, respectively).
The results of the logistic regression model for predictors of death at 60 days after ICU admission is presented in Table 2. Race and gender were not significant on univariate or multivariate analyses. Although significant on univariate analysis, hematologic malignancy, prior hematopoietic cell transplant (HCT), baseline comorbidities (hypertension, diabetes, heart failure, liver disease), and sepsis were also eliminated during model reduction. After adjusting for the remaining covariates, the RIFLE risk, injury, and failure categories remained significantly associated with 60-day mortality with odds ratios of 2.3, 3.0, and 14, respectively.
| VARIABLE | UNIVARIATE | MULTIVARIATE | |||
|---|---|---|---|---|---|
| OR | P | OR | 95% CI | P | |
| Age ≥55 years | 1.2 | 0.08 | 1.5 | 1.1–1.9 | 0.007 |
| Male vs. female | 0.997 | 0.98 | |||
| Ethnicity | |||||
| Black vs. white | 2.0 | <0.001 | |||
| Hispanic vs. white | 1.1 | 0.39 | |||
| Other vs. white | 0.8 | 0.46 | |||
| Hypertension | 1.3 | 0.02 | |||
| Diabetes | 1.6 | <0.001 | |||
| Heart failure | 2.5 | <0.001 | |||
| Chronic liver disease | 1.8 | 0.02 | |||
| RIFLE category | |||||
| Risk vs. no AKI | 4.1 | <0.001 | 2.3 | 1.5–3.6 | <0.001 |
| Injury vs. no AKI | 8.1 | <0.001 | 3.0 | 1.6–5.8 | 0.001 |
| Failure vs. no AKI | 35 | <0.001 | 14.3 | 7.2–29.0 | <0.001 |
| Amphotericin | 10.9 | <0.001 | 1.9 | 1.1–3.3 | 0.03 |
| Vasopressors | 6.3 | <0.001 | 2.0 | 1.4–2.6 | <0.001 |
| Mechanical ventilation | 2.1 | <0.001 | 1.9 | 1.4–2.5 | <0.001 |
| IV diuretics | 3.8 | <0.001 | 1.4 | 1.1–1.9 | 0.015 |
| Sepsis | 5.7 | <0.001 | |||
| Medical vs. surgical service | 9.9 | <0.001 | 2.2 | 1.5–3.1 | <0.001 |
| Liquid vs. solid tumor | 5.5 | <0.001 | |||
| Prior HCT | |||||
| Autologous | 1.7 | 0.23 | |||
| Allogeneic | 6.0 | <0.001 | |||
| Advanced vs. locoregional stage (SEER) | 4.4 | <0.001 | 2.1 | 1.6–2.6 | <0.001 |
| ER admission | 11.3 | <0.001 | 5.3 | 3.7–7.6 | <0.001 |
| Pre-ICU length of stay | 1.06 | <0.001 | 1.02 | 1.0–1.03 | 0.02 |
Likelihood ratio x2(12) = 818 (P < 0.001), positive predictive value 72%, negative predictive value 88%; area under the receiver operating curve = 0.88, Hosmer-Lemeshow x2(8) = 6.8 (P = 0.56).
OR, odds ratio; AKI, acute kidney injury; HCT, hematopoietic cell transplant; ER, emergency room; ICU, intensive care unit.
To further assess the relationship between serum creatinine and mortality, a separate logistic regression was performed using “percent rise in creatinine” as a continuous predictor variable (Table 3). Need for dialysis was also included as an independent variable. Aside from “percent rise in creatinine” and dialysis, model reduction yielded the same covariates as in the initial model. Dialysis had the largest effect on the odds of 60-day mortality (odds ratio = 6.2). After adjusting for dialysis, “percent rise in creatinine” remained significantly associated with 60-day mortality. For example, a 10% rise in creatinine increased the odds of mortality by 8%. The predictive capabilities of both logistic regression models were similar.
| VARIABLE | OR | 95% CI | P |
|---|---|---|---|
| Age ≥55 years | 1.4 | 1.1–1.9 | <0.001 |
| Percent increase in creatinine | 1.008 | 1.005–1.01 | <0.001 |
| ER admission | 5.4 | 3.8–7.7 | <0.001 |
| Pre-ICU length of stay (days) | 1.02 | 1.00–1.04 | 0.016 |
| SEER stage (distant vs. other) | 2.0 | 1.6–2.7 | <0.001 |
| Medical vs. surgical service | 2.2 | 1.5–3.2 | <0.001 |
| Vasopressors | 2.0 | 1.5–2.7 | <0.001 |
| Mechanical ventilation | 1.8 | 1.4–2.5 | <0.001 |
| Amphotericin | 1.8 | 1.1–3.2 | 0.031 |
| IV diuretics | 1.4 | 1.0–1.8 | 0.024 |
| Dialysis | 6.2 | 2.3–16.5 | <0.001 |
Likelihood ratio x2(11) = 815 (P < 0.001), positive predictive value 72%, negative predictive value 88%, area under the receiver operating curve = 0.88.
OR, odds ratio; ICU, intensive care unit; AKI, acute kidney injury; ER, emergency room.
We included AKI as a continuous variable in a multivariate regression to determine the relationship of AKI and dialysis with hospital cost (Table 4). The model was adjusted for numerous clinical and demographic variables. Age, gender, race, autologous transplant, tumor grade, diabetes, and liver disease were not significant predictors of hospital cost in the final model. The need for dialysis was associated with a 21% increase in hospital cost. Each percent increase in serum creatinine was associated with a 0.16% increase in cost. An interaction was identified between mechanical ventilation and sepsis (25% increase in hospital cost).
| VARIABLE | β | SE | P |
|---|---|---|---|
| Increase in creatinine (per 1%) | 0.00156 | 0.000257 | <0.001 |
| Dialysis | 0.213 | 0.0994 | 0.032 |
| Diuretics | 0.0831 | 0.0180 | <0.001 |
| Mechanical ventilation | 0.561 | 0.0299 | <0.001 |
| Allotransplant | 0.538 | 0.0960 | <0.001 |
| Medical vs. surgical service | 0.259 | 0.0381 | <0.001 |
| Liquid vs. solid tumor | 0.227 | 0.0433 | <0.001 |
| Distant vs. locoregional stage | 0.0717 | 0.0314 | 0.023 |
| Sepsis | 0.151 | 0.0622 | 0.015 |
| ER admission | −0.246 | 0.038 | <0.001 |
| Heart failure | 0.107 | 0.0469 | 0.023 |
| Hypertension | 0.0647 | 0.0271 | 0.017 |
| Mechanical ventilation × sepsis | 0.251 | 0.0853 | 0.003 |
| Constant | 10.8 | 0.0254 | <0.001 |
R2 = 0.32.
Discussion
The incidence of AKI in our study was 12.6% of all patients admitted to the ICU, and there was a progressive decrease in survival associated with worsening kidney injury. This association remained even after adjusting for covariates. AKI and the need for dialysis were also associated with increased hospital costs. To our knowledge, this is the largest single-center study to examine the RIFLE criteria for AKI in a critically-ill population with cancer.
A striking finding in our study is the significant effect that small elevations in serum creatinine may have on survival. An increase of 0.6 mg/dL in the RIFLE risk category increased the odds for mortality by a factor of 2.3 compared to patients without AKI. The median maximum creatinine in this group was only 1.3 mg/dL, which is still within the “normal” range for males in our institution. Criteria that define mild renal toxicity as a serum greater than “1.5 × the upper limit of normal” would exclude a significant number of patients in the RIFLE risk and injury categories, although their risk of mortality was significantly increased.[22] Other criteria that define AKI by glomerular filtration rate (GFR) are also problematic as estimating equations for GFR require serum creatinine to be in steady state. This is a false assumption to make in the setting of AKI, where serum creatinine may fluctuate daily. Serum creatinine is an insensitive marker of renal injury in patients with cancer, and more sensitive and specific biomarkers of AKI are currently under development.[23], [24] and [25] Until these markers are routinely available, renal injury in oncology practice and clinical trials may be better defined as a percentage rise in serum creatinine relative to baseline, similar to the RIFLE criteria.
Out of all variables examined, it is interesting that the need for dialysis had the greatest association with 60-day mortality (Table 3). Although we adjusted for other risk factors, there may still be residual confounding to explain the strong association of dialysis with mortality. However, it is also recognized that dialysis may promote a proinflammatory state[26] and that AKI, in itself, may lead to injury of distant organs via systemic cytokine release.[27] and [28] These deleterious effects may be amplified in patients with cancer, who frequently are neutropenic and have chronic inflammation (e.g. capillary leak syndrome, diffuse alveolar hemorrhage, graft-vs.-host disease). It is known that the need for dialysis after a stem cell transplant is associated with >70% mortality.[29] Although dialysis remains pivotal for volume and metabolic clearance, a true “therapy” for AKI has unfortunately remained elusive thus far.
Our overall incidence of 12.6% for AKI is lower than the reported incidence of 13%–42% in other studies of critically ill patients with cancer.[2], [30] and [31] We excluded patients who had a serum creatinine >1.5 mg/dL on admission to the ICU as we were interested in the development of AKI after ICU admission. This likely excluded patients who already had AKI on presentation, which may have contributed to the lower incidence of AKI and the need for dialysis in our study. Unlike previous studies, our cohort included a large number of patients on a surgical service who were electively admitted to the ICU for routine postoperative care and, therefore, were at lower risk of developing AKI. However, when limited to patients on a medical service, our incidence of 21% is consistent with the results of previous studies of patients in medical ICUs.
The prognosis of patients requiring dialysis was dismal, with an estimated 89% 60-day mortality. This is somewhat higher than the reported mortality of 66%–88% in previous studies.[32], [33], [34], [35] and [36] Given that our institution also serves as a referral cancer center for patients who have had progressive disease on standard therapy, it is possible that our patient population may have been more predisposed to complications from cancer therapy. Patients with hematological malignancies had a higher incidence of AKI and need for dialysis. However, underlying hematological malignancy and HCT were no longer significantly associated with 60-day mortality in the adjusted analysis. Similar to the findings of others, this would suggest that it is not the underlying malignancy itself but rather the complications of treatment and prolonged immunosuppression that lead to decreased survival in these patients.[37] and [38] Early goal-directed intensive life support should be considered for most patients,[39] but continuation of dialysis may not be of benefit, in terms of both survival and cost, in patients with hematologic malignancy who demonstrate minimal improvement.
Our study had certain limitations. Given the retrospective design, we cannot rule out selection bias or residual confounding. We were able to adjust for several variables specific to cancer and critical care as well as pre-ICU length of stay, which may be a surrogate marker for comorbidities and functional status. Nonetheless, our conclusions should be interpreted as hypothesis-generating. Second, our study is based on a single-center experience, which may limit its generalizability. Nonetheless, our study had a large sample size that was subjected to fairly uniform management. Third, we did not have data on end-of-life decisions, which may have impacted mortality and need for dialysis. Lastly, we were unable to obtain cost-to-charge ratios, which may limit the generalizability of our findings to other institutions. However, we reported on percent increases in cost as opposed to absolute dollar figures, which may adjust for some of this variation.
Conclusions
AKI as defined by the RIFLE criteria may be predictive of short-term mortality in critically ill patients with cancer. We have demonstrated that relatively small changes in serum creatinine are associated with higher mortality and that the need for dialysis entails a very poor prognosis. The mechanism behind the increased mortality in patients with hematological malignancies appears to be secondary to the associated complications of therapy, as opposed to the underlying cancer itself. We hypothesize that strategies to prevent the development of AKI and progression to dialysis dependence may improve survival. Whether the prevention of AKI translates to cost savings is also of interest.
References
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Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
IV iron sucrose for cancer and/or chemotherapy-induced anemia in patients treated with erythropoiesis-stimulating agents
Lowell B. Anthony, MD,1 Nashat Y. Gabrail, MD,2 Hassan Ghazal, MD,3, Donald V. Woytowitz, MD,4 Marshall S. Flam, MD,5 Anibal Drelichman, MD,6, David M. Loesch, MD,7, Demi A. Niforos, MS,8, and Antoinette Mangione, MD, PharmD9; for the Iron Sucrose Study Group*
1 Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, New Orleans, LA; 2 Nashat Cancer Center, Canton, OH; 3 Kentucky Cancer Clinic, Hazard, KY; 4 Florida Cancer Specialists, Fort Myers, FL; 5 Hematology/Oncology Group of Fresno, Fresno, CA; 6 Newland Medical Associates, Southfield, MI; 7 Oncology/Hematology Associates, Indianapolis, IN; 8 AAI Pharma, Inc., Natick, MA; and 9 Luitpold Pharmaceuticals/American Regent, Inc., Norristown, PA
Manuscript received January 2, 2011; accepted June 16, 2011.
This work was presented at the 43rd Annual Meeting of the American Society of Clinical Oncology; June 1–5, 2007 in Chicago, IL, and was supported by Luitpold Pharmaceuticals/American Regent, Inc., Shirley, NY.
Correspondence to: Lowell B. Anthony, MD, LSUHSC New Orleans, Ochsner Kenner Medical Center, 200 West Esplanade, Kenner, LA 70065; e-mail: lantho@lsuhsc.edu.
Conflicts of interest: Ms. Niforos was a fulltime salaried employee of AAI Pharma, Inc., contracted to perform all biostatistical services for the clinical trial. Dr. Mangione was a fulltime salaried employee of the trial sponsor, Luitpold Pharmaceuticals/American Regent, Inc. Drs. Anthony, Gabrail, Ghazal, Woytowitz, Flam, Drelichman, and Loesch have nothing to disclose.
Mild-to-moderate anemia occurs in up to 75% of cancer patients undergoing either single- or multimodality therapy and may contribute to an increased morbidity and reduced quality of life (QOL).1–4 This form of anemia resembles anemia of chronic disease, with a blunted erythropoietin response and inadequate erythropoietin production.5 Increasing hemoglobin (Hgb) concentrations and reducing red blood cell (RBC) transfusions while improving QOL and tolerance to cancer therapies are the treatment-related goals.
Intravenous (IV) iron is commonly administered with ESAs in CKD-associated anemia.12,13 Most studies regarding IV iron replacement in cancer and/or chemotherapy-induced anemia (CCIA) are positive, with one exception: Steensma et al14 reported no benefit in adding IV ferric gluconate to an ESA in a phase III randomized trial in which an oral placebo and iron were used as comparators. Practice guidelines are inconsistent, as the National Comprehensive Cancer Network (NCCN) recommends the IV route when iron is prescribed,6 and the American Society of Hematology/ American Society of Clinical Oncology considers the evidence insufficient to support routine IV iron use.15,16 Auerbach et al17 demonstrated that IV iron dextran results in a greater Hgb level increase than oral iron in ESAtreated patients. Approved formulations of IV iron in the United States include iron dextran, iron sucrose, and ferric gluconate, with the majority of published data with iron dextran.15,18,19 However, the iron dextrans have black-box warnings, and test doses are recommended. Henry et al20 reported that IV ferric gluconate significantly increased Hgb response when compared with oral iron or no iron and was well tolerated in CCIA.
Early work with IV iron sucrose includes a trial evaluating 67 lymphoma patients randomized between ESA or ESA with IV iron sucrose.21 Despite adequate bone marrow iron stores, the Hgb response was greater (91% vs 54%) and the time to reach a Hgb level > 2 g/dL was less (6 vs 12 weeks) in the IV iron-treated group.21 Another trial randomized 398 CCIA patients between fixed IV iron doses (mean weekly dose, 64.8 mg) with ESA versus standard practice (2% received IV iron).22 IV iron resulted in a trend toward a higher ferritin level, but transferrin saturation (TSAT) remained similar between the two groups.22 A study in patients with noniron-deficient anemic solid tumors receiving chemotherapy also demonstrated an increase in hemoglobin levels statistically favoring the darbepoetin alfa (Aranesp)/iron group.23 As additional information is needed, this study was performed to determine whether IV iron sucrose combined with ESA increases Hgb levels in CCIA patients who have been previously treated with an ESA.
Patients and methods
Patient eligibility
his was an open-label, phase III, randomized, institutional review board-approved, multicenter study at 56 US centers. After signing informed consent, patients ≥ 18 years of age with a histologic diagnosis of cancer (acute leukemia or myeloproliferative syndrome excluded) receiving ongoing or planned chemotherapy, with a Hgb level ≤ 10.0 g/dL, body weight > 50 kg, and a Karnofsky performance status of ≥ 60%, were eligible. Patients were excluded if they had iron depletion, active infection, myelophthisic bone marrow (except for hematologic malignancy), hypoplastic bone marrow, uncontrolled hypertension, bleeding, or planned surgery. To ensure a stable baseline Hgb value, no IV iron within 2 months of consent or RBC transfusions within 3 weeks of randomization were allowed.
Treatment
After 8 weeks of fixed ESA doses in stage 1, patients were classified as either ESA responders (≥ 1 g/dL Hgb level increase from baseline) or nonresponders (< 1 g/dL Hgb level increase from baseline), with each group separately randomized centrally using block randomization to receive either IV iron sucrose or no iron treatment (Figure 1). At the time of randomization (beginning of stage 2), patients were stratified according to malignancy type (solid tumor vs hematologic) and Hgb level (< 12 g/dL vs ≥ 12 g/dL for ESA responders; < 9.5 g/dL vs ≥ 9.5 g/dL for ESA nonresponders).
The calculated dose of the study drug (iron sucrose [Venofer]; 7 mg/kg up to 500 mg maximum) was added to 500 mL of normal or half-normal saline and administered IV over 4 hours.24 Patients randomized to receive iron sucrose were scheduled to receive up to three infusions at 1- to 3-week intervals during the first 9 weeks of stage 2, with the first dose administered as soon as possible after randomization. The last dose of ESA was given on or before week 12 of stage 2.
Outcome measures
The primary endpoint for efficacy was the change from baseline (end of stage 1) to the maximum Hgb level achieved during stage 2 in patients who responded to ESA. Major secondary endpoints included changes in Hgb levels when iron sucrose was added to ESA nonresponders as well as the percentage of all randomized patients with Hgb level increases > 1 g/dL, > 2 g/dL, and > 3 g/dL; changes in Hgb levels and iron indices from baseline at each visit; and changes in the 13-item Functional Assessment of Chronic Illness Therapy (FACIT) fatigue scale. Hgb levels were obtained weekly, and iron indices were measured every 3 weeks. The FACIT fatigue scale was measured during stage 1 at consent, weeks 4, and 8 and during stage 2 weeks 3, 6, 9, and at the end of the study.
Adverse events were recorded hourly during iron sucrose administration and from the day of randomization through study completion or 30 days following the last dose of study drug, whichever was later. Investigators provided the date of onset, severity, relationship, date of resolution, action taken, and adverse event outcome. Adverse drug events were events considered by the investigator to be possibly, probably, or definitely related to the study drug.
Statistical method
The sample size was based on the hypothesis that iron-treated ESA responders (group A) would have a 1.0 g/dL or higher mean increase in Hgb levels than would ESA responders who did not receive iron (group B). The standard deviation (SD) of the difference was assumed to be ≤ 1.5 g/dL. Targeting a 1.0 g/dL change in Hgb level to be significant, 49 patients/ group were required (alpha = 0.05; beta = 0.10). Assuming that the ESA response rate in stage I was at least 40% and that the stage I and stage 2 dropout rates were no more than 10% and 25%, respectively, 325 patients were the targeted number for stage I enrollment, with adjustments made by monitoring the stage I response rate.
The intent-to-treat (ITT) population included patients randomized into stage 2 based on actual treatment. The evaluable population included ITT patients who completed at least 10 weeks of stage 2 or who had interventions (RBC transfusions or nonstudy iron) prior to week 10.
Continuous variables were assessed using analysis of covariance and t-tests. Ordinal responses were analyzed with the Fisher’s exact test and Cochran-Mantel-Haenszel statistics. Changes from baseline to each visit for all FACIT scores were assessed for treatment groups with the unpaired two-sample t-test.
Results
Patient disposition and demographics
Of the 375 patients enrolled during the run-in stage 1 period (between July 2003 and October 2005), 132 patients discontinued treatment (the most common reasons were a required intervention [50], withdrawn consent [23], and adverse events [17]). Fourteen patients completed stage 1 but did not enter stage 2. Figure 2 shows the numbers of patients who were randomly assigned to the two treatment groups and were evaluated for safety and efficacy as well as reasons for study discontinuation. Table 1 shows the patient numbers assigned to the various treatment groups (A to D) based on ESA response in stage I and the study population; it also demonstrates the similar baseline demographic characteristics between the treatment groups. At baseline (ie, prior to randomization), there were no statistically significant differences in Hgb level, TSAT, and ferritin level between the ESA responders (A vs B) and nonresponders (C vs D).
Efficacy of iron sucrose
Mean maximum improvement in Hgb levels (Table 2). Among ESA responders (groups A and B), a statistically significantly greater mean maximum Hgb level increase was observed among patients who received iron sucrose (group A) than among those who did not (group B), achieving the primary endpoint (ITT, P = 0.004; evaluable, P = 0.008). A statistically significant greater increase in the mean maximum Hgb level was observed following iron sucrose (groups A and C) when compared with no iron treatment (groups B and D), regardless of prior ESA response. In the ESA nonresponder group, a significant increase (P = 0.027) in the mean maximum Hgb level was observed between those who received iron sucrose (group C) and those who did not (group D) in the ITT population; a statistical difference was not seen in the evaluable population (P = 0.082).
With regard to tumor subtypes, breast cancer and other tumor types, but not lung cancer, were associated with statistically significant increases in maximum Hgb levels following iron sucrose, regardless of prior ESA response.
Absolute increases in Hgb levels (Table 2). A greater proportion of patients assigned to IV iron sucrose achieved a ≥ 2 g/dL and ≥ 3 g/dL increase in Hgb level during the study than did those who did not receive iron. These differences were statistically significant for all the groups except for the evaluable ≥ 3 g/dL nonresponder group. The only statistically significant difference in the proportion achieving a ≥ 1 g/dL Hgb level increase occurred in the ESA nonresponder groups. In addition, baseline hematologic characteristics and iron indices did not predict the efficacy of IV iron treatment (as defined by a > 1 g/dL or > 2 g/dL increase in Hgb level). In the IV iron sucrose-treated group, there was no statistical difference in these baseline characteristics in the patients who demonstrated a > 1 g/dL (data not shown) or a > 2 g/dL treatment response to IV iron.
Changes from baseline in Hgb and ferritin levels and in TSAT. Figure 3 summarizes the Hgb level, ferritin level, and TSAT responses by study visit after IV iron sucrose compared with no iron in the ITT population. Between treatment groups, statistically significant differences (P < 0.05) were present by weeks 7, 3, and 13 for Hgb level, ferritin level, and TSAT, respectively. At the end of the study, week 13, the mean Hgb level increase from baseline was 2.3 g/dL versus 1.2 g/dL (P < 0.002), the mean ferritin level increase from baseline was 419 ng/mL versus a decrease of 50 ng/mL (P < 0.001), and the mean TSAT increase from baseline was 8.8% versus 0.2% (P < 0.005) in the iron sucrose versus no iron group.
Changes in fatigue levels (FACIT fatigue scale). There was a statistically significant decrease in the level of fatigue at the end of the study compared with at baseline (end of stage 1) in the iron sucrose-treated patients in the ITT but not in the evaluable population (–3.3 iron sucrose/–2.1 no iron, P = 0.022 ITT; –3.0 iron sucrose/–1.7 no iron, P = 0.058 evaluable population). No significant decrease in the level of fatigue was experienced by the patients who received no iron. There were no statistically significant differences between the groups in changes from baseline at each visit..
Safety of iron sucrose
Extent of exposure. In the ITT population, the mean per patient total dose of iron sucrose administered was 1,123 (SD, 402) mg in group A (responders) and 1,113 (SD, 387) mg in group C (nonresponders).
Adverse drug events (ADEs). All safety analyses were performed using the ITT population. Serious ADEs were experienced by three patients in the iron sucrose group (chest pain, hypersensitivity, and hypotension, one patient each) and by no patients in the ESA-only group. One ESA-only group patient (arthralgia) and four iron sucrose patients (hypersensitivity; abdominal pain; arthralgia and muscle cramps; myalgia, nausea, and vomiting) were prematurely discontinued from the study drug due to the occurrence of an ADE.
At least one ADE was experienced by 37.4% of the patients in the iron sucrose group and 0.8% in the control group. The most common (³ 5%) ADEs were nausea (8.1%), dysgeusia (8.1%), back pain (6.1%), arthralgia (6.1%), muscle cramp (6.1%), and peripheral edema (5.1%). Within the ESA-only group, the only ADE reported was hypertension (one subject, 0.8%).
Eleven grade 3 (National Institutes of Health/National Cancer Institute– Common Terminology Criteria, version 2.0) ADEs occurred in iron sucrose-treated patients and included nausea (2.0%), hypotension (2.0%), abdominal pain (1.0%), chest pain (1.0%), hypersensitivity (1.0%), arthralgia (1.0%), dizziness (1.0%), dyspnea (1.0%), and hypertension (1.0%). A serious grade 3 hypotensive event occurred in a 49-year-old woman weighing 50 kg who experienced dizziness, nausea, vomiting, and transient hypotension (110/60 mm Hg to 70/40 mm Hg) after her first iron sucrose dose of 375 mg. Ninety minutes later, following IV steroids, iron sucrose was restarted and the hypotension recurred. The patient received two subsequent lower iron sucrose doses (200 mg over 4 hours), with no further adverse reactions.
Deaths and thrombotic events. These events are summarized in Table 3. None of these events was judged by the investigators to be related to the study drug.
Laboratory results. Statistically greater mean increases in ferritin levels, TSAT, Hgb levels, hematocrit, mean corpuscular hemoglobin, mean corpuscular volume, and monocytes oc curred in the iron sucrose-treated group. There were no significant differences between treatment groups in clinical chemistry safety laboratory results.
Discussion
This study is the first to evaluate IV iron in CCIA patients who have received prior ESA therapy. IV iron sucrose administered with ESAs significantly increased Hgb levels in CCIA patients. Prior ESA response did not predict Hgb level response to iron sucrose, as benefit was demonstrated in both ESA responders and nonresponders. Baseline hematologic/ iron indices also did not predict IV iron responsiveness, as these characteristics were similar in IV iron responders and nonresponders. Improvement in QOL, as measured by fatigue levels at study completion, was also observed after IV iron but not in the no iron group. IV iron studies are commonly open-label because of the difficulty in blinding iron’s viscous dark-colored solution.
This study design limits the significance of QOL measurements in IV iron studies, where primary endpoints are typically objective measurements. Even though transfusion rates were lower in the IV iron groups (5.1% in groups A and C [A = 1.7%; C = 10%]) than in the no iron groups (10.4% in groups B and D [B = 2.6%; D = 22.9%]), this difference was not statistically significant (Fisher’s exact test, P = 0.215). Our findings support the prior observations that IV iron replacement in combination with ESAs effectively increases Hgb levels and is safe.17,20,21,25,26
Combining IV iron with ESA increases the Hgb level response and may either shorten the time to response and/or decrease the ESA requirement. Approximately 30%–50% of patients are nonresponders after 12–24 weeks of ESA therapy.8,9,17,27,28 Iron deficiency may be a major factor accounting for ESA resistance. Decreased ESA responsiveness in the dialysis population can be corrected by providing adequate iron supplementation. 11,18 Also, ESA nonresponders may become responders with IV iron replacement while continuing the ESA. ESA treatment in responders can produce a functional iron deficiency, because the ESA produces a rapid initiation of erythropoiesis. Inducing functional iron deficiency with ESA therapy implies that the iron supply to the erythron may be the rate-limiting step in erythropoiesis, and the IV iron dose may be important.25 As ESA responders and nonresponders experienced improvement in Hgb levels with IV iron therapy in this trial, IV iron supplementation may be required to achieve and/or maintain a response to ESA therapy.
Iron available for erythropoiesis is derived from the balance between dietary sources and that in the usable pool within the reticuloendothelial system.29 ESA therapy can result in RBC production that exceeds the rate of iron mobilization, even with adequate iron stores. Inflammatory cytokines may also hinder the release of stored iron from macrophages by inducing hepcidin and thus further contribute to an inadequate rate of RBC production.30–34
Of note, baseline ferritin levels were higher in the ESA nonresponders (groups C and D) than in the ESA responders (groups A and B), although these differences were not statistically significant. This finding may be consistent with elevated inflammatory cytokines impairing the availability of iron, leading to a failed ESA response. ESA resistance is multifactorial, with these factors contributing to the rapid depletion of the usable iron pool, thus blunting the ESA response. Identifying factors that allow for maximizing ESA therapy in CCIA patients may result in greater ESA efficiency. The IV route of iron replacement is superior to oral administration and accounts for one of these variables.17,21,25,26
Safely administering IV iron is an important factor that influences the choice of iron preparations. In the United States, the only IV iron indicated for iron deficiency anemia is iron dextran. The risk of allergic reactions and the need for test doses may account for practitioners limiting the use of iron dextran, despite a compelling medical need for rapid, reliable, and safe replenishment of body iron in populations such as those with CKD35–37 and CCIA. The non–dextran- containing IV irons (iron sucrose, ferric gluconate) are currently only FDA approved for CKD indications at doses of 100–200 mg over 2–5 minutes or up to 400 mg over 2.5 hours for iron sucrose and only 125 mg over 10 minutes for ferric gluconate. 18,19
This study supports other findings that IV iron sucrose is generally well tolerated at doses of 7 mg/kg, up to a maximum of 500 mg over 4 hours, in CCIA. Caution should be exercised, however, especially in patients with a lower body weight. This concern is supported by a study of iron sucrose in nondialysis CKD, where hypotension occurred in two patients < 65 kg after 500 mg doses were administered over 4 hours.38
Conclusion
This study’s primary objective was to determine whether prior response to ESA treatment would influence response to IV iron, not to detect differences between functional and absolute iron deficiency. Our findings support that administration of IV iron while continuing ESA treatment may correct functional, as well as absolute, iron deficiency in CCIA. Baseline iron indices did not predict responsiveness to iron sucrose. Without additional data identifying predictors of ESA responsiveness in CCIA, a more proactive approach that includes IV iron may be warranted, as in CKDrelated anemia. As a better understanding of functional iron deficiency evolves, it is becoming apparent that IV iron is important to optimize the response to ESAs for CCIA. Additional studies are needed to understand the mechanisms responsible for functional iron deficiency in CCIA and to assist in identifying the optimal IV iron administration schedule.
Acknowledgments: The authors wish to thank the study coordinators; the patients at each of the participating centers; and Drs. Perry Rigby and Robert Means, for reviewing the manuscript.
*Additional members of the Iron Sucrose Study Group include Ali Ben-Jacob, MD, Cache Valley Cancer Treatment and Research Clinic, Inc., Logan, UT; Amol Rakkar, MD, Hope Center, Terre Haute, IN; Philip Chatham, MD, Granada Hills, CA; Ahmed Maqbool, MD, Welborn Clinic, Research Center, Evansville, IN; Timothy Pluard, MD, Washington University, Medical Oncology, St. Peters, MO; Nafisa Burhani, MD, Joliet Oncology- Hematology Associates, LTD, Joliet, IL; David Henry, MD, Pennsylvania Hematology and Oncology Associates, Philadelphia, PA; David Watkins, MD, Allison Cancer Center, Midland, TX; Howard Ozer, MD, University of Oklahoma Health Science Center-Hematology Oncology Section, Oklahoma City, OK; Leo Orr, MD, Leo E. Orr, Inc., Los Angeles, CA; Billy Clowney, MD, Santee Hematology Oncology, Sumter, SC, Rene Rothestein-Rubin, MD, Rittenhouse Hematology/ Oncology, Philadelphia, PA; Peter Eisenberg, MD, California Cancer Care, Greenbrae, CA; Rosalba Rodriguez, MD, Chula Vista, CA; Kumar Kapisthalam, MD, United Professional Center, Pasco Hernando Oncology, New Port Richey, FL; Jennifer Caskey, MD, Wheat Ridge, CO; Sayed E. Ahmend, MD, Sebring, FL; Patricia Braly, MD, Hematology and Oncology Specialties, New Orleans, LA; Donald Flemming, MD, Medical Center of Vincennes, The Bierhaus Center, Vincennes, IN; William Tester, MD, Albert Einstein Cancer Center, Philadelphia, PA; William Solomon, MD, SUNY Downstate Medical Center, Brooklyn, NY; Mark Hancock, MD, Mile Hile Oncology, Denver, CO; Youssef Hanna, MD, Huron Medical Center, Port Huron, MI; Scot Sorensen, MD, Prairie View Clinic, Lincoln, NE; and Mark Yoffe, MD, Raleigh, NC.
References
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8. Demetri GD, Kris M, Wade J, Degos L, Cella D. Quality-of-life benefit in chemotherapy patients treated with epoetin alfa is independent of disease response or tumor type: results from a prospective community oncology study. Procrit Study Group. J Clin Oncol 1998;16:3412–3425.
9. Gabrilove JL, Cleeland CS, Livingston RB, Sarokhan B, Winer E, Einhorn LH. Clinical evaluation of once-weekly dosing of epoetin alfa in chemotherapy patients: improvements in hemoglobin and quality of life are similar to three-times-weekly dosing. J Clin Oncol 2001;19:2875–2882.
10. Glaspy J, Bukowski R, Steinberg D, Taylor C, Tchekmedyian S, Vadhan-Raj S. Impact of therapy with epoetin alfa on clinical outcomes in patients with nonmyeloid malignancies during cancer chemotherapy in community oncology practice. Procrit Study Group. J Clin Oncol 1997;15:1218–1234.
11. Drüeke TB, Bárány P, Cazzola M, et al. Management of iron deficiency in renal anemia: guidelines for the optimal therapeutic approach in erythropoietin-treated patients. Clin Nephrol 1997;48:1–8.
12. Fishbane S, Frei GL, Maesaka J. Reduction in recombinant human erythropoietin doses by the use of chronic intravenous iron supplementation. Am J Kidney Dis 1995;26:41–46.
13. Van Wyck DB, Roppolo M, Martinez CO, et al. A randomized, controlled trial comparing IV iron sucrose to oral iron in anemic patients with nondialysis-dependent CKD. Kidney Int 2005;68:2846–2856.
14. Steensma DP, Sloan JA, Dakhil SR, et al. Phase III, randomized study of the effects of parenteral iron, oral iron, or no iron supplementation on the erythropoietic re sponse to darbepoetin alfa for patients with chemotherapy-associated anemia. J Clin Oncol 2011;29:97–105.
15. Auerbach M, Ballard H, Glaspy J. Clinical update: intravenous iron for anaemia. Lancet 2007;369:1502–1504.
16. Bokemeyer C, Aapro MS, Courdi A, et al. EORTC guidelines for the use of erythropoietic proteins in anaemic patients with cancer: 2006 update. Eur J Cancer 2007;43:258– 270.
17. Auerbach M, Ballard H, Trout JR, et al. Intravenous iron optimizes the response to recombinant human erythropoietin in cancer patients with chemotherapy-related anemia: a multicenter, open-label, randomized trial. J Clin Oncol 2004;22:1301–1307.
18. Aronoff GR, Bennett WM, Blumenthal S, et al. Iron sucrose in hemodialysis patients: safety of replacement and maintenance regimens. Kidney Int 2004;66:1193–1198.
19. Faich G, Strobos J. Sodium ferric gluconate complex in sucrose: safer intravenous iron therapy than iron dextrans. Am J Kidney Dis 1999;33:464–470.
20. Henry DH, Dahl NV, Auerbach M, Tchekmedyian S, Laufman LR. Intravenous ferric gluconate significantly improves response to epoetin alfa versus oral iron or no iron in anemic patients with cancer receiving chemotherapy. Oncologist 2007;12:231–242.
21. Hedenus M, Birgegård G, Näsman P, et al. Addition of intravenous iron to epoetin beta increases hemoglobin response and decreases epoetin dose requirement in anemic patients with lymphoproliferative malignancies: a randomized multicenter study. Leukemia 2007;21:627–632.
22. Bastit L, Vandebroek A, Altintas S, et al. Randomized, multicenter, controlled trial comparing the efficacy and safety of darbepoetin alpha administered every 3 weeks with or without intravenous iron in patients with chemotherapy-induced anemia. J Clin Oncol 2008;26:1611–1618.
23. Pedrazzoli P, Farris A, Del Prete S, et al. Randomized trial of intravenous iron supplementation in patients with chemotherapy- related anemia without iron deficiency treated with darbepoetin alpha. J Clin Oncol 2008;26:1619–1625.
24. Chandler G, Harchowal J, Macdougall IC. Intravenous iron sucrose: establishing a safe dose. Am J Kidney Dis 2001;38:988–991.
25. Lerchenmueller C, Husseini F, Gaede B, Mossman T, Suto T, Vanderbroek A. Intravenous (IV) iron supplementation in patients with chemotherapy-induced anemia (CIA) receiving darbepoetin alfa every 3 weeks (q3w): iron parameters in a randomized controlled trial. Blood 2006;108:1552.
26. Pinter T, Mossman T, Suto T, Vansteenkiste J. Effects of intravenous iron supplementation on responses to every-3-week darbepoetin alfa by baseline hemoglobin in patients with chemotherapy-induced anemia. J Clin Oncol 2007;25(18S):9106.
27. Glaspy J, Jadeja JS, Justice G, et al. A dose-finding and safety study of novel erythropoiesis stimulating protein (NESP) for the treatment of anaemia in patients receiving multicycle chemotherapy. Br J Cancer 2001;84(suppl 1):17–23.
28. Littlewood TJ, Bajetta E, Nortier JW, Vercammen E, Rapoport B; Epoetin Alfa Study Group. Effects of epoetin alfa on hematologic parameters and quality of life in cancer patients receiving nonplatinum chemotherapy: results of a randomized, double-blind, placebocontrolled trial. J Clin Oncol 2001;19:2865– 2874.
29. Henry DH. Supplemental iron: a key to optimizing the response of cancer-related anemia to rHuEPO? Oncologist 1998;3:275–278. 30. Ganz T. Hepcidin—a regulator of intestinal iron absorption and iron recycling by macrophages. Best Pract ClinHaematol 2005;18:171–182.
31. Ganz T. Hepcidin—a peptide hormone at the interface of innate immunity and iron metabolism. Curr Top Microbiol Immunol 2006;306:183–198.
32. Viatte L, Nicolas G, Lou DQ, et al. Chronic hepcidin induction causes hyposideremia and alters the pattern of cellular iron accumulation in hemochromatotic mice. Blood 2006;107:2952–2958.
33. Weinstein DA, Roy CN, Fleming MD, Loda MF, Wolfsdorf JI, Andrews NC. Inappropriate expression of hepcidin is associated with iron refractory anemia: implications for the anemia of chronic disease. Blood 2002;100:3776–3781.
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Lowell B. Anthony, MD,1 Nashat Y. Gabrail, MD,2 Hassan Ghazal, MD,3, Donald V. Woytowitz, MD,4 Marshall S. Flam, MD,5 Anibal Drelichman, MD,6, David M. Loesch, MD,7, Demi A. Niforos, MS,8, and Antoinette Mangione, MD, PharmD9; for the Iron Sucrose Study Group*
1 Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, New Orleans, LA; 2 Nashat Cancer Center, Canton, OH; 3 Kentucky Cancer Clinic, Hazard, KY; 4 Florida Cancer Specialists, Fort Myers, FL; 5 Hematology/Oncology Group of Fresno, Fresno, CA; 6 Newland Medical Associates, Southfield, MI; 7 Oncology/Hematology Associates, Indianapolis, IN; 8 AAI Pharma, Inc., Natick, MA; and 9 Luitpold Pharmaceuticals/American Regent, Inc., Norristown, PA
Manuscript received January 2, 2011; accepted June 16, 2011.
This work was presented at the 43rd Annual Meeting of the American Society of Clinical Oncology; June 1–5, 2007 in Chicago, IL, and was supported by Luitpold Pharmaceuticals/American Regent, Inc., Shirley, NY.
Correspondence to: Lowell B. Anthony, MD, LSUHSC New Orleans, Ochsner Kenner Medical Center, 200 West Esplanade, Kenner, LA 70065; e-mail: lantho@lsuhsc.edu.
Conflicts of interest: Ms. Niforos was a fulltime salaried employee of AAI Pharma, Inc., contracted to perform all biostatistical services for the clinical trial. Dr. Mangione was a fulltime salaried employee of the trial sponsor, Luitpold Pharmaceuticals/American Regent, Inc. Drs. Anthony, Gabrail, Ghazal, Woytowitz, Flam, Drelichman, and Loesch have nothing to disclose.
Mild-to-moderate anemia occurs in up to 75% of cancer patients undergoing either single- or multimodality therapy and may contribute to an increased morbidity and reduced quality of life (QOL).1–4 This form of anemia resembles anemia of chronic disease, with a blunted erythropoietin response and inadequate erythropoietin production.5 Increasing hemoglobin (Hgb) concentrations and reducing red blood cell (RBC) transfusions while improving QOL and tolerance to cancer therapies are the treatment-related goals.
Intravenous (IV) iron is commonly administered with ESAs in CKD-associated anemia.12,13 Most studies regarding IV iron replacement in cancer and/or chemotherapy-induced anemia (CCIA) are positive, with one exception: Steensma et al14 reported no benefit in adding IV ferric gluconate to an ESA in a phase III randomized trial in which an oral placebo and iron were used as comparators. Practice guidelines are inconsistent, as the National Comprehensive Cancer Network (NCCN) recommends the IV route when iron is prescribed,6 and the American Society of Hematology/ American Society of Clinical Oncology considers the evidence insufficient to support routine IV iron use.15,16 Auerbach et al17 demonstrated that IV iron dextran results in a greater Hgb level increase than oral iron in ESAtreated patients. Approved formulations of IV iron in the United States include iron dextran, iron sucrose, and ferric gluconate, with the majority of published data with iron dextran.15,18,19 However, the iron dextrans have black-box warnings, and test doses are recommended. Henry et al20 reported that IV ferric gluconate significantly increased Hgb response when compared with oral iron or no iron and was well tolerated in CCIA.
Early work with IV iron sucrose includes a trial evaluating 67 lymphoma patients randomized between ESA or ESA with IV iron sucrose.21 Despite adequate bone marrow iron stores, the Hgb response was greater (91% vs 54%) and the time to reach a Hgb level > 2 g/dL was less (6 vs 12 weeks) in the IV iron-treated group.21 Another trial randomized 398 CCIA patients between fixed IV iron doses (mean weekly dose, 64.8 mg) with ESA versus standard practice (2% received IV iron).22 IV iron resulted in a trend toward a higher ferritin level, but transferrin saturation (TSAT) remained similar between the two groups.22 A study in patients with noniron-deficient anemic solid tumors receiving chemotherapy also demonstrated an increase in hemoglobin levels statistically favoring the darbepoetin alfa (Aranesp)/iron group.23 As additional information is needed, this study was performed to determine whether IV iron sucrose combined with ESA increases Hgb levels in CCIA patients who have been previously treated with an ESA.
Patients and methods
Patient eligibility
his was an open-label, phase III, randomized, institutional review board-approved, multicenter study at 56 US centers. After signing informed consent, patients ≥ 18 years of age with a histologic diagnosis of cancer (acute leukemia or myeloproliferative syndrome excluded) receiving ongoing or planned chemotherapy, with a Hgb level ≤ 10.0 g/dL, body weight > 50 kg, and a Karnofsky performance status of ≥ 60%, were eligible. Patients were excluded if they had iron depletion, active infection, myelophthisic bone marrow (except for hematologic malignancy), hypoplastic bone marrow, uncontrolled hypertension, bleeding, or planned surgery. To ensure a stable baseline Hgb value, no IV iron within 2 months of consent or RBC transfusions within 3 weeks of randomization were allowed.
Treatment
After 8 weeks of fixed ESA doses in stage 1, patients were classified as either ESA responders (≥ 1 g/dL Hgb level increase from baseline) or nonresponders (< 1 g/dL Hgb level increase from baseline), with each group separately randomized centrally using block randomization to receive either IV iron sucrose or no iron treatment (Figure 1). At the time of randomization (beginning of stage 2), patients were stratified according to malignancy type (solid tumor vs hematologic) and Hgb level (< 12 g/dL vs ≥ 12 g/dL for ESA responders; < 9.5 g/dL vs ≥ 9.5 g/dL for ESA nonresponders).
The calculated dose of the study drug (iron sucrose [Venofer]; 7 mg/kg up to 500 mg maximum) was added to 500 mL of normal or half-normal saline and administered IV over 4 hours.24 Patients randomized to receive iron sucrose were scheduled to receive up to three infusions at 1- to 3-week intervals during the first 9 weeks of stage 2, with the first dose administered as soon as possible after randomization. The last dose of ESA was given on or before week 12 of stage 2.
Outcome measures
The primary endpoint for efficacy was the change from baseline (end of stage 1) to the maximum Hgb level achieved during stage 2 in patients who responded to ESA. Major secondary endpoints included changes in Hgb levels when iron sucrose was added to ESA nonresponders as well as the percentage of all randomized patients with Hgb level increases > 1 g/dL, > 2 g/dL, and > 3 g/dL; changes in Hgb levels and iron indices from baseline at each visit; and changes in the 13-item Functional Assessment of Chronic Illness Therapy (FACIT) fatigue scale. Hgb levels were obtained weekly, and iron indices were measured every 3 weeks. The FACIT fatigue scale was measured during stage 1 at consent, weeks 4, and 8 and during stage 2 weeks 3, 6, 9, and at the end of the study.
Adverse events were recorded hourly during iron sucrose administration and from the day of randomization through study completion or 30 days following the last dose of study drug, whichever was later. Investigators provided the date of onset, severity, relationship, date of resolution, action taken, and adverse event outcome. Adverse drug events were events considered by the investigator to be possibly, probably, or definitely related to the study drug.
Statistical method
The sample size was based on the hypothesis that iron-treated ESA responders (group A) would have a 1.0 g/dL or higher mean increase in Hgb levels than would ESA responders who did not receive iron (group B). The standard deviation (SD) of the difference was assumed to be ≤ 1.5 g/dL. Targeting a 1.0 g/dL change in Hgb level to be significant, 49 patients/ group were required (alpha = 0.05; beta = 0.10). Assuming that the ESA response rate in stage I was at least 40% and that the stage I and stage 2 dropout rates were no more than 10% and 25%, respectively, 325 patients were the targeted number for stage I enrollment, with adjustments made by monitoring the stage I response rate.
The intent-to-treat (ITT) population included patients randomized into stage 2 based on actual treatment. The evaluable population included ITT patients who completed at least 10 weeks of stage 2 or who had interventions (RBC transfusions or nonstudy iron) prior to week 10.
Continuous variables were assessed using analysis of covariance and t-tests. Ordinal responses were analyzed with the Fisher’s exact test and Cochran-Mantel-Haenszel statistics. Changes from baseline to each visit for all FACIT scores were assessed for treatment groups with the unpaired two-sample t-test.
Results
Patient disposition and demographics
Of the 375 patients enrolled during the run-in stage 1 period (between July 2003 and October 2005), 132 patients discontinued treatment (the most common reasons were a required intervention [50], withdrawn consent [23], and adverse events [17]). Fourteen patients completed stage 1 but did not enter stage 2. Figure 2 shows the numbers of patients who were randomly assigned to the two treatment groups and were evaluated for safety and efficacy as well as reasons for study discontinuation. Table 1 shows the patient numbers assigned to the various treatment groups (A to D) based on ESA response in stage I and the study population; it also demonstrates the similar baseline demographic characteristics between the treatment groups. At baseline (ie, prior to randomization), there were no statistically significant differences in Hgb level, TSAT, and ferritin level between the ESA responders (A vs B) and nonresponders (C vs D).
Efficacy of iron sucrose
Mean maximum improvement in Hgb levels (Table 2). Among ESA responders (groups A and B), a statistically significantly greater mean maximum Hgb level increase was observed among patients who received iron sucrose (group A) than among those who did not (group B), achieving the primary endpoint (ITT, P = 0.004; evaluable, P = 0.008). A statistically significant greater increase in the mean maximum Hgb level was observed following iron sucrose (groups A and C) when compared with no iron treatment (groups B and D), regardless of prior ESA response. In the ESA nonresponder group, a significant increase (P = 0.027) in the mean maximum Hgb level was observed between those who received iron sucrose (group C) and those who did not (group D) in the ITT population; a statistical difference was not seen in the evaluable population (P = 0.082).
With regard to tumor subtypes, breast cancer and other tumor types, but not lung cancer, were associated with statistically significant increases in maximum Hgb levels following iron sucrose, regardless of prior ESA response.
Absolute increases in Hgb levels (Table 2). A greater proportion of patients assigned to IV iron sucrose achieved a ≥ 2 g/dL and ≥ 3 g/dL increase in Hgb level during the study than did those who did not receive iron. These differences were statistically significant for all the groups except for the evaluable ≥ 3 g/dL nonresponder group. The only statistically significant difference in the proportion achieving a ≥ 1 g/dL Hgb level increase occurred in the ESA nonresponder groups. In addition, baseline hematologic characteristics and iron indices did not predict the efficacy of IV iron treatment (as defined by a > 1 g/dL or > 2 g/dL increase in Hgb level). In the IV iron sucrose-treated group, there was no statistical difference in these baseline characteristics in the patients who demonstrated a > 1 g/dL (data not shown) or a > 2 g/dL treatment response to IV iron.
Changes from baseline in Hgb and ferritin levels and in TSAT. Figure 3 summarizes the Hgb level, ferritin level, and TSAT responses by study visit after IV iron sucrose compared with no iron in the ITT population. Between treatment groups, statistically significant differences (P < 0.05) were present by weeks 7, 3, and 13 for Hgb level, ferritin level, and TSAT, respectively. At the end of the study, week 13, the mean Hgb level increase from baseline was 2.3 g/dL versus 1.2 g/dL (P < 0.002), the mean ferritin level increase from baseline was 419 ng/mL versus a decrease of 50 ng/mL (P < 0.001), and the mean TSAT increase from baseline was 8.8% versus 0.2% (P < 0.005) in the iron sucrose versus no iron group.
Changes in fatigue levels (FACIT fatigue scale). There was a statistically significant decrease in the level of fatigue at the end of the study compared with at baseline (end of stage 1) in the iron sucrose-treated patients in the ITT but not in the evaluable population (–3.3 iron sucrose/–2.1 no iron, P = 0.022 ITT; –3.0 iron sucrose/–1.7 no iron, P = 0.058 evaluable population). No significant decrease in the level of fatigue was experienced by the patients who received no iron. There were no statistically significant differences between the groups in changes from baseline at each visit..
Safety of iron sucrose
Extent of exposure. In the ITT population, the mean per patient total dose of iron sucrose administered was 1,123 (SD, 402) mg in group A (responders) and 1,113 (SD, 387) mg in group C (nonresponders).
Adverse drug events (ADEs). All safety analyses were performed using the ITT population. Serious ADEs were experienced by three patients in the iron sucrose group (chest pain, hypersensitivity, and hypotension, one patient each) and by no patients in the ESA-only group. One ESA-only group patient (arthralgia) and four iron sucrose patients (hypersensitivity; abdominal pain; arthralgia and muscle cramps; myalgia, nausea, and vomiting) were prematurely discontinued from the study drug due to the occurrence of an ADE.
At least one ADE was experienced by 37.4% of the patients in the iron sucrose group and 0.8% in the control group. The most common (³ 5%) ADEs were nausea (8.1%), dysgeusia (8.1%), back pain (6.1%), arthralgia (6.1%), muscle cramp (6.1%), and peripheral edema (5.1%). Within the ESA-only group, the only ADE reported was hypertension (one subject, 0.8%).
Eleven grade 3 (National Institutes of Health/National Cancer Institute– Common Terminology Criteria, version 2.0) ADEs occurred in iron sucrose-treated patients and included nausea (2.0%), hypotension (2.0%), abdominal pain (1.0%), chest pain (1.0%), hypersensitivity (1.0%), arthralgia (1.0%), dizziness (1.0%), dyspnea (1.0%), and hypertension (1.0%). A serious grade 3 hypotensive event occurred in a 49-year-old woman weighing 50 kg who experienced dizziness, nausea, vomiting, and transient hypotension (110/60 mm Hg to 70/40 mm Hg) after her first iron sucrose dose of 375 mg. Ninety minutes later, following IV steroids, iron sucrose was restarted and the hypotension recurred. The patient received two subsequent lower iron sucrose doses (200 mg over 4 hours), with no further adverse reactions.
Deaths and thrombotic events. These events are summarized in Table 3. None of these events was judged by the investigators to be related to the study drug.
Laboratory results. Statistically greater mean increases in ferritin levels, TSAT, Hgb levels, hematocrit, mean corpuscular hemoglobin, mean corpuscular volume, and monocytes oc curred in the iron sucrose-treated group. There were no significant differences between treatment groups in clinical chemistry safety laboratory results.
Discussion
This study is the first to evaluate IV iron in CCIA patients who have received prior ESA therapy. IV iron sucrose administered with ESAs significantly increased Hgb levels in CCIA patients. Prior ESA response did not predict Hgb level response to iron sucrose, as benefit was demonstrated in both ESA responders and nonresponders. Baseline hematologic/ iron indices also did not predict IV iron responsiveness, as these characteristics were similar in IV iron responders and nonresponders. Improvement in QOL, as measured by fatigue levels at study completion, was also observed after IV iron but not in the no iron group. IV iron studies are commonly open-label because of the difficulty in blinding iron’s viscous dark-colored solution.
This study design limits the significance of QOL measurements in IV iron studies, where primary endpoints are typically objective measurements. Even though transfusion rates were lower in the IV iron groups (5.1% in groups A and C [A = 1.7%; C = 10%]) than in the no iron groups (10.4% in groups B and D [B = 2.6%; D = 22.9%]), this difference was not statistically significant (Fisher’s exact test, P = 0.215). Our findings support the prior observations that IV iron replacement in combination with ESAs effectively increases Hgb levels and is safe.17,20,21,25,26
Combining IV iron with ESA increases the Hgb level response and may either shorten the time to response and/or decrease the ESA requirement. Approximately 30%–50% of patients are nonresponders after 12–24 weeks of ESA therapy.8,9,17,27,28 Iron deficiency may be a major factor accounting for ESA resistance. Decreased ESA responsiveness in the dialysis population can be corrected by providing adequate iron supplementation. 11,18 Also, ESA nonresponders may become responders with IV iron replacement while continuing the ESA. ESA treatment in responders can produce a functional iron deficiency, because the ESA produces a rapid initiation of erythropoiesis. Inducing functional iron deficiency with ESA therapy implies that the iron supply to the erythron may be the rate-limiting step in erythropoiesis, and the IV iron dose may be important.25 As ESA responders and nonresponders experienced improvement in Hgb levels with IV iron therapy in this trial, IV iron supplementation may be required to achieve and/or maintain a response to ESA therapy.
Iron available for erythropoiesis is derived from the balance between dietary sources and that in the usable pool within the reticuloendothelial system.29 ESA therapy can result in RBC production that exceeds the rate of iron mobilization, even with adequate iron stores. Inflammatory cytokines may also hinder the release of stored iron from macrophages by inducing hepcidin and thus further contribute to an inadequate rate of RBC production.30–34
Of note, baseline ferritin levels were higher in the ESA nonresponders (groups C and D) than in the ESA responders (groups A and B), although these differences were not statistically significant. This finding may be consistent with elevated inflammatory cytokines impairing the availability of iron, leading to a failed ESA response. ESA resistance is multifactorial, with these factors contributing to the rapid depletion of the usable iron pool, thus blunting the ESA response. Identifying factors that allow for maximizing ESA therapy in CCIA patients may result in greater ESA efficiency. The IV route of iron replacement is superior to oral administration and accounts for one of these variables.17,21,25,26
Safely administering IV iron is an important factor that influences the choice of iron preparations. In the United States, the only IV iron indicated for iron deficiency anemia is iron dextran. The risk of allergic reactions and the need for test doses may account for practitioners limiting the use of iron dextran, despite a compelling medical need for rapid, reliable, and safe replenishment of body iron in populations such as those with CKD35–37 and CCIA. The non–dextran- containing IV irons (iron sucrose, ferric gluconate) are currently only FDA approved for CKD indications at doses of 100–200 mg over 2–5 minutes or up to 400 mg over 2.5 hours for iron sucrose and only 125 mg over 10 minutes for ferric gluconate. 18,19
This study supports other findings that IV iron sucrose is generally well tolerated at doses of 7 mg/kg, up to a maximum of 500 mg over 4 hours, in CCIA. Caution should be exercised, however, especially in patients with a lower body weight. This concern is supported by a study of iron sucrose in nondialysis CKD, where hypotension occurred in two patients < 65 kg after 500 mg doses were administered over 4 hours.38
Conclusion
This study’s primary objective was to determine whether prior response to ESA treatment would influence response to IV iron, not to detect differences between functional and absolute iron deficiency. Our findings support that administration of IV iron while continuing ESA treatment may correct functional, as well as absolute, iron deficiency in CCIA. Baseline iron indices did not predict responsiveness to iron sucrose. Without additional data identifying predictors of ESA responsiveness in CCIA, a more proactive approach that includes IV iron may be warranted, as in CKDrelated anemia. As a better understanding of functional iron deficiency evolves, it is becoming apparent that IV iron is important to optimize the response to ESAs for CCIA. Additional studies are needed to understand the mechanisms responsible for functional iron deficiency in CCIA and to assist in identifying the optimal IV iron administration schedule.
Acknowledgments: The authors wish to thank the study coordinators; the patients at each of the participating centers; and Drs. Perry Rigby and Robert Means, for reviewing the manuscript.
*Additional members of the Iron Sucrose Study Group include Ali Ben-Jacob, MD, Cache Valley Cancer Treatment and Research Clinic, Inc., Logan, UT; Amol Rakkar, MD, Hope Center, Terre Haute, IN; Philip Chatham, MD, Granada Hills, CA; Ahmed Maqbool, MD, Welborn Clinic, Research Center, Evansville, IN; Timothy Pluard, MD, Washington University, Medical Oncology, St. Peters, MO; Nafisa Burhani, MD, Joliet Oncology- Hematology Associates, LTD, Joliet, IL; David Henry, MD, Pennsylvania Hematology and Oncology Associates, Philadelphia, PA; David Watkins, MD, Allison Cancer Center, Midland, TX; Howard Ozer, MD, University of Oklahoma Health Science Center-Hematology Oncology Section, Oklahoma City, OK; Leo Orr, MD, Leo E. Orr, Inc., Los Angeles, CA; Billy Clowney, MD, Santee Hematology Oncology, Sumter, SC, Rene Rothestein-Rubin, MD, Rittenhouse Hematology/ Oncology, Philadelphia, PA; Peter Eisenberg, MD, California Cancer Care, Greenbrae, CA; Rosalba Rodriguez, MD, Chula Vista, CA; Kumar Kapisthalam, MD, United Professional Center, Pasco Hernando Oncology, New Port Richey, FL; Jennifer Caskey, MD, Wheat Ridge, CO; Sayed E. Ahmend, MD, Sebring, FL; Patricia Braly, MD, Hematology and Oncology Specialties, New Orleans, LA; Donald Flemming, MD, Medical Center of Vincennes, The Bierhaus Center, Vincennes, IN; William Tester, MD, Albert Einstein Cancer Center, Philadelphia, PA; William Solomon, MD, SUNY Downstate Medical Center, Brooklyn, NY; Mark Hancock, MD, Mile Hile Oncology, Denver, CO; Youssef Hanna, MD, Huron Medical Center, Port Huron, MI; Scot Sorensen, MD, Prairie View Clinic, Lincoln, NE; and Mark Yoffe, MD, Raleigh, NC.
References
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8. Demetri GD, Kris M, Wade J, Degos L, Cella D. Quality-of-life benefit in chemotherapy patients treated with epoetin alfa is independent of disease response or tumor type: results from a prospective community oncology study. Procrit Study Group. J Clin Oncol 1998;16:3412–3425.
9. Gabrilove JL, Cleeland CS, Livingston RB, Sarokhan B, Winer E, Einhorn LH. Clinical evaluation of once-weekly dosing of epoetin alfa in chemotherapy patients: improvements in hemoglobin and quality of life are similar to three-times-weekly dosing. J Clin Oncol 2001;19:2875–2882.
10. Glaspy J, Bukowski R, Steinberg D, Taylor C, Tchekmedyian S, Vadhan-Raj S. Impact of therapy with epoetin alfa on clinical outcomes in patients with nonmyeloid malignancies during cancer chemotherapy in community oncology practice. Procrit Study Group. J Clin Oncol 1997;15:1218–1234.
11. Drüeke TB, Bárány P, Cazzola M, et al. Management of iron deficiency in renal anemia: guidelines for the optimal therapeutic approach in erythropoietin-treated patients. Clin Nephrol 1997;48:1–8.
12. Fishbane S, Frei GL, Maesaka J. Reduction in recombinant human erythropoietin doses by the use of chronic intravenous iron supplementation. Am J Kidney Dis 1995;26:41–46.
13. Van Wyck DB, Roppolo M, Martinez CO, et al. A randomized, controlled trial comparing IV iron sucrose to oral iron in anemic patients with nondialysis-dependent CKD. Kidney Int 2005;68:2846–2856.
14. Steensma DP, Sloan JA, Dakhil SR, et al. Phase III, randomized study of the effects of parenteral iron, oral iron, or no iron supplementation on the erythropoietic re sponse to darbepoetin alfa for patients with chemotherapy-associated anemia. J Clin Oncol 2011;29:97–105.
15. Auerbach M, Ballard H, Glaspy J. Clinical update: intravenous iron for anaemia. Lancet 2007;369:1502–1504.
16. Bokemeyer C, Aapro MS, Courdi A, et al. EORTC guidelines for the use of erythropoietic proteins in anaemic patients with cancer: 2006 update. Eur J Cancer 2007;43:258– 270.
17. Auerbach M, Ballard H, Trout JR, et al. Intravenous iron optimizes the response to recombinant human erythropoietin in cancer patients with chemotherapy-related anemia: a multicenter, open-label, randomized trial. J Clin Oncol 2004;22:1301–1307.
18. Aronoff GR, Bennett WM, Blumenthal S, et al. Iron sucrose in hemodialysis patients: safety of replacement and maintenance regimens. Kidney Int 2004;66:1193–1198.
19. Faich G, Strobos J. Sodium ferric gluconate complex in sucrose: safer intravenous iron therapy than iron dextrans. Am J Kidney Dis 1999;33:464–470.
20. Henry DH, Dahl NV, Auerbach M, Tchekmedyian S, Laufman LR. Intravenous ferric gluconate significantly improves response to epoetin alfa versus oral iron or no iron in anemic patients with cancer receiving chemotherapy. Oncologist 2007;12:231–242.
21. Hedenus M, Birgegård G, Näsman P, et al. Addition of intravenous iron to epoetin beta increases hemoglobin response and decreases epoetin dose requirement in anemic patients with lymphoproliferative malignancies: a randomized multicenter study. Leukemia 2007;21:627–632.
22. Bastit L, Vandebroek A, Altintas S, et al. Randomized, multicenter, controlled trial comparing the efficacy and safety of darbepoetin alpha administered every 3 weeks with or without intravenous iron in patients with chemotherapy-induced anemia. J Clin Oncol 2008;26:1611–1618.
23. Pedrazzoli P, Farris A, Del Prete S, et al. Randomized trial of intravenous iron supplementation in patients with chemotherapy- related anemia without iron deficiency treated with darbepoetin alpha. J Clin Oncol 2008;26:1619–1625.
24. Chandler G, Harchowal J, Macdougall IC. Intravenous iron sucrose: establishing a safe dose. Am J Kidney Dis 2001;38:988–991.
25. Lerchenmueller C, Husseini F, Gaede B, Mossman T, Suto T, Vanderbroek A. Intravenous (IV) iron supplementation in patients with chemotherapy-induced anemia (CIA) receiving darbepoetin alfa every 3 weeks (q3w): iron parameters in a randomized controlled trial. Blood 2006;108:1552.
26. Pinter T, Mossman T, Suto T, Vansteenkiste J. Effects of intravenous iron supplementation on responses to every-3-week darbepoetin alfa by baseline hemoglobin in patients with chemotherapy-induced anemia. J Clin Oncol 2007;25(18S):9106.
27. Glaspy J, Jadeja JS, Justice G, et al. A dose-finding and safety study of novel erythropoiesis stimulating protein (NESP) for the treatment of anaemia in patients receiving multicycle chemotherapy. Br J Cancer 2001;84(suppl 1):17–23.
28. Littlewood TJ, Bajetta E, Nortier JW, Vercammen E, Rapoport B; Epoetin Alfa Study Group. Effects of epoetin alfa on hematologic parameters and quality of life in cancer patients receiving nonplatinum chemotherapy: results of a randomized, double-blind, placebocontrolled trial. J Clin Oncol 2001;19:2865– 2874.
29. Henry DH. Supplemental iron: a key to optimizing the response of cancer-related anemia to rHuEPO? Oncologist 1998;3:275–278. 30. Ganz T. Hepcidin—a regulator of intestinal iron absorption and iron recycling by macrophages. Best Pract ClinHaematol 2005;18:171–182.
31. Ganz T. Hepcidin—a peptide hormone at the interface of innate immunity and iron metabolism. Curr Top Microbiol Immunol 2006;306:183–198.
32. Viatte L, Nicolas G, Lou DQ, et al. Chronic hepcidin induction causes hyposideremia and alters the pattern of cellular iron accumulation in hemochromatotic mice. Blood 2006;107:2952–2958.
33. Weinstein DA, Roy CN, Fleming MD, Loda MF, Wolfsdorf JI, Andrews NC. Inappropriate expression of hepcidin is associated with iron refractory anemia: implications for the anemia of chronic disease. Blood 2002;100:3776–3781.
34. Wrighting DM, Andrews NC. Interleukin- 6 induces hepcidin expression through STAT3. Blood 2006;108:3204–3209.
35. Wysowski DK, Swartz L, Borders- Hemphill BV, Goulding MR, Dormitzer C. Use of parenteral iron products and serious anaphylactic-type reactions. Am J Hematol 2010;85:650–654.
36. Bailie GR, Clark JA, Lane CE, Lane PL. Hypersensitivity reactions and deaths associated with intravenous iron preparations. Nephrol Dialysis Transplant 2005;20:1443– 1449.
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Lowell B. Anthony, MD,1 Nashat Y. Gabrail, MD,2 Hassan Ghazal, MD,3, Donald V. Woytowitz, MD,4 Marshall S. Flam, MD,5 Anibal Drelichman, MD,6, David M. Loesch, MD,7, Demi A. Niforos, MS,8, and Antoinette Mangione, MD, PharmD9; for the Iron Sucrose Study Group*
1 Stanley S. Scott Cancer Center, Louisiana State University Health Sciences Center, New Orleans, LA; 2 Nashat Cancer Center, Canton, OH; 3 Kentucky Cancer Clinic, Hazard, KY; 4 Florida Cancer Specialists, Fort Myers, FL; 5 Hematology/Oncology Group of Fresno, Fresno, CA; 6 Newland Medical Associates, Southfield, MI; 7 Oncology/Hematology Associates, Indianapolis, IN; 8 AAI Pharma, Inc., Natick, MA; and 9 Luitpold Pharmaceuticals/American Regent, Inc., Norristown, PA
Manuscript received January 2, 2011; accepted June 16, 2011.
This work was presented at the 43rd Annual Meeting of the American Society of Clinical Oncology; June 1–5, 2007 in Chicago, IL, and was supported by Luitpold Pharmaceuticals/American Regent, Inc., Shirley, NY.
Correspondence to: Lowell B. Anthony, MD, LSUHSC New Orleans, Ochsner Kenner Medical Center, 200 West Esplanade, Kenner, LA 70065; e-mail: lantho@lsuhsc.edu.
Conflicts of interest: Ms. Niforos was a fulltime salaried employee of AAI Pharma, Inc., contracted to perform all biostatistical services for the clinical trial. Dr. Mangione was a fulltime salaried employee of the trial sponsor, Luitpold Pharmaceuticals/American Regent, Inc. Drs. Anthony, Gabrail, Ghazal, Woytowitz, Flam, Drelichman, and Loesch have nothing to disclose.
Mild-to-moderate anemia occurs in up to 75% of cancer patients undergoing either single- or multimodality therapy and may contribute to an increased morbidity and reduced quality of life (QOL).1–4 This form of anemia resembles anemia of chronic disease, with a blunted erythropoietin response and inadequate erythropoietin production.5 Increasing hemoglobin (Hgb) concentrations and reducing red blood cell (RBC) transfusions while improving QOL and tolerance to cancer therapies are the treatment-related goals.
Intravenous (IV) iron is commonly administered with ESAs in CKD-associated anemia.12,13 Most studies regarding IV iron replacement in cancer and/or chemotherapy-induced anemia (CCIA) are positive, with one exception: Steensma et al14 reported no benefit in adding IV ferric gluconate to an ESA in a phase III randomized trial in which an oral placebo and iron were used as comparators. Practice guidelines are inconsistent, as the National Comprehensive Cancer Network (NCCN) recommends the IV route when iron is prescribed,6 and the American Society of Hematology/ American Society of Clinical Oncology considers the evidence insufficient to support routine IV iron use.15,16 Auerbach et al17 demonstrated that IV iron dextran results in a greater Hgb level increase than oral iron in ESAtreated patients. Approved formulations of IV iron in the United States include iron dextran, iron sucrose, and ferric gluconate, with the majority of published data with iron dextran.15,18,19 However, the iron dextrans have black-box warnings, and test doses are recommended. Henry et al20 reported that IV ferric gluconate significantly increased Hgb response when compared with oral iron or no iron and was well tolerated in CCIA.
Early work with IV iron sucrose includes a trial evaluating 67 lymphoma patients randomized between ESA or ESA with IV iron sucrose.21 Despite adequate bone marrow iron stores, the Hgb response was greater (91% vs 54%) and the time to reach a Hgb level > 2 g/dL was less (6 vs 12 weeks) in the IV iron-treated group.21 Another trial randomized 398 CCIA patients between fixed IV iron doses (mean weekly dose, 64.8 mg) with ESA versus standard practice (2% received IV iron).22 IV iron resulted in a trend toward a higher ferritin level, but transferrin saturation (TSAT) remained similar between the two groups.22 A study in patients with noniron-deficient anemic solid tumors receiving chemotherapy also demonstrated an increase in hemoglobin levels statistically favoring the darbepoetin alfa (Aranesp)/iron group.23 As additional information is needed, this study was performed to determine whether IV iron sucrose combined with ESA increases Hgb levels in CCIA patients who have been previously treated with an ESA.
Patients and methods
Patient eligibility
his was an open-label, phase III, randomized, institutional review board-approved, multicenter study at 56 US centers. After signing informed consent, patients ≥ 18 years of age with a histologic diagnosis of cancer (acute leukemia or myeloproliferative syndrome excluded) receiving ongoing or planned chemotherapy, with a Hgb level ≤ 10.0 g/dL, body weight > 50 kg, and a Karnofsky performance status of ≥ 60%, were eligible. Patients were excluded if they had iron depletion, active infection, myelophthisic bone marrow (except for hematologic malignancy), hypoplastic bone marrow, uncontrolled hypertension, bleeding, or planned surgery. To ensure a stable baseline Hgb value, no IV iron within 2 months of consent or RBC transfusions within 3 weeks of randomization were allowed.
Treatment
After 8 weeks of fixed ESA doses in stage 1, patients were classified as either ESA responders (≥ 1 g/dL Hgb level increase from baseline) or nonresponders (< 1 g/dL Hgb level increase from baseline), with each group separately randomized centrally using block randomization to receive either IV iron sucrose or no iron treatment (Figure 1). At the time of randomization (beginning of stage 2), patients were stratified according to malignancy type (solid tumor vs hematologic) and Hgb level (< 12 g/dL vs ≥ 12 g/dL for ESA responders; < 9.5 g/dL vs ≥ 9.5 g/dL for ESA nonresponders).
The calculated dose of the study drug (iron sucrose [Venofer]; 7 mg/kg up to 500 mg maximum) was added to 500 mL of normal or half-normal saline and administered IV over 4 hours.24 Patients randomized to receive iron sucrose were scheduled to receive up to three infusions at 1- to 3-week intervals during the first 9 weeks of stage 2, with the first dose administered as soon as possible after randomization. The last dose of ESA was given on or before week 12 of stage 2.
Outcome measures
The primary endpoint for efficacy was the change from baseline (end of stage 1) to the maximum Hgb level achieved during stage 2 in patients who responded to ESA. Major secondary endpoints included changes in Hgb levels when iron sucrose was added to ESA nonresponders as well as the percentage of all randomized patients with Hgb level increases > 1 g/dL, > 2 g/dL, and > 3 g/dL; changes in Hgb levels and iron indices from baseline at each visit; and changes in the 13-item Functional Assessment of Chronic Illness Therapy (FACIT) fatigue scale. Hgb levels were obtained weekly, and iron indices were measured every 3 weeks. The FACIT fatigue scale was measured during stage 1 at consent, weeks 4, and 8 and during stage 2 weeks 3, 6, 9, and at the end of the study.
Adverse events were recorded hourly during iron sucrose administration and from the day of randomization through study completion or 30 days following the last dose of study drug, whichever was later. Investigators provided the date of onset, severity, relationship, date of resolution, action taken, and adverse event outcome. Adverse drug events were events considered by the investigator to be possibly, probably, or definitely related to the study drug.
Statistical method
The sample size was based on the hypothesis that iron-treated ESA responders (group A) would have a 1.0 g/dL or higher mean increase in Hgb levels than would ESA responders who did not receive iron (group B). The standard deviation (SD) of the difference was assumed to be ≤ 1.5 g/dL. Targeting a 1.0 g/dL change in Hgb level to be significant, 49 patients/ group were required (alpha = 0.05; beta = 0.10). Assuming that the ESA response rate in stage I was at least 40% and that the stage I and stage 2 dropout rates were no more than 10% and 25%, respectively, 325 patients were the targeted number for stage I enrollment, with adjustments made by monitoring the stage I response rate.
The intent-to-treat (ITT) population included patients randomized into stage 2 based on actual treatment. The evaluable population included ITT patients who completed at least 10 weeks of stage 2 or who had interventions (RBC transfusions or nonstudy iron) prior to week 10.
Continuous variables were assessed using analysis of covariance and t-tests. Ordinal responses were analyzed with the Fisher’s exact test and Cochran-Mantel-Haenszel statistics. Changes from baseline to each visit for all FACIT scores were assessed for treatment groups with the unpaired two-sample t-test.
Results
Patient disposition and demographics
Of the 375 patients enrolled during the run-in stage 1 period (between July 2003 and October 2005), 132 patients discontinued treatment (the most common reasons were a required intervention [50], withdrawn consent [23], and adverse events [17]). Fourteen patients completed stage 1 but did not enter stage 2. Figure 2 shows the numbers of patients who were randomly assigned to the two treatment groups and were evaluated for safety and efficacy as well as reasons for study discontinuation. Table 1 shows the patient numbers assigned to the various treatment groups (A to D) based on ESA response in stage I and the study population; it also demonstrates the similar baseline demographic characteristics between the treatment groups. At baseline (ie, prior to randomization), there were no statistically significant differences in Hgb level, TSAT, and ferritin level between the ESA responders (A vs B) and nonresponders (C vs D).
Efficacy of iron sucrose
Mean maximum improvement in Hgb levels (Table 2). Among ESA responders (groups A and B), a statistically significantly greater mean maximum Hgb level increase was observed among patients who received iron sucrose (group A) than among those who did not (group B), achieving the primary endpoint (ITT, P = 0.004; evaluable, P = 0.008). A statistically significant greater increase in the mean maximum Hgb level was observed following iron sucrose (groups A and C) when compared with no iron treatment (groups B and D), regardless of prior ESA response. In the ESA nonresponder group, a significant increase (P = 0.027) in the mean maximum Hgb level was observed between those who received iron sucrose (group C) and those who did not (group D) in the ITT population; a statistical difference was not seen in the evaluable population (P = 0.082).
With regard to tumor subtypes, breast cancer and other tumor types, but not lung cancer, were associated with statistically significant increases in maximum Hgb levels following iron sucrose, regardless of prior ESA response.
Absolute increases in Hgb levels (Table 2). A greater proportion of patients assigned to IV iron sucrose achieved a ≥ 2 g/dL and ≥ 3 g/dL increase in Hgb level during the study than did those who did not receive iron. These differences were statistically significant for all the groups except for the evaluable ≥ 3 g/dL nonresponder group. The only statistically significant difference in the proportion achieving a ≥ 1 g/dL Hgb level increase occurred in the ESA nonresponder groups. In addition, baseline hematologic characteristics and iron indices did not predict the efficacy of IV iron treatment (as defined by a > 1 g/dL or > 2 g/dL increase in Hgb level). In the IV iron sucrose-treated group, there was no statistical difference in these baseline characteristics in the patients who demonstrated a > 1 g/dL (data not shown) or a > 2 g/dL treatment response to IV iron.
Changes from baseline in Hgb and ferritin levels and in TSAT. Figure 3 summarizes the Hgb level, ferritin level, and TSAT responses by study visit after IV iron sucrose compared with no iron in the ITT population. Between treatment groups, statistically significant differences (P < 0.05) were present by weeks 7, 3, and 13 for Hgb level, ferritin level, and TSAT, respectively. At the end of the study, week 13, the mean Hgb level increase from baseline was 2.3 g/dL versus 1.2 g/dL (P < 0.002), the mean ferritin level increase from baseline was 419 ng/mL versus a decrease of 50 ng/mL (P < 0.001), and the mean TSAT increase from baseline was 8.8% versus 0.2% (P < 0.005) in the iron sucrose versus no iron group.
Changes in fatigue levels (FACIT fatigue scale). There was a statistically significant decrease in the level of fatigue at the end of the study compared with at baseline (end of stage 1) in the iron sucrose-treated patients in the ITT but not in the evaluable population (–3.3 iron sucrose/–2.1 no iron, P = 0.022 ITT; –3.0 iron sucrose/–1.7 no iron, P = 0.058 evaluable population). No significant decrease in the level of fatigue was experienced by the patients who received no iron. There were no statistically significant differences between the groups in changes from baseline at each visit..
Safety of iron sucrose
Extent of exposure. In the ITT population, the mean per patient total dose of iron sucrose administered was 1,123 (SD, 402) mg in group A (responders) and 1,113 (SD, 387) mg in group C (nonresponders).
Adverse drug events (ADEs). All safety analyses were performed using the ITT population. Serious ADEs were experienced by three patients in the iron sucrose group (chest pain, hypersensitivity, and hypotension, one patient each) and by no patients in the ESA-only group. One ESA-only group patient (arthralgia) and four iron sucrose patients (hypersensitivity; abdominal pain; arthralgia and muscle cramps; myalgia, nausea, and vomiting) were prematurely discontinued from the study drug due to the occurrence of an ADE.
At least one ADE was experienced by 37.4% of the patients in the iron sucrose group and 0.8% in the control group. The most common (³ 5%) ADEs were nausea (8.1%), dysgeusia (8.1%), back pain (6.1%), arthralgia (6.1%), muscle cramp (6.1%), and peripheral edema (5.1%). Within the ESA-only group, the only ADE reported was hypertension (one subject, 0.8%).
Eleven grade 3 (National Institutes of Health/National Cancer Institute– Common Terminology Criteria, version 2.0) ADEs occurred in iron sucrose-treated patients and included nausea (2.0%), hypotension (2.0%), abdominal pain (1.0%), chest pain (1.0%), hypersensitivity (1.0%), arthralgia (1.0%), dizziness (1.0%), dyspnea (1.0%), and hypertension (1.0%). A serious grade 3 hypotensive event occurred in a 49-year-old woman weighing 50 kg who experienced dizziness, nausea, vomiting, and transient hypotension (110/60 mm Hg to 70/40 mm Hg) after her first iron sucrose dose of 375 mg. Ninety minutes later, following IV steroids, iron sucrose was restarted and the hypotension recurred. The patient received two subsequent lower iron sucrose doses (200 mg over 4 hours), with no further adverse reactions.
Deaths and thrombotic events. These events are summarized in Table 3. None of these events was judged by the investigators to be related to the study drug.
Laboratory results. Statistically greater mean increases in ferritin levels, TSAT, Hgb levels, hematocrit, mean corpuscular hemoglobin, mean corpuscular volume, and monocytes oc curred in the iron sucrose-treated group. There were no significant differences between treatment groups in clinical chemistry safety laboratory results.
Discussion
This study is the first to evaluate IV iron in CCIA patients who have received prior ESA therapy. IV iron sucrose administered with ESAs significantly increased Hgb levels in CCIA patients. Prior ESA response did not predict Hgb level response to iron sucrose, as benefit was demonstrated in both ESA responders and nonresponders. Baseline hematologic/ iron indices also did not predict IV iron responsiveness, as these characteristics were similar in IV iron responders and nonresponders. Improvement in QOL, as measured by fatigue levels at study completion, was also observed after IV iron but not in the no iron group. IV iron studies are commonly open-label because of the difficulty in blinding iron’s viscous dark-colored solution.
This study design limits the significance of QOL measurements in IV iron studies, where primary endpoints are typically objective measurements. Even though transfusion rates were lower in the IV iron groups (5.1% in groups A and C [A = 1.7%; C = 10%]) than in the no iron groups (10.4% in groups B and D [B = 2.6%; D = 22.9%]), this difference was not statistically significant (Fisher’s exact test, P = 0.215). Our findings support the prior observations that IV iron replacement in combination with ESAs effectively increases Hgb levels and is safe.17,20,21,25,26
Combining IV iron with ESA increases the Hgb level response and may either shorten the time to response and/or decrease the ESA requirement. Approximately 30%–50% of patients are nonresponders after 12–24 weeks of ESA therapy.8,9,17,27,28 Iron deficiency may be a major factor accounting for ESA resistance. Decreased ESA responsiveness in the dialysis population can be corrected by providing adequate iron supplementation. 11,18 Also, ESA nonresponders may become responders with IV iron replacement while continuing the ESA. ESA treatment in responders can produce a functional iron deficiency, because the ESA produces a rapid initiation of erythropoiesis. Inducing functional iron deficiency with ESA therapy implies that the iron supply to the erythron may be the rate-limiting step in erythropoiesis, and the IV iron dose may be important.25 As ESA responders and nonresponders experienced improvement in Hgb levels with IV iron therapy in this trial, IV iron supplementation may be required to achieve and/or maintain a response to ESA therapy.
Iron available for erythropoiesis is derived from the balance between dietary sources and that in the usable pool within the reticuloendothelial system.29 ESA therapy can result in RBC production that exceeds the rate of iron mobilization, even with adequate iron stores. Inflammatory cytokines may also hinder the release of stored iron from macrophages by inducing hepcidin and thus further contribute to an inadequate rate of RBC production.30–34
Of note, baseline ferritin levels were higher in the ESA nonresponders (groups C and D) than in the ESA responders (groups A and B), although these differences were not statistically significant. This finding may be consistent with elevated inflammatory cytokines impairing the availability of iron, leading to a failed ESA response. ESA resistance is multifactorial, with these factors contributing to the rapid depletion of the usable iron pool, thus blunting the ESA response. Identifying factors that allow for maximizing ESA therapy in CCIA patients may result in greater ESA efficiency. The IV route of iron replacement is superior to oral administration and accounts for one of these variables.17,21,25,26
Safely administering IV iron is an important factor that influences the choice of iron preparations. In the United States, the only IV iron indicated for iron deficiency anemia is iron dextran. The risk of allergic reactions and the need for test doses may account for practitioners limiting the use of iron dextran, despite a compelling medical need for rapid, reliable, and safe replenishment of body iron in populations such as those with CKD35–37 and CCIA. The non–dextran- containing IV irons (iron sucrose, ferric gluconate) are currently only FDA approved for CKD indications at doses of 100–200 mg over 2–5 minutes or up to 400 mg over 2.5 hours for iron sucrose and only 125 mg over 10 minutes for ferric gluconate. 18,19
This study supports other findings that IV iron sucrose is generally well tolerated at doses of 7 mg/kg, up to a maximum of 500 mg over 4 hours, in CCIA. Caution should be exercised, however, especially in patients with a lower body weight. This concern is supported by a study of iron sucrose in nondialysis CKD, where hypotension occurred in two patients < 65 kg after 500 mg doses were administered over 4 hours.38
Conclusion
This study’s primary objective was to determine whether prior response to ESA treatment would influence response to IV iron, not to detect differences between functional and absolute iron deficiency. Our findings support that administration of IV iron while continuing ESA treatment may correct functional, as well as absolute, iron deficiency in CCIA. Baseline iron indices did not predict responsiveness to iron sucrose. Without additional data identifying predictors of ESA responsiveness in CCIA, a more proactive approach that includes IV iron may be warranted, as in CKDrelated anemia. As a better understanding of functional iron deficiency evolves, it is becoming apparent that IV iron is important to optimize the response to ESAs for CCIA. Additional studies are needed to understand the mechanisms responsible for functional iron deficiency in CCIA and to assist in identifying the optimal IV iron administration schedule.
Acknowledgments: The authors wish to thank the study coordinators; the patients at each of the participating centers; and Drs. Perry Rigby and Robert Means, for reviewing the manuscript.
*Additional members of the Iron Sucrose Study Group include Ali Ben-Jacob, MD, Cache Valley Cancer Treatment and Research Clinic, Inc., Logan, UT; Amol Rakkar, MD, Hope Center, Terre Haute, IN; Philip Chatham, MD, Granada Hills, CA; Ahmed Maqbool, MD, Welborn Clinic, Research Center, Evansville, IN; Timothy Pluard, MD, Washington University, Medical Oncology, St. Peters, MO; Nafisa Burhani, MD, Joliet Oncology- Hematology Associates, LTD, Joliet, IL; David Henry, MD, Pennsylvania Hematology and Oncology Associates, Philadelphia, PA; David Watkins, MD, Allison Cancer Center, Midland, TX; Howard Ozer, MD, University of Oklahoma Health Science Center-Hematology Oncology Section, Oklahoma City, OK; Leo Orr, MD, Leo E. Orr, Inc., Los Angeles, CA; Billy Clowney, MD, Santee Hematology Oncology, Sumter, SC, Rene Rothestein-Rubin, MD, Rittenhouse Hematology/ Oncology, Philadelphia, PA; Peter Eisenberg, MD, California Cancer Care, Greenbrae, CA; Rosalba Rodriguez, MD, Chula Vista, CA; Kumar Kapisthalam, MD, United Professional Center, Pasco Hernando Oncology, New Port Richey, FL; Jennifer Caskey, MD, Wheat Ridge, CO; Sayed E. Ahmend, MD, Sebring, FL; Patricia Braly, MD, Hematology and Oncology Specialties, New Orleans, LA; Donald Flemming, MD, Medical Center of Vincennes, The Bierhaus Center, Vincennes, IN; William Tester, MD, Albert Einstein Cancer Center, Philadelphia, PA; William Solomon, MD, SUNY Downstate Medical Center, Brooklyn, NY; Mark Hancock, MD, Mile Hile Oncology, Denver, CO; Youssef Hanna, MD, Huron Medical Center, Port Huron, MI; Scot Sorensen, MD, Prairie View Clinic, Lincoln, NE; and Mark Yoffe, MD, Raleigh, NC.
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A Phase II Tolerability Trial of Neoadjuvant Docetaxel with Carboplatin and Capecitabine in Locally Advanced Breast Cancer
The standard of care for locally advanced breast cancer (LABC) is neoadjuvant chemotherapy,1 with LABC including clinical stages IIA, IIB, and IIIA. The goals of preoperative chemotherapy are to downstage so as to render breast conservation feasible, to eradicate disease in the axillary nodes, and to allow in vivo testing of tumor drug sensitivity, all with the ultimate aim of improving prognosis. Clinical trials have demonstrated that the pathologic in-breast response generally correlates with pathologic response in the lymph nodes. Furthermore, nodal status at the time of surgery correlates with overall survival (OS) and disease-free survival (DFS).2,3 A combined analysis of two large prospective neoadjuvant chemotherapy trials demonstrated significantly higher 5-year OS and DFS in patients achieving in-breast pathologic complete response (pCR), compared with those who did not (OS, 89% vs 64%; DFS, 87% vs 58%, respectively).4
At the start of this trial, the most effective neoad- juvant regimen remained in question. Even now, National Comprehensive Cancer Center guidelines suggest that any recommended adjuvant regimen can be used in the neoadjuvant setting.1 Numerous phase II and III trials have evaluated single-agent5–8 and combination9– 32 chemotherapies, most of which are anthracycline- based, with pCR rates reported between 7% and 36%. In the NSABP-B27 study, patients treated preoperatively with four cycles of doxorubicin and cyclophosphamide (AC) followed by four cycles of docetaxel (Taxotere) had a 26% pCR rate versus a 13% pCR rate in those receiving preoperative AC and postoperative docetaxel. Despite the doubling of pCR with neoadjuvant docetaxel, there was no difference in DFS or OS.9 However, as reported by Kuerer et al, patients achieving a pCR after completion of neoadjuvant chemotherapy appeared to have superior survival.4
Many previous trials (including the study reported here) did not exclude patients with human epidermal growth factor receptor 2 (HER2)-positive disease. It is now well established that such patients should be treated with neoadjuvant regimens incorporating HER2-targeted therapy. In fact, an early neoadjuvant study of paclitaxel followed by fluorouracil, epirubicin, and cyclophosphamide with or without 24 weeks of concurrent trastuzumab (Herceptin) in patients with HER2-positive tumors was closed early because patients receiving trastuzumab had a pCR rate of 65%, compared with 26% in those who did not receive it.33 Expanded clinical trials of this approach are in progress.
The selection of capecitabine (Xeloda) and docetaxel in the present trial was based on the hypothesis that the upregulation of thymidine phosphorylase by docetaxel should increase the activity of capecitabine. 34–36 Single-agent docetaxel in the neoadjuvant setting has yielded pCR rates of 7%–20%.6–8 Treatment with docetaxel and capecitabine together has been reported to produce pCR rates of 10%–21%.37–39 The addition of carboplatin was based on studies by Hurley et al at the University of Miami39– 41 suggesting that platinum salts appeared quite active in the neoadjuvant setting, with the combination of docetaxel and cisplatin producing a pCR rate of 20%, with no residual disease in the breast or axilla.40 Other regimens incorporating cisplatin or carboplatin have pCR rates ranging from 16% to 24%.27,42–44
Patients and methods
Study design
In this phase II multicenter study, patients were assigned to receive docetaxel (30 mg/m2 IV) and carboplatin (AUC 2 IV) on days 1, 8, and 15 of each 28-day cycle plus capecitabine (625 mg/m2 PO) twice daily on days 5–18. The capecitabine dose was based on observations that this dose was effective and relatively nontoxic in metastatic breast cancer (C.L. Vogel, empirical observations). Patients were to receive four cycles prior to surgical resection.
Given that this neoadjuvant regimen was under study, all of the patients were scheduled to receive a proven standard postoperative adjuvant chemotherapy regimen, starting 4–6 weeks postoperatively, with doxorubicin (60 mg/m2 IV) and cyclophosphamide (600 mg/m2 IV) every 21 days for 4 cycles. This sequential design was prompted by studies such as the NSABP B-27 and Aberdeen trials.9,32
Radiation therapy after lumpectomy or mastectomy was given according to individual institution guidelines. Patients with hormone receptor–positive tumors received appropriate antihormonal therapy. Tumor measurements were assessed at baseline and on day 1 of each cycle by physical examination with calipers. No breast or other imaging was required during the period of neoadjuvant chemotherapy or immediately preoperatively. Patients were considered evaluable if they proceeded to surgery after all intended cycles of neoadjuvant chemotherapy or if they developed disease progression during neoadjuvant therapy.
Patients
Eligible patients were men and women regardless of menopausal status ≥ 18 years of age with coreneedle biopsy proven locally advanced or inflammatory breast cancer. Breast cancer characteristics such as estrogen receptor (ER), progesterone receptor (PR), or HER2 status were collected but not used for inclusion/exclusion. Eligible tumors were T2 requiring mastectomy; T3N0–2; T4; and any TN2–3 that by calipers was > 2 cm or with fixed or matted axillary or imaging-detected internal mammary nodes. Patients with prior ductal carcinoma in situ (DCIS) were included, as were those with ≤ T2N0M0 breast cancer > 5 years prior.
Other requirements were an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1; life expectancy > 6 months; negative metastatic workup (bone scan and CT chest/abdomen/pelvis); adequate bone marrow, liver, and kidney function; and peripheral neuropathy ≤ grade 1. All patients of child-bearing potential were required to consent to dual methods of contraception during treatment and for 3 months afterward. A negative pregnancy test was required for these women before treatment, and any suspicion of pregnancy had to be reported to the treating physician.
Study endpoints
The primary endpoint of the study was the in-breast pCR after four cycles of platinum-based neoadjuvant chemotherapy. Pathologic complete response was defined as complete disappearance of invasive and in situ disease or invasive disease alone. During the course of this trial, it became generally acceptable to include patients with only residual DCIS as equivalent to pCR.45
The secondary endpoints were pCR in the lymph nodes; clinical response rate; tolerability; breast conservation; time to disease progression (local, regional, and distant); and OS. Also recorded was minimal residual disease (MRD), which we arbitrarily defined as ≤ 1 cm invasive carcinoma at resection. The overall treatment plan included postoperative AC to provide a standard-of-care regimen to maximize curative potential.
Statistical analysis
Data were analyzed on an intentto- treat basis. Although pCR rates with doxorubicin plus either cyclophosphamide or docetaxel have been < 15%, the studies by Smith et al26 and Hurley et al39 with in-breast pCR rates of at least 20% served as comparators (albeit imprecise).
Applying the min/max statistical design, the procedure tests the null hypothesis H0: P ≤ 0.15 against the alternative hypothesis H1: P ≥ 0.30. The overall level of significance and power for this design are 5% and 80%, respectively. The sample size needed for the first stage was 23 evaluable patients. If three or fewer pCR responses were observed, then the study would be terminated and the treatment regimen would not be investigated further. Otherwise, an additional 25 evaluable patients would be accrued for a total of 48 study patients. If 11 or fewer responses were observed, then the study would be terminated. Otherwise, this treatment regimen would be recommended to proceed to phase III for further investigation.
Tolerability assessment
At each visit, toxicities were assessed and graded according to the National Cancer Institute Common Toxicity Criteria, version 2.46 Two dose reductions were allowed for all drugs.
Ethical considerations
The investigational nature of this study was fully disclosed to each patient. In accordance with institutional and federal guidelines, the patients were guided through and subsequently signed the informed consent approved by the appropriate site Institutional Review Board.
Literature review
The terms “neoadjuvant” and “breast” were used in a literature search on PubMed, with filters “English” and “clinical trials.” Abstracts for each of the 398 results were reviewed We used phase II or III trials with at least 30 patients, at least four cycles of chemotherapy, and clearly defined pCR for comparison to this study.
Results Patients
Between June 2003 and December 2006, 50 women with a median age of 49 years (range, 28–75 years) were enrolled. One patient was ineligible due to preceding lumpectomy. The 49 eligible patients were treated with ≥ 1 cycle of neoadjuvant chemotherapy between June 27, 2003, and April 12, 2007.
The baseline characteristics of the 49 eligible patients are summarized in Table 3. Thirty-one patients (63%) were premenopausal. Twenty patients (41%) were positive for either ER or PR and were negative for HER2. Eight patients (16%) had HER2- positive tumors, and 23 (46%) had triple-negative tumors. At baseline, 22 patients (45%) had clinical lymphadenopathy, and 1 patient (2%) had inflammatory breast cancer.
The 41 patients (83%) who completed all four cycles of therapy were evaluable for response; 8 (16%) were inevaluable due to noncompliance (1), grade 3 or 4 toxicity (5), or withdrawal of consent (2). The following efficacy assessments apply to the 41 evaluable patients, whereas the toxicity assessments include the 49 patients who received at least one full cycle of chemotherapy.
Clinical response
At study onset, of the 49 eligible patients, 38 (78%) had a palpable inbreast tumor (median size, 5.5 cm); 22 (45%) had enlarged nodes, and 34 (69%) had confirmed nodal involvement (by biopsy or imaging). A clinical complete response (cCR) rate in the breast was seen in 23 of 41 (56%) evaluable patients. Of 22 patients with baseline lymphadenopathy (by imaging or physical examination), 13 had axillary assessment by physical examination throughout treatment, with 12 (92%) exhibiting a cCR in the axilla.
Pathologic response
After four cycles of chemotherapy, an in-breast pCR (the primary endpoint) was demonstrated in 6 of 41 patients (15%). One of these six patients had residual DCIS and is listed separately. All of these patients had nodal pCR, whereas overall, 20 patients (49%) had negative nodes at resection.
The pathology reports of two patients were read as having invasive tumor within lymphatics and lymphovascular invasion (one each) with no measurable disease, with tumor thus sized as Tx. Neither of these patients had involved lymph nodes. Fourteen patients (34%) had MRD in the breast, and 8 of these 14 patients (57%) had residual nodal disease. Nine patients (22%) had T1c tumors (> 1–2 cm), with five of these nine patients (55%) having nodal disease. Seven patients (17%) had T2 tumors (> 2–5 cm) tumors, with five of these seven patients (71%) having nodal disease. These findings are summarized in Table 4. The correlation between in-breast cCR and pCR was 26%.
Biologic features of responders
Of interest, five of the six patients with a pCR had triple-negative tumors. This translates to a 22% pCR rate (5 of 23) in the triple-negative subset, and a pCR rate of 6% (1 of 18) in patients with ER-positive and/ or PR-positive tumors. The remaining patient with a pCR had ER-, PR-, and HER2-positive disease.
One patient had inflammatory breast cancer at diagnosis, and another developed this during the course of chemotherapy; the latter patient was removed from the study for progressive disease. Interestingly, the patient who presented with inflammatory breast cancer was one of the six patients with a pCR. Both of these inflammatory disease patients had triple-negative tumors.
Conversion to breast conservation
Breast conservation was offered to patients if it was deemed appropriate by the treating surgeon. Preoperative imaging was not mandated and thus was not routinely performed. Mastectomy was ultimately performed in 4 of the 6 patients (67%) with pCR and in 22 of the 35 patients (63%) with less than a pCR. Thus, the choice for breast conservation did not correlate well with response to chemotherapy.
Time to disease progression
At a median follow-up of 48 months (range, 7–63), 36 of 41 patients (88%) remained free of disease (range, 19–63 months). Two patients had progressive disease while they were on study treatment and had T3 tumors on resection. Another three patients were found to have progressive disease at 10, 41, and 50 months from study day 1.
Of the nine patients with T1c disease, only one patient (who had positive nodes at resection) had a recurrence (at 41 months). Overall, the patients who had a recurrence had MRD (one patient), T1c (one patient), T2 (one patient), and T3 (the same two patients whose disease progressed while they were on treatment and continued to progress after surgery).
Disease-free and overall survival
Three patients were lost to followup, with point of last contact at 19, 34, and 59 months. Of the 41 evaluable patients, 5 patients developed progressive disease, with 2 of these patients progressing during the study treatment. Disease-free survival at 12, 24, and 36 months was 89%, 89%, and 78%, respectively. Overall survival at these same time points was 95%, 90%, and 76%. None of the patients with a pCR is known to have recurrent disease. Of the six patients achieving pCR, two were lost to follow-up after 34 and 59 months, and four continued diseasefree at 38, 39, 55, and 62 months.
Adverse events
Five patients were removed from the study secondary to toxicities. Grade 3 and 4 toxicity events are summarized in Table 5. Grade 3 toxicities were anemia (4), diarrhea (2), epigastric pain (1), fatigue (2), hand-foot syndrome (1), infection (1), leukopenia (9), pain (5), and peripheral sensory neuropathy (1). Grade 4 toxicities were depression (1) and leukopenia (4). Toxicities (all grades) occurring in ≥ 10% of the 49 treated patients were anemia (76%), leukopenia (70%), fatigue (67%), nausea (59%), alopecia (49%), thrombocytopenia (47%), diarrhea (47%), constipation (37%), pain (35%), vomiting (31%), epigastric pain (27%), nail changes (22%), epiphora (22%), hand-foot syndrome (20%), infection (18%), edema (16%), rash (16%), anorexia (16%), and depression (10%). In the intent-to-treat population, there were nine dose reductions among nine patients, and 19 dose delays among 15 patients.
Discussion
The combination of agents tested thus far in the neoadjuvant setting consistently produce pCR rates far less than 50% in unselected populations. This study was begun prior to the widespread use of personalized medicine. Most prior published trials had utilized anthracycline-based chemotherapy, with response rates generally ranging between 7% and 36%.6,9–26,28–31,41,42
The idea of thymidine phosphorylase upregulation by the combination of capecitabine and docetaxel upon which this study was largely based34–36 has since been disputed.47 The primary endpoint of this trial of a novel platinum- based regimen was a pCR rate of 15%. It is significant that 83% of the pCRs were in triple-negative tumors. A secondary endpoint of MRD was calculated, as this was in the original design of the study, but ultimately was not relevant to the primary endpoint.
Ultimately, pCR is the more relevant point of discussion for the modern era. The 15% pCR rate seen in this phase II study was within range of those achieved in numerous other phase II/III neoadjuvant chemotherapy trials with ≥ 25 patients, ≥ 3 cycles of chemotherapy, and pCR defined as absence of carcinoma in the breast and axilla. To date, no patient in our study with a pCR has been noted to have recurrent disease. However, a recently published French study found a 22% recurrence rate at 11 years in patients with triple-negative breast cancer achieving pCR, highlighting the importance of longer-term follow- up.48.
The inclusion of patients with HER2-positive disease in neoadjuvant studies without HER2-targeted therapy was standard at the time that this study was conducted, but is no longer appropriate. If we were to exclude the eight HER2-positive patients from analysis, then there would be only 34 patients evaluable for response, with a pCR rate of 18%. Buzdar et al33 demonstrated a 65% pCR rate in women with HER2-positive disease treated with neoadjuvant chemotherapy plus trastuzumab. The improvement in pCR with the addition of trastuzumab is supported by other confirmatory trials. Authors of a single-arm trial of dose-dense epirubicin and cyclophosphamide followed by dosedense docetaxel and trastuzumab in a HER2-positive population reported a pCR rate of 57%.49 The randomized NOAH study50 achieved a pCR rate of 23% in 115 patients treated with trastuzumab-based chemotherapy.
It is interesting to note that five of six patients (83%) achieving a pCR in our study had triple-negative tumors. Investigators at the University of Miami presented a retrospective review of locally advanced triple-negative breast cancer treated with docetaxel and a platinum salt, with 61% of patients also receiving AC. The authors reported a pCR rate of 34% overall and 40% for patients receiving AC.51 A pCR rate of 60% was noted in the triplenegative subset of patients in another study evaluating docetaxel, doxorubicin, and cyclophosphamide with or without vinorelbine/capecitabine (GeparTrio Study).52 Further, a pCR rate of 72% was achieved with singleagent cisplatin in a group of 25 women with BRCA1 mutations, suggesting, if confirmed by others, that this largely triple- negative population may be exquisitely sensitive to platinum salts.43 In contrast, in a previous study of cisplatin in BRCA mutation carriers, Garber et al44 reported a pCR rate of 22%, suggesting that further trials are needed specifically in BRCA carriers and in triple-negative tumors to see whether these specific patient subsets preferentially derive benefit from platinum salts in the neoadjuvant setting.
The results of the current study are consistent with others indicating a low likelihood of pCR in patients with ERpositive tumors. In fact, none of our ER-positive patients had a pCR. Neoadjuvant endocrine therapy in postmenopausal women with ER- and/ or PR-positive disease is a reasonable treatment option for selected patients, but endpoints other than pCR have often been used.53,54 It is therefore difficult to directly compare these two strategies. Currently, investigators are comparing the three aromatase inhibitors head to head in the neoadjuvant setting for postmenopausal women with hormone receptor–positive tumors.55
The historic pCR ceiling appears to be rising, albeit slowly. Where targets such as HER2 overexpression and triple- negative biology are recognized, progress is being made. Patient eligibility criteria for neoadjuvant breast cancer studies at the time of this trial were quite broad, and it is now recognized that specific subsets of breast cancer respond differently to different classes of agents. Furthermore, our knowledge about breast cancer prognostic markers continues to expand. Had this study been designed in 2011, other data points such as Ki67 would have been collected. A recently published study on neoadjuvant triplenegative breast cancer found that only patients with baseline Ki67% expression > 10% achieved pCR.56
Given the long-term implications of not achieving pCR, optimal treatment of patients in the adjuvant setting is critical. Although neoadjuvantly treated patients with ER-positive or HER2-positive disease go on to receive adjuvant agents (antihormonal therapy for ER-positive disease and trastuzumab for HER2-positive disease), patients with triple-negative disease lack long-term therapies of proven efficacy. Perhaps, as we edge closer to defining the optimal neoadjuvant agents for each subset of patients, this will be less of a concern. Many earlyphase neoadjuvant studies have been conducted, with promising reports, yet the results of larger, randomized trials continue to frustrate both investigators and clinicians. These deficits in care can only be answered by carefully planned randomized clinical trials.
Acknowledgment: Funding for this study was provided by sanofi-aventis, U.S.
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35. Endo M, Shinbori N, Fukase Y, et al. Induction of thymidine phosphorylase expression and enhancement of efficacy of capecitabine or 5´deoxy-5-fluorouridine by cyclophosphamide in mammary tumor models. Int J Cancer 1999;83:127–134.
36. Yamamoto S, Kurebayashi J, Kurosumi M, et al. Combined effects of docetaxel and fluoropyrimidines on tumor growth and expression of interleukin-6 and thymidine phosphorylase in breast cancer xenografts. Cancer Chemother Pharmacol 2001;48:283–288.
37. Lebowitz PF, Eng-Wong J, Swain SM, et al. A phase II trial of neoadjuvant docetaxel and capecitabine for locally advanced breast cancer. Clin Cancer Res 2004;10:6764–6769.
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39. Hurley J, Reis I, Silva O, et al. Weekly docetaxel/carboplatin as primary systemic therapy for Her2-negative locally advanced breast cancer. Clin Breast Cancer 2005;6:447–454.
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ABOUT THE AUTHORS
Aruna Mani, MD; Sandra X. Franco, MD; Grace Wang, MD: Neil Abramson, MD; Lee S. Schwartzberg, MD: James Jakub, MD; Elizabeth Tan-Chiu, MD: Alisha Stein, RNC, BSN, OCN; Alejandra T. Perez, MD; and Charles L Vogel, MD.
Affiliations: Dr. Mani is a breast medical oncologist at Memorial Cancer Institute, Pembroke Pines, FL. Dr. Franco is now Chief of Oncology at the Oncology Center, Clinica del Country, Bogota, Colombia. Dr. Wang is an oncologist at Advanced Medical Specialties, Miami, FL. Dr. Abramson is Clinical Professor of Medicine and Emeritus Director of Education and Research at Baptist Cancer Institute, University of Florida, Jacksonville, FL. Dr. Schwartzberg is Medical Director of The West Clinic, Memphis, TN. Dr. Jakub is now Assistant Professor of Surgery, Division of Gastroenterology and General Surgery, Mayo Clinic, Rochester, MN. Dr. Tan-Chiu is Medical Director of Florida Cancer Care, Davie, FL. Dr. Schwartz is Principal Investigator at Mount Sinai Medical Center, Miami Beach, FL. Ms. Frankel is Director of Oncology Clinical Research and Development at Memorial Cancer Institute, Hollywood, FL. Dr. Krill-Jackson is an oncologist at Mount Sinai Comprehensive Cancer Center, Miami, FL. Ms. Stein is now Oncology Clinical Coordinator at Genentech Inc., Fort Lauderdale, FL. Dr. Perez is Director of the Breast Cancer Center at Memorial Cancer Institute, Hollywood, FL. Dr. Vogel is Professor of Clinical Medicine and Director of the Women’s Center, Sylvester Comprehensive Cancer Center, Deerfield Beach, FL.
Conflicts of interest: Dr. Vogel has served as an advisor and is a member of the speakers’ bureaus of sanofi-aventis U.S. and Roche, as well as many other companies whose products were not part of the current study plan. The other authors have no pertinent conflicts of interest to disclose.
The standard of care for locally advanced breast cancer (LABC) is neoadjuvant chemotherapy,1 with LABC including clinical stages IIA, IIB, and IIIA. The goals of preoperative chemotherapy are to downstage so as to render breast conservation feasible, to eradicate disease in the axillary nodes, and to allow in vivo testing of tumor drug sensitivity, all with the ultimate aim of improving prognosis. Clinical trials have demonstrated that the pathologic in-breast response generally correlates with pathologic response in the lymph nodes. Furthermore, nodal status at the time of surgery correlates with overall survival (OS) and disease-free survival (DFS).2,3 A combined analysis of two large prospective neoadjuvant chemotherapy trials demonstrated significantly higher 5-year OS and DFS in patients achieving in-breast pathologic complete response (pCR), compared with those who did not (OS, 89% vs 64%; DFS, 87% vs 58%, respectively).4
At the start of this trial, the most effective neoad- juvant regimen remained in question. Even now, National Comprehensive Cancer Center guidelines suggest that any recommended adjuvant regimen can be used in the neoadjuvant setting.1 Numerous phase II and III trials have evaluated single-agent5–8 and combination9– 32 chemotherapies, most of which are anthracycline- based, with pCR rates reported between 7% and 36%. In the NSABP-B27 study, patients treated preoperatively with four cycles of doxorubicin and cyclophosphamide (AC) followed by four cycles of docetaxel (Taxotere) had a 26% pCR rate versus a 13% pCR rate in those receiving preoperative AC and postoperative docetaxel. Despite the doubling of pCR with neoadjuvant docetaxel, there was no difference in DFS or OS.9 However, as reported by Kuerer et al, patients achieving a pCR after completion of neoadjuvant chemotherapy appeared to have superior survival.4
Many previous trials (including the study reported here) did not exclude patients with human epidermal growth factor receptor 2 (HER2)-positive disease. It is now well established that such patients should be treated with neoadjuvant regimens incorporating HER2-targeted therapy. In fact, an early neoadjuvant study of paclitaxel followed by fluorouracil, epirubicin, and cyclophosphamide with or without 24 weeks of concurrent trastuzumab (Herceptin) in patients with HER2-positive tumors was closed early because patients receiving trastuzumab had a pCR rate of 65%, compared with 26% in those who did not receive it.33 Expanded clinical trials of this approach are in progress.
The selection of capecitabine (Xeloda) and docetaxel in the present trial was based on the hypothesis that the upregulation of thymidine phosphorylase by docetaxel should increase the activity of capecitabine. 34–36 Single-agent docetaxel in the neoadjuvant setting has yielded pCR rates of 7%–20%.6–8 Treatment with docetaxel and capecitabine together has been reported to produce pCR rates of 10%–21%.37–39 The addition of carboplatin was based on studies by Hurley et al at the University of Miami39– 41 suggesting that platinum salts appeared quite active in the neoadjuvant setting, with the combination of docetaxel and cisplatin producing a pCR rate of 20%, with no residual disease in the breast or axilla.40 Other regimens incorporating cisplatin or carboplatin have pCR rates ranging from 16% to 24%.27,42–44
Patients and methods
Study design
In this phase II multicenter study, patients were assigned to receive docetaxel (30 mg/m2 IV) and carboplatin (AUC 2 IV) on days 1, 8, and 15 of each 28-day cycle plus capecitabine (625 mg/m2 PO) twice daily on days 5–18. The capecitabine dose was based on observations that this dose was effective and relatively nontoxic in metastatic breast cancer (C.L. Vogel, empirical observations). Patients were to receive four cycles prior to surgical resection.
Given that this neoadjuvant regimen was under study, all of the patients were scheduled to receive a proven standard postoperative adjuvant chemotherapy regimen, starting 4–6 weeks postoperatively, with doxorubicin (60 mg/m2 IV) and cyclophosphamide (600 mg/m2 IV) every 21 days for 4 cycles. This sequential design was prompted by studies such as the NSABP B-27 and Aberdeen trials.9,32
Radiation therapy after lumpectomy or mastectomy was given according to individual institution guidelines. Patients with hormone receptor–positive tumors received appropriate antihormonal therapy. Tumor measurements were assessed at baseline and on day 1 of each cycle by physical examination with calipers. No breast or other imaging was required during the period of neoadjuvant chemotherapy or immediately preoperatively. Patients were considered evaluable if they proceeded to surgery after all intended cycles of neoadjuvant chemotherapy or if they developed disease progression during neoadjuvant therapy.
Patients
Eligible patients were men and women regardless of menopausal status ≥ 18 years of age with coreneedle biopsy proven locally advanced or inflammatory breast cancer. Breast cancer characteristics such as estrogen receptor (ER), progesterone receptor (PR), or HER2 status were collected but not used for inclusion/exclusion. Eligible tumors were T2 requiring mastectomy; T3N0–2; T4; and any TN2–3 that by calipers was > 2 cm or with fixed or matted axillary or imaging-detected internal mammary nodes. Patients with prior ductal carcinoma in situ (DCIS) were included, as were those with ≤ T2N0M0 breast cancer > 5 years prior.
Other requirements were an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1; life expectancy > 6 months; negative metastatic workup (bone scan and CT chest/abdomen/pelvis); adequate bone marrow, liver, and kidney function; and peripheral neuropathy ≤ grade 1. All patients of child-bearing potential were required to consent to dual methods of contraception during treatment and for 3 months afterward. A negative pregnancy test was required for these women before treatment, and any suspicion of pregnancy had to be reported to the treating physician.
Study endpoints
The primary endpoint of the study was the in-breast pCR after four cycles of platinum-based neoadjuvant chemotherapy. Pathologic complete response was defined as complete disappearance of invasive and in situ disease or invasive disease alone. During the course of this trial, it became generally acceptable to include patients with only residual DCIS as equivalent to pCR.45
The secondary endpoints were pCR in the lymph nodes; clinical response rate; tolerability; breast conservation; time to disease progression (local, regional, and distant); and OS. Also recorded was minimal residual disease (MRD), which we arbitrarily defined as ≤ 1 cm invasive carcinoma at resection. The overall treatment plan included postoperative AC to provide a standard-of-care regimen to maximize curative potential.
Statistical analysis
Data were analyzed on an intentto- treat basis. Although pCR rates with doxorubicin plus either cyclophosphamide or docetaxel have been < 15%, the studies by Smith et al26 and Hurley et al39 with in-breast pCR rates of at least 20% served as comparators (albeit imprecise).
Applying the min/max statistical design, the procedure tests the null hypothesis H0: P ≤ 0.15 against the alternative hypothesis H1: P ≥ 0.30. The overall level of significance and power for this design are 5% and 80%, respectively. The sample size needed for the first stage was 23 evaluable patients. If three or fewer pCR responses were observed, then the study would be terminated and the treatment regimen would not be investigated further. Otherwise, an additional 25 evaluable patients would be accrued for a total of 48 study patients. If 11 or fewer responses were observed, then the study would be terminated. Otherwise, this treatment regimen would be recommended to proceed to phase III for further investigation.
Tolerability assessment
At each visit, toxicities were assessed and graded according to the National Cancer Institute Common Toxicity Criteria, version 2.46 Two dose reductions were allowed for all drugs.
Ethical considerations
The investigational nature of this study was fully disclosed to each patient. In accordance with institutional and federal guidelines, the patients were guided through and subsequently signed the informed consent approved by the appropriate site Institutional Review Board.
Literature review
The terms “neoadjuvant” and “breast” were used in a literature search on PubMed, with filters “English” and “clinical trials.” Abstracts for each of the 398 results were reviewed We used phase II or III trials with at least 30 patients, at least four cycles of chemotherapy, and clearly defined pCR for comparison to this study.
Results Patients
Between June 2003 and December 2006, 50 women with a median age of 49 years (range, 28–75 years) were enrolled. One patient was ineligible due to preceding lumpectomy. The 49 eligible patients were treated with ≥ 1 cycle of neoadjuvant chemotherapy between June 27, 2003, and April 12, 2007.
The baseline characteristics of the 49 eligible patients are summarized in Table 3. Thirty-one patients (63%) were premenopausal. Twenty patients (41%) were positive for either ER or PR and were negative for HER2. Eight patients (16%) had HER2- positive tumors, and 23 (46%) had triple-negative tumors. At baseline, 22 patients (45%) had clinical lymphadenopathy, and 1 patient (2%) had inflammatory breast cancer.
The 41 patients (83%) who completed all four cycles of therapy were evaluable for response; 8 (16%) were inevaluable due to noncompliance (1), grade 3 or 4 toxicity (5), or withdrawal of consent (2). The following efficacy assessments apply to the 41 evaluable patients, whereas the toxicity assessments include the 49 patients who received at least one full cycle of chemotherapy.
Clinical response
At study onset, of the 49 eligible patients, 38 (78%) had a palpable inbreast tumor (median size, 5.5 cm); 22 (45%) had enlarged nodes, and 34 (69%) had confirmed nodal involvement (by biopsy or imaging). A clinical complete response (cCR) rate in the breast was seen in 23 of 41 (56%) evaluable patients. Of 22 patients with baseline lymphadenopathy (by imaging or physical examination), 13 had axillary assessment by physical examination throughout treatment, with 12 (92%) exhibiting a cCR in the axilla.
Pathologic response
After four cycles of chemotherapy, an in-breast pCR (the primary endpoint) was demonstrated in 6 of 41 patients (15%). One of these six patients had residual DCIS and is listed separately. All of these patients had nodal pCR, whereas overall, 20 patients (49%) had negative nodes at resection.
The pathology reports of two patients were read as having invasive tumor within lymphatics and lymphovascular invasion (one each) with no measurable disease, with tumor thus sized as Tx. Neither of these patients had involved lymph nodes. Fourteen patients (34%) had MRD in the breast, and 8 of these 14 patients (57%) had residual nodal disease. Nine patients (22%) had T1c tumors (> 1–2 cm), with five of these nine patients (55%) having nodal disease. Seven patients (17%) had T2 tumors (> 2–5 cm) tumors, with five of these seven patients (71%) having nodal disease. These findings are summarized in Table 4. The correlation between in-breast cCR and pCR was 26%.
Biologic features of responders
Of interest, five of the six patients with a pCR had triple-negative tumors. This translates to a 22% pCR rate (5 of 23) in the triple-negative subset, and a pCR rate of 6% (1 of 18) in patients with ER-positive and/ or PR-positive tumors. The remaining patient with a pCR had ER-, PR-, and HER2-positive disease.
One patient had inflammatory breast cancer at diagnosis, and another developed this during the course of chemotherapy; the latter patient was removed from the study for progressive disease. Interestingly, the patient who presented with inflammatory breast cancer was one of the six patients with a pCR. Both of these inflammatory disease patients had triple-negative tumors.
Conversion to breast conservation
Breast conservation was offered to patients if it was deemed appropriate by the treating surgeon. Preoperative imaging was not mandated and thus was not routinely performed. Mastectomy was ultimately performed in 4 of the 6 patients (67%) with pCR and in 22 of the 35 patients (63%) with less than a pCR. Thus, the choice for breast conservation did not correlate well with response to chemotherapy.
Time to disease progression
At a median follow-up of 48 months (range, 7–63), 36 of 41 patients (88%) remained free of disease (range, 19–63 months). Two patients had progressive disease while they were on study treatment and had T3 tumors on resection. Another three patients were found to have progressive disease at 10, 41, and 50 months from study day 1.
Of the nine patients with T1c disease, only one patient (who had positive nodes at resection) had a recurrence (at 41 months). Overall, the patients who had a recurrence had MRD (one patient), T1c (one patient), T2 (one patient), and T3 (the same two patients whose disease progressed while they were on treatment and continued to progress after surgery).
Disease-free and overall survival
Three patients were lost to followup, with point of last contact at 19, 34, and 59 months. Of the 41 evaluable patients, 5 patients developed progressive disease, with 2 of these patients progressing during the study treatment. Disease-free survival at 12, 24, and 36 months was 89%, 89%, and 78%, respectively. Overall survival at these same time points was 95%, 90%, and 76%. None of the patients with a pCR is known to have recurrent disease. Of the six patients achieving pCR, two were lost to follow-up after 34 and 59 months, and four continued diseasefree at 38, 39, 55, and 62 months.
Adverse events
Five patients were removed from the study secondary to toxicities. Grade 3 and 4 toxicity events are summarized in Table 5. Grade 3 toxicities were anemia (4), diarrhea (2), epigastric pain (1), fatigue (2), hand-foot syndrome (1), infection (1), leukopenia (9), pain (5), and peripheral sensory neuropathy (1). Grade 4 toxicities were depression (1) and leukopenia (4). Toxicities (all grades) occurring in ≥ 10% of the 49 treated patients were anemia (76%), leukopenia (70%), fatigue (67%), nausea (59%), alopecia (49%), thrombocytopenia (47%), diarrhea (47%), constipation (37%), pain (35%), vomiting (31%), epigastric pain (27%), nail changes (22%), epiphora (22%), hand-foot syndrome (20%), infection (18%), edema (16%), rash (16%), anorexia (16%), and depression (10%). In the intent-to-treat population, there were nine dose reductions among nine patients, and 19 dose delays among 15 patients.
Discussion
The combination of agents tested thus far in the neoadjuvant setting consistently produce pCR rates far less than 50% in unselected populations. This study was begun prior to the widespread use of personalized medicine. Most prior published trials had utilized anthracycline-based chemotherapy, with response rates generally ranging between 7% and 36%.6,9–26,28–31,41,42
The idea of thymidine phosphorylase upregulation by the combination of capecitabine and docetaxel upon which this study was largely based34–36 has since been disputed.47 The primary endpoint of this trial of a novel platinum- based regimen was a pCR rate of 15%. It is significant that 83% of the pCRs were in triple-negative tumors. A secondary endpoint of MRD was calculated, as this was in the original design of the study, but ultimately was not relevant to the primary endpoint.
Ultimately, pCR is the more relevant point of discussion for the modern era. The 15% pCR rate seen in this phase II study was within range of those achieved in numerous other phase II/III neoadjuvant chemotherapy trials with ≥ 25 patients, ≥ 3 cycles of chemotherapy, and pCR defined as absence of carcinoma in the breast and axilla. To date, no patient in our study with a pCR has been noted to have recurrent disease. However, a recently published French study found a 22% recurrence rate at 11 years in patients with triple-negative breast cancer achieving pCR, highlighting the importance of longer-term follow- up.48.
The inclusion of patients with HER2-positive disease in neoadjuvant studies without HER2-targeted therapy was standard at the time that this study was conducted, but is no longer appropriate. If we were to exclude the eight HER2-positive patients from analysis, then there would be only 34 patients evaluable for response, with a pCR rate of 18%. Buzdar et al33 demonstrated a 65% pCR rate in women with HER2-positive disease treated with neoadjuvant chemotherapy plus trastuzumab. The improvement in pCR with the addition of trastuzumab is supported by other confirmatory trials. Authors of a single-arm trial of dose-dense epirubicin and cyclophosphamide followed by dosedense docetaxel and trastuzumab in a HER2-positive population reported a pCR rate of 57%.49 The randomized NOAH study50 achieved a pCR rate of 23% in 115 patients treated with trastuzumab-based chemotherapy.
It is interesting to note that five of six patients (83%) achieving a pCR in our study had triple-negative tumors. Investigators at the University of Miami presented a retrospective review of locally advanced triple-negative breast cancer treated with docetaxel and a platinum salt, with 61% of patients also receiving AC. The authors reported a pCR rate of 34% overall and 40% for patients receiving AC.51 A pCR rate of 60% was noted in the triplenegative subset of patients in another study evaluating docetaxel, doxorubicin, and cyclophosphamide with or without vinorelbine/capecitabine (GeparTrio Study).52 Further, a pCR rate of 72% was achieved with singleagent cisplatin in a group of 25 women with BRCA1 mutations, suggesting, if confirmed by others, that this largely triple- negative population may be exquisitely sensitive to platinum salts.43 In contrast, in a previous study of cisplatin in BRCA mutation carriers, Garber et al44 reported a pCR rate of 22%, suggesting that further trials are needed specifically in BRCA carriers and in triple-negative tumors to see whether these specific patient subsets preferentially derive benefit from platinum salts in the neoadjuvant setting.
The results of the current study are consistent with others indicating a low likelihood of pCR in patients with ERpositive tumors. In fact, none of our ER-positive patients had a pCR. Neoadjuvant endocrine therapy in postmenopausal women with ER- and/ or PR-positive disease is a reasonable treatment option for selected patients, but endpoints other than pCR have often been used.53,54 It is therefore difficult to directly compare these two strategies. Currently, investigators are comparing the three aromatase inhibitors head to head in the neoadjuvant setting for postmenopausal women with hormone receptor–positive tumors.55
The historic pCR ceiling appears to be rising, albeit slowly. Where targets such as HER2 overexpression and triple- negative biology are recognized, progress is being made. Patient eligibility criteria for neoadjuvant breast cancer studies at the time of this trial were quite broad, and it is now recognized that specific subsets of breast cancer respond differently to different classes of agents. Furthermore, our knowledge about breast cancer prognostic markers continues to expand. Had this study been designed in 2011, other data points such as Ki67 would have been collected. A recently published study on neoadjuvant triplenegative breast cancer found that only patients with baseline Ki67% expression > 10% achieved pCR.56
Given the long-term implications of not achieving pCR, optimal treatment of patients in the adjuvant setting is critical. Although neoadjuvantly treated patients with ER-positive or HER2-positive disease go on to receive adjuvant agents (antihormonal therapy for ER-positive disease and trastuzumab for HER2-positive disease), patients with triple-negative disease lack long-term therapies of proven efficacy. Perhaps, as we edge closer to defining the optimal neoadjuvant agents for each subset of patients, this will be less of a concern. Many earlyphase neoadjuvant studies have been conducted, with promising reports, yet the results of larger, randomized trials continue to frustrate both investigators and clinicians. These deficits in care can only be answered by carefully planned randomized clinical trials.
Acknowledgment: Funding for this study was provided by sanofi-aventis, U.S.
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35. Endo M, Shinbori N, Fukase Y, et al. Induction of thymidine phosphorylase expression and enhancement of efficacy of capecitabine or 5´deoxy-5-fluorouridine by cyclophosphamide in mammary tumor models. Int J Cancer 1999;83:127–134.
36. Yamamoto S, Kurebayashi J, Kurosumi M, et al. Combined effects of docetaxel and fluoropyrimidines on tumor growth and expression of interleukin-6 and thymidine phosphorylase in breast cancer xenografts. Cancer Chemother Pharmacol 2001;48:283–288.
37. Lebowitz PF, Eng-Wong J, Swain SM, et al. A phase II trial of neoadjuvant docetaxel and capecitabine for locally advanced breast cancer. Clin Cancer Res 2004;10:6764–6769.
38. Lee KS, Ro J, Nam BH, et al. A randomized phase-III trial of docetaxel/capecitabine versus doxorubicin/cyclophosphamide as primary chemotherapy for patients with stage II/III breast cancer. Breast Cancer Res Treat 2008;109:481–489.
39. Hurley J, Reis I, Silva O, et al. Weekly docetaxel/carboplatin as primary systemic therapy for Her2-negative locally advanced breast cancer. Clin Breast Cancer 2005;6:447–454.
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ABOUT THE AUTHORS
Aruna Mani, MD; Sandra X. Franco, MD; Grace Wang, MD: Neil Abramson, MD; Lee S. Schwartzberg, MD: James Jakub, MD; Elizabeth Tan-Chiu, MD: Alisha Stein, RNC, BSN, OCN; Alejandra T. Perez, MD; and Charles L Vogel, MD.
Affiliations: Dr. Mani is a breast medical oncologist at Memorial Cancer Institute, Pembroke Pines, FL. Dr. Franco is now Chief of Oncology at the Oncology Center, Clinica del Country, Bogota, Colombia. Dr. Wang is an oncologist at Advanced Medical Specialties, Miami, FL. Dr. Abramson is Clinical Professor of Medicine and Emeritus Director of Education and Research at Baptist Cancer Institute, University of Florida, Jacksonville, FL. Dr. Schwartzberg is Medical Director of The West Clinic, Memphis, TN. Dr. Jakub is now Assistant Professor of Surgery, Division of Gastroenterology and General Surgery, Mayo Clinic, Rochester, MN. Dr. Tan-Chiu is Medical Director of Florida Cancer Care, Davie, FL. Dr. Schwartz is Principal Investigator at Mount Sinai Medical Center, Miami Beach, FL. Ms. Frankel is Director of Oncology Clinical Research and Development at Memorial Cancer Institute, Hollywood, FL. Dr. Krill-Jackson is an oncologist at Mount Sinai Comprehensive Cancer Center, Miami, FL. Ms. Stein is now Oncology Clinical Coordinator at Genentech Inc., Fort Lauderdale, FL. Dr. Perez is Director of the Breast Cancer Center at Memorial Cancer Institute, Hollywood, FL. Dr. Vogel is Professor of Clinical Medicine and Director of the Women’s Center, Sylvester Comprehensive Cancer Center, Deerfield Beach, FL.
Conflicts of interest: Dr. Vogel has served as an advisor and is a member of the speakers’ bureaus of sanofi-aventis U.S. and Roche, as well as many other companies whose products were not part of the current study plan. The other authors have no pertinent conflicts of interest to disclose.
The standard of care for locally advanced breast cancer (LABC) is neoadjuvant chemotherapy,1 with LABC including clinical stages IIA, IIB, and IIIA. The goals of preoperative chemotherapy are to downstage so as to render breast conservation feasible, to eradicate disease in the axillary nodes, and to allow in vivo testing of tumor drug sensitivity, all with the ultimate aim of improving prognosis. Clinical trials have demonstrated that the pathologic in-breast response generally correlates with pathologic response in the lymph nodes. Furthermore, nodal status at the time of surgery correlates with overall survival (OS) and disease-free survival (DFS).2,3 A combined analysis of two large prospective neoadjuvant chemotherapy trials demonstrated significantly higher 5-year OS and DFS in patients achieving in-breast pathologic complete response (pCR), compared with those who did not (OS, 89% vs 64%; DFS, 87% vs 58%, respectively).4
At the start of this trial, the most effective neoad- juvant regimen remained in question. Even now, National Comprehensive Cancer Center guidelines suggest that any recommended adjuvant regimen can be used in the neoadjuvant setting.1 Numerous phase II and III trials have evaluated single-agent5–8 and combination9– 32 chemotherapies, most of which are anthracycline- based, with pCR rates reported between 7% and 36%. In the NSABP-B27 study, patients treated preoperatively with four cycles of doxorubicin and cyclophosphamide (AC) followed by four cycles of docetaxel (Taxotere) had a 26% pCR rate versus a 13% pCR rate in those receiving preoperative AC and postoperative docetaxel. Despite the doubling of pCR with neoadjuvant docetaxel, there was no difference in DFS or OS.9 However, as reported by Kuerer et al, patients achieving a pCR after completion of neoadjuvant chemotherapy appeared to have superior survival.4
Many previous trials (including the study reported here) did not exclude patients with human epidermal growth factor receptor 2 (HER2)-positive disease. It is now well established that such patients should be treated with neoadjuvant regimens incorporating HER2-targeted therapy. In fact, an early neoadjuvant study of paclitaxel followed by fluorouracil, epirubicin, and cyclophosphamide with or without 24 weeks of concurrent trastuzumab (Herceptin) in patients with HER2-positive tumors was closed early because patients receiving trastuzumab had a pCR rate of 65%, compared with 26% in those who did not receive it.33 Expanded clinical trials of this approach are in progress.
The selection of capecitabine (Xeloda) and docetaxel in the present trial was based on the hypothesis that the upregulation of thymidine phosphorylase by docetaxel should increase the activity of capecitabine. 34–36 Single-agent docetaxel in the neoadjuvant setting has yielded pCR rates of 7%–20%.6–8 Treatment with docetaxel and capecitabine together has been reported to produce pCR rates of 10%–21%.37–39 The addition of carboplatin was based on studies by Hurley et al at the University of Miami39– 41 suggesting that platinum salts appeared quite active in the neoadjuvant setting, with the combination of docetaxel and cisplatin producing a pCR rate of 20%, with no residual disease in the breast or axilla.40 Other regimens incorporating cisplatin or carboplatin have pCR rates ranging from 16% to 24%.27,42–44
Patients and methods
Study design
In this phase II multicenter study, patients were assigned to receive docetaxel (30 mg/m2 IV) and carboplatin (AUC 2 IV) on days 1, 8, and 15 of each 28-day cycle plus capecitabine (625 mg/m2 PO) twice daily on days 5–18. The capecitabine dose was based on observations that this dose was effective and relatively nontoxic in metastatic breast cancer (C.L. Vogel, empirical observations). Patients were to receive four cycles prior to surgical resection.
Given that this neoadjuvant regimen was under study, all of the patients were scheduled to receive a proven standard postoperative adjuvant chemotherapy regimen, starting 4–6 weeks postoperatively, with doxorubicin (60 mg/m2 IV) and cyclophosphamide (600 mg/m2 IV) every 21 days for 4 cycles. This sequential design was prompted by studies such as the NSABP B-27 and Aberdeen trials.9,32
Radiation therapy after lumpectomy or mastectomy was given according to individual institution guidelines. Patients with hormone receptor–positive tumors received appropriate antihormonal therapy. Tumor measurements were assessed at baseline and on day 1 of each cycle by physical examination with calipers. No breast or other imaging was required during the period of neoadjuvant chemotherapy or immediately preoperatively. Patients were considered evaluable if they proceeded to surgery after all intended cycles of neoadjuvant chemotherapy or if they developed disease progression during neoadjuvant therapy.
Patients
Eligible patients were men and women regardless of menopausal status ≥ 18 years of age with coreneedle biopsy proven locally advanced or inflammatory breast cancer. Breast cancer characteristics such as estrogen receptor (ER), progesterone receptor (PR), or HER2 status were collected but not used for inclusion/exclusion. Eligible tumors were T2 requiring mastectomy; T3N0–2; T4; and any TN2–3 that by calipers was > 2 cm or with fixed or matted axillary or imaging-detected internal mammary nodes. Patients with prior ductal carcinoma in situ (DCIS) were included, as were those with ≤ T2N0M0 breast cancer > 5 years prior.
Other requirements were an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1; life expectancy > 6 months; negative metastatic workup (bone scan and CT chest/abdomen/pelvis); adequate bone marrow, liver, and kidney function; and peripheral neuropathy ≤ grade 1. All patients of child-bearing potential were required to consent to dual methods of contraception during treatment and for 3 months afterward. A negative pregnancy test was required for these women before treatment, and any suspicion of pregnancy had to be reported to the treating physician.
Study endpoints
The primary endpoint of the study was the in-breast pCR after four cycles of platinum-based neoadjuvant chemotherapy. Pathologic complete response was defined as complete disappearance of invasive and in situ disease or invasive disease alone. During the course of this trial, it became generally acceptable to include patients with only residual DCIS as equivalent to pCR.45
The secondary endpoints were pCR in the lymph nodes; clinical response rate; tolerability; breast conservation; time to disease progression (local, regional, and distant); and OS. Also recorded was minimal residual disease (MRD), which we arbitrarily defined as ≤ 1 cm invasive carcinoma at resection. The overall treatment plan included postoperative AC to provide a standard-of-care regimen to maximize curative potential.
Statistical analysis
Data were analyzed on an intentto- treat basis. Although pCR rates with doxorubicin plus either cyclophosphamide or docetaxel have been < 15%, the studies by Smith et al26 and Hurley et al39 with in-breast pCR rates of at least 20% served as comparators (albeit imprecise).
Applying the min/max statistical design, the procedure tests the null hypothesis H0: P ≤ 0.15 against the alternative hypothesis H1: P ≥ 0.30. The overall level of significance and power for this design are 5% and 80%, respectively. The sample size needed for the first stage was 23 evaluable patients. If three or fewer pCR responses were observed, then the study would be terminated and the treatment regimen would not be investigated further. Otherwise, an additional 25 evaluable patients would be accrued for a total of 48 study patients. If 11 or fewer responses were observed, then the study would be terminated. Otherwise, this treatment regimen would be recommended to proceed to phase III for further investigation.
Tolerability assessment
At each visit, toxicities were assessed and graded according to the National Cancer Institute Common Toxicity Criteria, version 2.46 Two dose reductions were allowed for all drugs.
Ethical considerations
The investigational nature of this study was fully disclosed to each patient. In accordance with institutional and federal guidelines, the patients were guided through and subsequently signed the informed consent approved by the appropriate site Institutional Review Board.
Literature review
The terms “neoadjuvant” and “breast” were used in a literature search on PubMed, with filters “English” and “clinical trials.” Abstracts for each of the 398 results were reviewed We used phase II or III trials with at least 30 patients, at least four cycles of chemotherapy, and clearly defined pCR for comparison to this study.
Results Patients
Between June 2003 and December 2006, 50 women with a median age of 49 years (range, 28–75 years) were enrolled. One patient was ineligible due to preceding lumpectomy. The 49 eligible patients were treated with ≥ 1 cycle of neoadjuvant chemotherapy between June 27, 2003, and April 12, 2007.
The baseline characteristics of the 49 eligible patients are summarized in Table 3. Thirty-one patients (63%) were premenopausal. Twenty patients (41%) were positive for either ER or PR and were negative for HER2. Eight patients (16%) had HER2- positive tumors, and 23 (46%) had triple-negative tumors. At baseline, 22 patients (45%) had clinical lymphadenopathy, and 1 patient (2%) had inflammatory breast cancer.
The 41 patients (83%) who completed all four cycles of therapy were evaluable for response; 8 (16%) were inevaluable due to noncompliance (1), grade 3 or 4 toxicity (5), or withdrawal of consent (2). The following efficacy assessments apply to the 41 evaluable patients, whereas the toxicity assessments include the 49 patients who received at least one full cycle of chemotherapy.
Clinical response
At study onset, of the 49 eligible patients, 38 (78%) had a palpable inbreast tumor (median size, 5.5 cm); 22 (45%) had enlarged nodes, and 34 (69%) had confirmed nodal involvement (by biopsy or imaging). A clinical complete response (cCR) rate in the breast was seen in 23 of 41 (56%) evaluable patients. Of 22 patients with baseline lymphadenopathy (by imaging or physical examination), 13 had axillary assessment by physical examination throughout treatment, with 12 (92%) exhibiting a cCR in the axilla.
Pathologic response
After four cycles of chemotherapy, an in-breast pCR (the primary endpoint) was demonstrated in 6 of 41 patients (15%). One of these six patients had residual DCIS and is listed separately. All of these patients had nodal pCR, whereas overall, 20 patients (49%) had negative nodes at resection.
The pathology reports of two patients were read as having invasive tumor within lymphatics and lymphovascular invasion (one each) with no measurable disease, with tumor thus sized as Tx. Neither of these patients had involved lymph nodes. Fourteen patients (34%) had MRD in the breast, and 8 of these 14 patients (57%) had residual nodal disease. Nine patients (22%) had T1c tumors (> 1–2 cm), with five of these nine patients (55%) having nodal disease. Seven patients (17%) had T2 tumors (> 2–5 cm) tumors, with five of these seven patients (71%) having nodal disease. These findings are summarized in Table 4. The correlation between in-breast cCR and pCR was 26%.
Biologic features of responders
Of interest, five of the six patients with a pCR had triple-negative tumors. This translates to a 22% pCR rate (5 of 23) in the triple-negative subset, and a pCR rate of 6% (1 of 18) in patients with ER-positive and/ or PR-positive tumors. The remaining patient with a pCR had ER-, PR-, and HER2-positive disease.
One patient had inflammatory breast cancer at diagnosis, and another developed this during the course of chemotherapy; the latter patient was removed from the study for progressive disease. Interestingly, the patient who presented with inflammatory breast cancer was one of the six patients with a pCR. Both of these inflammatory disease patients had triple-negative tumors.
Conversion to breast conservation
Breast conservation was offered to patients if it was deemed appropriate by the treating surgeon. Preoperative imaging was not mandated and thus was not routinely performed. Mastectomy was ultimately performed in 4 of the 6 patients (67%) with pCR and in 22 of the 35 patients (63%) with less than a pCR. Thus, the choice for breast conservation did not correlate well with response to chemotherapy.
Time to disease progression
At a median follow-up of 48 months (range, 7–63), 36 of 41 patients (88%) remained free of disease (range, 19–63 months). Two patients had progressive disease while they were on study treatment and had T3 tumors on resection. Another three patients were found to have progressive disease at 10, 41, and 50 months from study day 1.
Of the nine patients with T1c disease, only one patient (who had positive nodes at resection) had a recurrence (at 41 months). Overall, the patients who had a recurrence had MRD (one patient), T1c (one patient), T2 (one patient), and T3 (the same two patients whose disease progressed while they were on treatment and continued to progress after surgery).
Disease-free and overall survival
Three patients were lost to followup, with point of last contact at 19, 34, and 59 months. Of the 41 evaluable patients, 5 patients developed progressive disease, with 2 of these patients progressing during the study treatment. Disease-free survival at 12, 24, and 36 months was 89%, 89%, and 78%, respectively. Overall survival at these same time points was 95%, 90%, and 76%. None of the patients with a pCR is known to have recurrent disease. Of the six patients achieving pCR, two were lost to follow-up after 34 and 59 months, and four continued diseasefree at 38, 39, 55, and 62 months.
Adverse events
Five patients were removed from the study secondary to toxicities. Grade 3 and 4 toxicity events are summarized in Table 5. Grade 3 toxicities were anemia (4), diarrhea (2), epigastric pain (1), fatigue (2), hand-foot syndrome (1), infection (1), leukopenia (9), pain (5), and peripheral sensory neuropathy (1). Grade 4 toxicities were depression (1) and leukopenia (4). Toxicities (all grades) occurring in ≥ 10% of the 49 treated patients were anemia (76%), leukopenia (70%), fatigue (67%), nausea (59%), alopecia (49%), thrombocytopenia (47%), diarrhea (47%), constipation (37%), pain (35%), vomiting (31%), epigastric pain (27%), nail changes (22%), epiphora (22%), hand-foot syndrome (20%), infection (18%), edema (16%), rash (16%), anorexia (16%), and depression (10%). In the intent-to-treat population, there were nine dose reductions among nine patients, and 19 dose delays among 15 patients.
Discussion
The combination of agents tested thus far in the neoadjuvant setting consistently produce pCR rates far less than 50% in unselected populations. This study was begun prior to the widespread use of personalized medicine. Most prior published trials had utilized anthracycline-based chemotherapy, with response rates generally ranging between 7% and 36%.6,9–26,28–31,41,42
The idea of thymidine phosphorylase upregulation by the combination of capecitabine and docetaxel upon which this study was largely based34–36 has since been disputed.47 The primary endpoint of this trial of a novel platinum- based regimen was a pCR rate of 15%. It is significant that 83% of the pCRs were in triple-negative tumors. A secondary endpoint of MRD was calculated, as this was in the original design of the study, but ultimately was not relevant to the primary endpoint.
Ultimately, pCR is the more relevant point of discussion for the modern era. The 15% pCR rate seen in this phase II study was within range of those achieved in numerous other phase II/III neoadjuvant chemotherapy trials with ≥ 25 patients, ≥ 3 cycles of chemotherapy, and pCR defined as absence of carcinoma in the breast and axilla. To date, no patient in our study with a pCR has been noted to have recurrent disease. However, a recently published French study found a 22% recurrence rate at 11 years in patients with triple-negative breast cancer achieving pCR, highlighting the importance of longer-term follow- up.48.
The inclusion of patients with HER2-positive disease in neoadjuvant studies without HER2-targeted therapy was standard at the time that this study was conducted, but is no longer appropriate. If we were to exclude the eight HER2-positive patients from analysis, then there would be only 34 patients evaluable for response, with a pCR rate of 18%. Buzdar et al33 demonstrated a 65% pCR rate in women with HER2-positive disease treated with neoadjuvant chemotherapy plus trastuzumab. The improvement in pCR with the addition of trastuzumab is supported by other confirmatory trials. Authors of a single-arm trial of dose-dense epirubicin and cyclophosphamide followed by dosedense docetaxel and trastuzumab in a HER2-positive population reported a pCR rate of 57%.49 The randomized NOAH study50 achieved a pCR rate of 23% in 115 patients treated with trastuzumab-based chemotherapy.
It is interesting to note that five of six patients (83%) achieving a pCR in our study had triple-negative tumors. Investigators at the University of Miami presented a retrospective review of locally advanced triple-negative breast cancer treated with docetaxel and a platinum salt, with 61% of patients also receiving AC. The authors reported a pCR rate of 34% overall and 40% for patients receiving AC.51 A pCR rate of 60% was noted in the triplenegative subset of patients in another study evaluating docetaxel, doxorubicin, and cyclophosphamide with or without vinorelbine/capecitabine (GeparTrio Study).52 Further, a pCR rate of 72% was achieved with singleagent cisplatin in a group of 25 women with BRCA1 mutations, suggesting, if confirmed by others, that this largely triple- negative population may be exquisitely sensitive to platinum salts.43 In contrast, in a previous study of cisplatin in BRCA mutation carriers, Garber et al44 reported a pCR rate of 22%, suggesting that further trials are needed specifically in BRCA carriers and in triple-negative tumors to see whether these specific patient subsets preferentially derive benefit from platinum salts in the neoadjuvant setting.
The results of the current study are consistent with others indicating a low likelihood of pCR in patients with ERpositive tumors. In fact, none of our ER-positive patients had a pCR. Neoadjuvant endocrine therapy in postmenopausal women with ER- and/ or PR-positive disease is a reasonable treatment option for selected patients, but endpoints other than pCR have often been used.53,54 It is therefore difficult to directly compare these two strategies. Currently, investigators are comparing the three aromatase inhibitors head to head in the neoadjuvant setting for postmenopausal women with hormone receptor–positive tumors.55
The historic pCR ceiling appears to be rising, albeit slowly. Where targets such as HER2 overexpression and triple- negative biology are recognized, progress is being made. Patient eligibility criteria for neoadjuvant breast cancer studies at the time of this trial were quite broad, and it is now recognized that specific subsets of breast cancer respond differently to different classes of agents. Furthermore, our knowledge about breast cancer prognostic markers continues to expand. Had this study been designed in 2011, other data points such as Ki67 would have been collected. A recently published study on neoadjuvant triplenegative breast cancer found that only patients with baseline Ki67% expression > 10% achieved pCR.56
Given the long-term implications of not achieving pCR, optimal treatment of patients in the adjuvant setting is critical. Although neoadjuvantly treated patients with ER-positive or HER2-positive disease go on to receive adjuvant agents (antihormonal therapy for ER-positive disease and trastuzumab for HER2-positive disease), patients with triple-negative disease lack long-term therapies of proven efficacy. Perhaps, as we edge closer to defining the optimal neoadjuvant agents for each subset of patients, this will be less of a concern. Many earlyphase neoadjuvant studies have been conducted, with promising reports, yet the results of larger, randomized trials continue to frustrate both investigators and clinicians. These deficits in care can only be answered by carefully planned randomized clinical trials.
Acknowledgment: Funding for this study was provided by sanofi-aventis, U.S.
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ABOUT THE AUTHORS
Aruna Mani, MD; Sandra X. Franco, MD; Grace Wang, MD: Neil Abramson, MD; Lee S. Schwartzberg, MD: James Jakub, MD; Elizabeth Tan-Chiu, MD: Alisha Stein, RNC, BSN, OCN; Alejandra T. Perez, MD; and Charles L Vogel, MD.
Affiliations: Dr. Mani is a breast medical oncologist at Memorial Cancer Institute, Pembroke Pines, FL. Dr. Franco is now Chief of Oncology at the Oncology Center, Clinica del Country, Bogota, Colombia. Dr. Wang is an oncologist at Advanced Medical Specialties, Miami, FL. Dr. Abramson is Clinical Professor of Medicine and Emeritus Director of Education and Research at Baptist Cancer Institute, University of Florida, Jacksonville, FL. Dr. Schwartzberg is Medical Director of The West Clinic, Memphis, TN. Dr. Jakub is now Assistant Professor of Surgery, Division of Gastroenterology and General Surgery, Mayo Clinic, Rochester, MN. Dr. Tan-Chiu is Medical Director of Florida Cancer Care, Davie, FL. Dr. Schwartz is Principal Investigator at Mount Sinai Medical Center, Miami Beach, FL. Ms. Frankel is Director of Oncology Clinical Research and Development at Memorial Cancer Institute, Hollywood, FL. Dr. Krill-Jackson is an oncologist at Mount Sinai Comprehensive Cancer Center, Miami, FL. Ms. Stein is now Oncology Clinical Coordinator at Genentech Inc., Fort Lauderdale, FL. Dr. Perez is Director of the Breast Cancer Center at Memorial Cancer Institute, Hollywood, FL. Dr. Vogel is Professor of Clinical Medicine and Director of the Women’s Center, Sylvester Comprehensive Cancer Center, Deerfield Beach, FL.
Conflicts of interest: Dr. Vogel has served as an advisor and is a member of the speakers’ bureaus of sanofi-aventis U.S. and Roche, as well as many other companies whose products were not part of the current study plan. The other authors have no pertinent conflicts of interest to disclose.
A Pilot Trial of Decision Aids to Give Truthful Prognostic and Treatment Information to Chemotherapy Patients with Advanced Cancer
Original research
Thomas J. Smith MD, a,
Abstract
Most cancer patients do not have an explicit discussion about prognosis and treatment despite documented adverse outcomes. Few decision aids have been developed to assist the difficult discussions of palliative management. We developed decision aids for people with advanced incurable breast, colorectal, lung, and hormone-refractory prostate cancers facing first-, second-, third-, and fourth-line chemotherapy. We recruited patients from our urban oncology clinic after gaining the permission of their treating oncologist. We measured knowledge of curability and treatment benefit before and after the intervention. Twenty-six of 27 (96%) patients completed the aids, with a mean age of 63, 56% female, 56% married, 56% African American, and 67% with a high school education or more. Most patients (14/27, 52%) thought a person with their advanced cancer could be cured, which was reduced (to 8/26, 31%, P = 0.15) after the decision aid. Nearly all overestimated the effect of palliative chemotherapy. No distress was noted, and hope did not change. The majority (20/27, 74%) found the information helpful to them, and almost all (25/27, 93%) wanted to share the information with their family and physicians. It is possible to give incurable patients their prognosis, treatment options, and options for improving end-of-life care without causing distress or lack of hope. Almost all find the information helpful and want to share it with doctors and family. Research is needed to test the findings in a larger sample and measure the outcomes of truthful information on quality of life, quality of care, and costs.
Article Outline
We designed decision aids for patients with incurable cancer and attempted to determine if people would opt for full disclosure about prognosis and treatment. If they opted for full disclosure, we assessed current knowledge about chance of cure, survival, disease response rates, and symptom control, before and after. This pilot trial was done to see if patients would complete a decision aid about their advanced cancer, even if it contained truthful information about their limited prognosis and treatment benefits.
Methods
We created state-of-the-art tables of information for patients with advanced breast, lung, colon, and hormone-refractory prostate cancers, based on expert review, external review, and comparison with Up To Date© (available from the authors). The information was approved by all three oncologists involved. We used bar graphs to illustrate benefit, developed for patient education graphs for a randomized study of insurance types and treatment choices9 and in common use on the Web site Adjuvant Online (www.adjuvantonline.org).[10] and [11] It is similar to what we do with the written medical record, a concise review of diagnosis, prognosis, treatment options, side effects, and when to call the doctor.12
We tested the intervention in a heterogeneous sample of 27 patients recruited through the Dalton Oncology Clinic, which serves a mix of patients from the most discerning third-opinion clinical trial patient to the community cancer patient and provides most of the indigent cancer care in the central Virginia area. The study was done within 3 months in early 2009.
Our primary outcome was the number of patients who would opt for full disclosure once they viewed the decision aid. Our secondary outcomes included the following: the amount of information patients have about cure, response rates, and symptom control; the impact of truthful information on hope, as measured by the Herth Hope Index©13 (HHI) used to assess hope in clinical studies of adults;14 whether the information was deemed helpful to the patient; and whether the patient intended to share the information with a doctor.
Patients were accrued by reviewing the daily clinic list to find patients on treatment for incurable breast, colorectal, non-small-cell lung, or hormone-refractory prostate cancer. Treating oncologists were made aware of the study through e-mail, announcements, the Massey Cancer Center Web site, and individual meetings. All oncologists approached agreed to their chemotherapy patients participating in the study in general, and the primary nurse or treating oncologist was contacted about each eligible patient. Eligible patients were not contacted about the study when the treating oncologist or primary oncology nurse determined that a patient was experiencing significant distress or had significant psychiatric problems or difficulty with adjustment to illness or believed the patient would have great emotional difficulty with the information. The number of patients excluded by each oncologist due to concern about distress was estimated to be less than 10% of the total available but was not measured. Since these patients were not enrolled in the study, we did not collect information about them. A clinical psychologist and a chaplain were available to any patient who experienced distress during or after the interview process. The interview questions and intervention were administered by a member of the study team who was not the patient's oncologist or involved in his or her care. The interview team included a graduate student who was also a minister and chaplain (E. A. V.), a medical student with special training in empathic communication (L. A. D.), and/or the principal investigator (T. J. S.); usually one interviewer was present (L. A. D. or E. A. V.).
The interview sequence included screening questions to ensure that the patient wanted full information, sociodemographic questions, a pretest about the chance of cure and treatment effect for a patient with their illness, and the HHI. Next, the decision aid was administered. Immediately afterward, the patient completed a posttest, the HHI, and information about how he or she would use the information.
We modeled this approach on the Ottawa Decision Support Framework, a clinically tested decision-making tool designed to inform decisional conflict,[15] and [16] defined as uncertainty about which course of action to take when the choice involves balancing gain, risk, loss, regrets, or challenges to personal life.17
Our study was approved by the Massey Cancer Center Protocol Review and Monitoring System and the VCU Institutional Review Board for the Conduct of Human Research. Because it was not a clinical trial, no clinical trial registration was required.
Results
The patients were typical for our urban, tertiary referral, and safety net hospital and National Cancer Institute–designated cancer center, as shown in Table 1.
| Age (years) | |
| Mean | 63 ± 5 |
| Range | 46–74 |
| Gender | |
| Male | 12 (44%) |
| Female | 15 (56%) |
| Marital status | |
| Married or committed relationship | 15 (56%) |
| Divorced | 6 (22%) |
| Widowed | 2 (7%) |
| Single/never married | 2 (7%) |
| Ethnicity | |
| Caucasian | 12 (44%) |
| African American | 15 (56%) |
| Education completed | |
| Less than high school | 5 (9%) |
| Some high school | 1 (4%) |
| HS diploma/GED | 8 (30%) |
| Some college | 10 (37%) |
| Completed college | 1 (4%) |
| Completed postgrad | 2 (7%) |
| Total household income | |
| <$15,000 | 10 (37%) |
| $15,000–$34,999 | 8 (30%) |
| $35,000–$74,999 | 6 (22%) |
| >$75,000 | 1 (4%) |
| Don't know | 2 (7%) |
| Average number of people in household | 2.3 (SD 0.9) |
| Type of cancer and line of chemotherapy | |
| Breast 1st, 2nd, 3rd, 4th line | 5, 2, 1,1 = 9 total |
| Colorectal 1st, 2nd, 3rd, 4th line | 8, 5, 1, 0 = 14 total |
| Lung 1st, 2nd, 3rd, 4th line | 2, 0, 0, 0 = 2 total |
| Hormone-refractory prostate 1st, 2nd, 3rd, 4th line | 1, 1, 0, 0 = 2 |
Primary Outcome
Our primary outcome was to assess if patients would complete a decision aid with full disclosure. Of 27 patients, only one (4%) chose not to complete the decision aid after starting. She was a 55-year-old African American woman who had recently started first-line treatment for metastatic colorectal cancer. She had been told at another institution that she had lost too much weight and was too ill to benefit from chemotherapy, but with counseling she regained the weight and had a performance status of 2 at VCU. In her pretest, she answered that she thought a woman with metastatic colorectal cancer spread to bones and lymph glands could be cured, with a chance of cure of 50%. Once presented with the information (good treatments that prolong life and control symptoms but no chance of cure and 9% of patients with metastatic colorectal cancer alive at 5 years), she said that she did not want to finish the questions. She did complete her HHI, which did not change, and was not distressed (see Table 2, patient 12).
| PATIENT | SITUATION (1ST-, 2ND-, 3RD-, 4TH-LINE CHEMOTHERAPY) | COMMENT |
|---|---|---|
| 1 | CRC, 1st | “Feel little bit better.” “Didn't upset.” |
| 2 | BC, 2nd | “It gave me information on my condition.” |
| 3 | BC, 4th | “If I've got 6 months to live, I want to know so I can party” |
| 3 | LC, 1st | “It let me know I have longer than a year, possibly longer than that. Helpful.” |
| 5 | PC, 2nd | “Well, Dr. R said it couldn't be cured … I've done well for 16 months so far.” |
| 6 | CRC, 2nd | “We've already discussed everything. All information I think is helpful.” |
| 7 | CRC, 1st | “… happy that my life expectancy might be better than I thought.” At the end, he said “how good it was to talk and not hold things in.” |
| 8 | CRC, 3rd | “Verification of what I have been told.” |
| 9 | CRC, 2nd | “I know all this, but it was helpful. Especially for people who haven't heard it.” |
| 10 | CRC, 1st | |
| 11 | CRC, 2nd | “Helpful … just to think about my goals and that kind of thing.” |
| 12 | CRC, 1st | “Wouldn't know [about cure]. I can't answer those … [questions about cure rates, response rates after reviewing data]. Tell them to give people hope, not take away hope … not to 'just go smell the roses.' ” |
| 13 | CRC, 1st | “There were some things I didn't know—I didn't know about the 1–2 years—I'm not going to accept it though—I'm planning on more.” |
| 14 | BC, 1st | “Gave me info based on stats that I didn't know before.” |
| 15 | BC, 1st | “It's hard to explain. It's about what I have already known.” |
| 16 | BC, 2nd | “Helped me to understand … . That chemo is better than not having chemo.” |
| 17 | CRC, 1st | |
| 18 | CRC, 2nd | “Helpful to know what will happen, given strength, how to feel about things … to get to talk about things.” |
| 19 | BC, 1st | “Helpful. It opened my eyes, made me aware. I would want to know that.” |
| 20 | BC, 1st | Helpful. “It gave me a lot to think about. A whole lot of it I didn't know about.” |
| 21 | CRC, 1st | Helpful. “Knowing that I was doing something to help someone else. It made me think about what I have to look forward to in life.” |
| 22 | CRC, 2nd | “In a way, you're saying what the possibilities are. I just hope that I keep on trucking.” |
| 23 | BC, 3rd | “Always helpful to discuss prognosis.” |
| 24 | LC, 1st | “Helpful to know what chances I get, with or without (chemotherapy) treatment.” |
| 25 | PC, 1st | “Because the odds are a hell of a lot less than I thought, it's a bummer.” |
| 26 | BC, 1st | “It gave me a chance to see the percentage of things with breast cancer. I have a better understanding of the time line.” |
| 26 | CRC, 1st | “Made me understand some things.” On change in survival from >3 years to “don't know,” “I hope to live a right good while.” |
BC = breast cancer; CRC = colon or rectal cancer; LC = non-small-cell lung cancer; PC = hormone-refractory prostate cancer
In the pretest, almost all the patients, including the patient above, reported wanting full disclosure about cancer, prognosis, treatment, and side effects. In response to questions beginning “How much do you want to know about …” 27 of 27 answered “Tell me all” to the questions about “your cancer,” “your prognosis,” “treatment benefits,” and “treatment side effects.” Only one of 27 answered otherwise: “Tell me a little” about cancer, and “Tell me some” about prognosis.
Secondary Outcomes
Participants were overoptimistic about the results of palliative chemotherapy, as shown in Table 3. Most (14/27, 52%) people thought a person with “metastatic cancer (breast, colorectal, lung, prostate—specific to that person's disease) spread to the bones and lymph glands” could be cured. After the decision aid, more people recognized that their cancer could not be cured (17/25, 63%) but eight of 25 (32%, P = 0.15, Fischer's exact test) still thought a person with metastatic disease could be cured. Patients were particularly overoptimistic about the chance of their symptoms being helped by chemotherapy: 87% thought their symptoms would be helped by chemotherapy, and 60% thought a patient would have at least 50% shrinkage of their cancer before the exercise, which declined only slightly after the decision aid. (While the correct answer varies by disease, the number helped by chemotherapy is usually less than 50%, and response rates are always less than 50%.)
| PRE | POST | CHANGE | COMMENT | |
|---|---|---|---|---|
| Can this person with cancer in the bones and lymph nodes be cured by medical treatment? | Yes = 14 No = 11 Don't know = 2 | Yes = 8 No = 17 Don't know = 2 | Changed from yes to no = 6; changed from no to yes = 1 | Correct answer “no” P = 0.15 Fischer's exact test |
| 52.5 ± 32 | 47 ± 26 | −5.8 ± 28 | The correct answer is 0%; all overoptimistic | |
| What is the chance of her _____ cancer shrinking by half? In % | 60 ± 32 | 57.5 ± 17.6 | −4.2 ± 28 | All overoptimistic |
| What is the chance of _____ cancer symptoms being helped? In % | 87 ± 19 | 74.2 ± 21 | −6.7 ± 26 | All overoptimistic |
| How long does the average person live with advanced ____ cancer (using the choices from the breast cancer sheet for example)? | More realistic, but 2 people increased their expected length of survival | |||
| More than 3 years | 18 | 14 | −4 | |
| About 2 years | 6 | 11 | 5 | |
| About 6 months | 0 | 0 | 0 | |
| Just a few weeks | 1 | 0 | −1 | |
| Don't know/NA | 2 | 2 | 2 | |
| Distress observed by interviewer, nurse, or oncologist | No | No |
Categorical variables Yes and No analyzed by Fischer's exact test
Numerical variables analyzed by Student's t-test, unpaired; none significant
There was no change in responses to the HHI after the intervention as we have previously reported.18 Participants did not appear to be visibly distressed by the intervention. A psychologist and chaplain were made available, but no one requested their services. In our small clinic, the primary nurses and doctors have frequent interactions during visits and chemotherapy. No patient was reported to be distressed in any way, during that visit or subsequent visits.
The comments recorded by the patients or the interviewers at the end of the exercise showed that most patients would share the information, as shown in Table 4.
| Will you share it with anyone? | Yes = 20 No = 6 NA = 1 |
| If so, who? __ My family __ My oncologist __ My oncology nurse __ My primary care doctor __ Other ______ | All (family, ONC, PCP) = 14 Family only = 2 Oncologist = 12 PCP = 14, one said “not PCP” |
| Was this patient information sheet helpful to you? | Yes = 25 No = 1, “Bummer” NA = 2 |
NA = no answer; ONC = oncologist; PCP = primary care provider
In some cases, the average prognosis and treatment benefit, although small, was bigger than the person thought before the exercise. Nearly all found it helpful. Some illustrative comments are shown in Table 2.
We did not formally measure the time to complete the screening questions, pre- and posttests, pre- and post-HHI, and decision aid; but in most cases it took less than 20 minutes to complete the whole package including the pre- and post-tests. Review of the decision aid with the patient always took less than 5 minutes, even when we were reading it with the patient and family. This is consistent with work showing that oncologists state that completing an advance directive will take too much time but, in fact, it takes less than 10 minutes.[19] and [20]
Discussion
Historical data show that patients know little about their prognosis and the effect that treatment will have on their cancer. Yet, this knowledge is essential to making informed choices about treatment benefits, risks, and even costs. When tested in randomized controlled trials, decision aids led to more involvement in decision making.[21] and [22] However, there were no decision aids available about metastatic incurable disease, despite some promising early starts[23], [24], [25], [26], [27] and [28] and only one about first-line treatment,29 so we made a simple one. A successful decision aid may allow patients to discuss their situations with their physicians and develop management strategies that best concur with personal goals and preferences and help patients make plans in other areas of life.
Our findings suggest that most people do want honest information, even if the news is bad. We found that 27 of 27 enrolled patients initially reported wanting to know all the available information about their cancer, prognosis, treatment benefits, and treatment side effects. Also, 26 of 27 patients were able to complete the decision aid fully, our main outcome measure. While approximately 10% of available patients were excluded from accrual by their oncologists or oncology nurses due to preexisting distress, fear of distress in the patient or family member, uncontrolled symptoms, or psychiatric illness, in general there was excellent acceptance of the study by patients and oncologists. In this pilot study we did not investigate the attitudes of nonparticipants nor were we able to collect sociodemographic data to determine nonresponse bias, that is, whether certain types of patients are more likely to decline participation in the study.
Participants in the study were overoptimistic about their chances of cure, potential treatment response, symptom relief, and survival. None of these patients had curable disease, but 63% thought that a person with metastatic cancer of their type could be cured and gave the average chance of cure as 52%. Inaccurate assessment of cure rates decreased postintervention. At the pretest 14/27 (52%) believed a person with cancer similar to theirs could be cured, which changed to 8/26 (31%) at the posttest. This agrees with other studies that showed that patients mistook palliative radiation for curative radiation about one-third of the time, even when provided with accurate information.[1], [30] and [31]
Knowledge of prognosis and planning for the future is important as there is evidence of benefit to having the discussion about treatment outcomes. Recent data show improved quality of care, improved quality of life, and improved caregiver quality of life if the physician discusses death with the patient and family.5 Transplant patients with advanced directives had more than a twofold survival advantage over those without them.27 Conversely, over- or underestimating survival or treatment benefit can lead to bad health outcomes. Stem-cell transplant patients who were overoptimistic lived no longer than those with realistic views.[32], [33] and [34] Cancer patients who overestimated their survival were more likely to die a “bad” death (defined as death in an intensive care unit, on a ventilator, or with multiple hospitalizations and emergency room visits) without achieving life extension.35 It may be that the 16%–20% of patients with incurable solid tumors who start a new chemotherapy regimen within 2 weeks of death,36 when they are unlikely to benefit, simply do not know the prognosis or treatment effect or have different perspectives.37 Alternatively, we do not know how many patients decline second- and nth-line chemotherapy without knowing the full benefits and risks and who might choose chemotherapy if they knew second- or nth-line chemotherapy improved survival, pain scores, or quality of life. For instance, 40% of breast cancer patients will have some disease control from fourth-line chemotherapy for up to 4 months even if there is no evidence of improved survival.38
Patients consistently tell us to be truthful, compassionate, and clear and to stay the course with them.[39] and [40] Despite nearly all American patients stating that they want full disclosure about their prognosis, treatment options, and expected outcomes, most patients do not receive such information41 or receive such information far too late in their course.42 Even if terminally ill patients with cancer requested survival estimates, doctors would provide such estimates only 37% of the time, often an overestimate;7 and a recent meta-analysis showed that cancer physicians consistently overestimated prognosis by at least 30%.43 Honest information respects the autonomy of a patient to make decisions based on what is known about the outcomes of such decisions.44 Such information should not be forced on a patient, but the patient should be told that the information is available and that he or she has the right to accept or decline the information.45
When we started this project, colleagues were concerned about whether patients would want such information, that patients would be distressed by poor prognosis, that patients would give up hope, and that the procedures would take too much time. We also were concerned about the effect of giving such bad news on the provider, when prior research showed negative effects on the information-giver's mood and affect from such encounters46 and that doctors in general protect themselves by not giving bad news.47 Completion of the decision aid was difficult for the interviewers, too. Some commented on how hard it was to give “bad” information about chance of cure and expected survival, even for patients they did not know. While patients may be more comfortable having advance directive discussions with a doctor they do not know rather than their oncologist,48 it can still be hard for the provider. Surprisingly, it rarely took more than 20 minutes to discuss the information including the tests since the information was preprinted.
Patients vary in their approach to decision making, but the decisions should at least start with good information. Based on these preliminary findings, the piloted intervention is significant because it can lead to measurable impacts on knowledge about prognosis and appears to be judged helpful. We do not know the impact of full and truthful information on patient knowledge, decision making, hope, attendant choices about advanced medical directives, chemotherapy use, or hospice use. The next steps are to make the information available directly to patients on the Internet, which is in progress. The purpose is not to increase or decrease the use of palliative chemotherapy or hospice care; the lack of research into the decisions fully informed patients make precludes any such prediction. Since the intervention appears to be successful in this pilot trial, it will be tested in conjunction with standard care in a randomized clinical trial with measurement of quality of care, quality of life, and health-care cost outcomes.
Acknowledgments
This research was supported by VCU School of Medicine Research Year Out, GO8 LM0095259 from the National Library of Medicine (T. J. S., L. L., J. K.), and R01CA116227-01 (T. J. S.) from the National Cancer Institute.
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Appendix A
Decision Aids
Patient Name: ___
Date: ___/___/___
Lung Cancer Second Line Chemotherapy
What is my chance of being alive at one year if I take chemotherapy, or do best supportive care such as hospice?
Chemotherapy with a drug like docetaxel (Taxotere®) or pemetrexed (Alimta®) improves the chance of being alive at one year by 18 out of 100 people. With chemotherapy, 37 of 100 people were alive at one year. Without chemotherapy, 11 of 100 were alive.
Patients receiving docetaxel (Taxotere®) chemotherapy lived an average of 7.5 months, versus 4.6 months if they did not take chemotherapy. In other words, they lived 2 to 3 months longer.
If you are having cancer-related symptoms that limit your daily activities, the chances of being alive at one year are less than that described above.
The numbers given here are what happens to the average person with this disease in this situation. Half the patients will do better than this, and half will do worse. Your situation could be better or worse. The numbers given for the chance of cure are very accurate. The numbers are given to help you with your own decision making.
What is the chance of my cancer shrinking by half?
About 6 of 100 people will have their cancer shrink by half.
If you are having cancer-related symptoms that limit your daily activities, the chances are less than that described above.
What is the chance of my being cured by chemotherapy?
In this setting, there is no chance of cure. The goal may change to controlling the disease and any symptoms for as long as possible. You may want to talk with your doctor about your own chances and goals of therapy.
How long will chemotherapy make my cancer shrink, if it does?
For all patients who did not get chemotherapy, the average time before the cancer grew was 7 weeks. For patients who got chemotherapy, the average time before the cancer grew was 11 weeks.
What did chemotherapy do to quality of life?
Chemotherapy helped reduce pain scores and did not make quality of life worse.
What are the most common side effects?
The most common side effects will vary with the type of treatment given.
Some of the most common ones include the following:
Mucositis (mouth sores).
Nausea/vomiting; usually controllable.
Alopecia (hair loss).
Neutropenia (low white blood cell count) and infection requiring antibiotics.
Neuropathy (numbness and pain in the hands and feet).
Are there other issues that I should address at this time?
Many people use this time to address a life review–what they have learned during life that they want to share with their families, and planning for events in the future like birthdays or weddings).
Some people address spiritual issues.
Some people address financial issues like a will.
Some people address Advance Directives (Living Wills).
For instance, if you could not speak for yourself, who would you want to make decisions about your care?
If your heart stopped beating, or you stopped breathing, due to the cancer worsening, would you want to have resuscitation (CPR), or be allowed to die naturally without resuscitation?
Some people use this time to discuss with their loved ones how they would like to spend the rest of their life. For instance, where do you want to spend your last days? Where do you want to die?
Do you want to have hospice involved?
These are all difficult issues, but important to discuss with your family and your health care professionals.
Correspondence to: Thomas J. Smith, MD, Virginia Commonwealth University, Division of Hematology/Oncology and Palliative Care, MCV Box 980230, Richmond, VA 23298–0230; telephone: (804) 828–9723; fax: (804) 828–8079
Original research
Thomas J. Smith MD, a,
Abstract
Most cancer patients do not have an explicit discussion about prognosis and treatment despite documented adverse outcomes. Few decision aids have been developed to assist the difficult discussions of palliative management. We developed decision aids for people with advanced incurable breast, colorectal, lung, and hormone-refractory prostate cancers facing first-, second-, third-, and fourth-line chemotherapy. We recruited patients from our urban oncology clinic after gaining the permission of their treating oncologist. We measured knowledge of curability and treatment benefit before and after the intervention. Twenty-six of 27 (96%) patients completed the aids, with a mean age of 63, 56% female, 56% married, 56% African American, and 67% with a high school education or more. Most patients (14/27, 52%) thought a person with their advanced cancer could be cured, which was reduced (to 8/26, 31%, P = 0.15) after the decision aid. Nearly all overestimated the effect of palliative chemotherapy. No distress was noted, and hope did not change. The majority (20/27, 74%) found the information helpful to them, and almost all (25/27, 93%) wanted to share the information with their family and physicians. It is possible to give incurable patients their prognosis, treatment options, and options for improving end-of-life care without causing distress or lack of hope. Almost all find the information helpful and want to share it with doctors and family. Research is needed to test the findings in a larger sample and measure the outcomes of truthful information on quality of life, quality of care, and costs.
Article Outline
We designed decision aids for patients with incurable cancer and attempted to determine if people would opt for full disclosure about prognosis and treatment. If they opted for full disclosure, we assessed current knowledge about chance of cure, survival, disease response rates, and symptom control, before and after. This pilot trial was done to see if patients would complete a decision aid about their advanced cancer, even if it contained truthful information about their limited prognosis and treatment benefits.
Methods
We created state-of-the-art tables of information for patients with advanced breast, lung, colon, and hormone-refractory prostate cancers, based on expert review, external review, and comparison with Up To Date© (available from the authors). The information was approved by all three oncologists involved. We used bar graphs to illustrate benefit, developed for patient education graphs for a randomized study of insurance types and treatment choices9 and in common use on the Web site Adjuvant Online (www.adjuvantonline.org).[10] and [11] It is similar to what we do with the written medical record, a concise review of diagnosis, prognosis, treatment options, side effects, and when to call the doctor.12
We tested the intervention in a heterogeneous sample of 27 patients recruited through the Dalton Oncology Clinic, which serves a mix of patients from the most discerning third-opinion clinical trial patient to the community cancer patient and provides most of the indigent cancer care in the central Virginia area. The study was done within 3 months in early 2009.
Our primary outcome was the number of patients who would opt for full disclosure once they viewed the decision aid. Our secondary outcomes included the following: the amount of information patients have about cure, response rates, and symptom control; the impact of truthful information on hope, as measured by the Herth Hope Index©13 (HHI) used to assess hope in clinical studies of adults;14 whether the information was deemed helpful to the patient; and whether the patient intended to share the information with a doctor.
Patients were accrued by reviewing the daily clinic list to find patients on treatment for incurable breast, colorectal, non-small-cell lung, or hormone-refractory prostate cancer. Treating oncologists were made aware of the study through e-mail, announcements, the Massey Cancer Center Web site, and individual meetings. All oncologists approached agreed to their chemotherapy patients participating in the study in general, and the primary nurse or treating oncologist was contacted about each eligible patient. Eligible patients were not contacted about the study when the treating oncologist or primary oncology nurse determined that a patient was experiencing significant distress or had significant psychiatric problems or difficulty with adjustment to illness or believed the patient would have great emotional difficulty with the information. The number of patients excluded by each oncologist due to concern about distress was estimated to be less than 10% of the total available but was not measured. Since these patients were not enrolled in the study, we did not collect information about them. A clinical psychologist and a chaplain were available to any patient who experienced distress during or after the interview process. The interview questions and intervention were administered by a member of the study team who was not the patient's oncologist or involved in his or her care. The interview team included a graduate student who was also a minister and chaplain (E. A. V.), a medical student with special training in empathic communication (L. A. D.), and/or the principal investigator (T. J. S.); usually one interviewer was present (L. A. D. or E. A. V.).
The interview sequence included screening questions to ensure that the patient wanted full information, sociodemographic questions, a pretest about the chance of cure and treatment effect for a patient with their illness, and the HHI. Next, the decision aid was administered. Immediately afterward, the patient completed a posttest, the HHI, and information about how he or she would use the information.
We modeled this approach on the Ottawa Decision Support Framework, a clinically tested decision-making tool designed to inform decisional conflict,[15] and [16] defined as uncertainty about which course of action to take when the choice involves balancing gain, risk, loss, regrets, or challenges to personal life.17
Our study was approved by the Massey Cancer Center Protocol Review and Monitoring System and the VCU Institutional Review Board for the Conduct of Human Research. Because it was not a clinical trial, no clinical trial registration was required.
Results
The patients were typical for our urban, tertiary referral, and safety net hospital and National Cancer Institute–designated cancer center, as shown in Table 1.
| Age (years) | |
| Mean | 63 ± 5 |
| Range | 46–74 |
| Gender | |
| Male | 12 (44%) |
| Female | 15 (56%) |
| Marital status | |
| Married or committed relationship | 15 (56%) |
| Divorced | 6 (22%) |
| Widowed | 2 (7%) |
| Single/never married | 2 (7%) |
| Ethnicity | |
| Caucasian | 12 (44%) |
| African American | 15 (56%) |
| Education completed | |
| Less than high school | 5 (9%) |
| Some high school | 1 (4%) |
| HS diploma/GED | 8 (30%) |
| Some college | 10 (37%) |
| Completed college | 1 (4%) |
| Completed postgrad | 2 (7%) |
| Total household income | |
| <$15,000 | 10 (37%) |
| $15,000–$34,999 | 8 (30%) |
| $35,000–$74,999 | 6 (22%) |
| >$75,000 | 1 (4%) |
| Don't know | 2 (7%) |
| Average number of people in household | 2.3 (SD 0.9) |
| Type of cancer and line of chemotherapy | |
| Breast 1st, 2nd, 3rd, 4th line | 5, 2, 1,1 = 9 total |
| Colorectal 1st, 2nd, 3rd, 4th line | 8, 5, 1, 0 = 14 total |
| Lung 1st, 2nd, 3rd, 4th line | 2, 0, 0, 0 = 2 total |
| Hormone-refractory prostate 1st, 2nd, 3rd, 4th line | 1, 1, 0, 0 = 2 |
Primary Outcome
Our primary outcome was to assess if patients would complete a decision aid with full disclosure. Of 27 patients, only one (4%) chose not to complete the decision aid after starting. She was a 55-year-old African American woman who had recently started first-line treatment for metastatic colorectal cancer. She had been told at another institution that she had lost too much weight and was too ill to benefit from chemotherapy, but with counseling she regained the weight and had a performance status of 2 at VCU. In her pretest, she answered that she thought a woman with metastatic colorectal cancer spread to bones and lymph glands could be cured, with a chance of cure of 50%. Once presented with the information (good treatments that prolong life and control symptoms but no chance of cure and 9% of patients with metastatic colorectal cancer alive at 5 years), she said that she did not want to finish the questions. She did complete her HHI, which did not change, and was not distressed (see Table 2, patient 12).
| PATIENT | SITUATION (1ST-, 2ND-, 3RD-, 4TH-LINE CHEMOTHERAPY) | COMMENT |
|---|---|---|
| 1 | CRC, 1st | “Feel little bit better.” “Didn't upset.” |
| 2 | BC, 2nd | “It gave me information on my condition.” |
| 3 | BC, 4th | “If I've got 6 months to live, I want to know so I can party” |
| 3 | LC, 1st | “It let me know I have longer than a year, possibly longer than that. Helpful.” |
| 5 | PC, 2nd | “Well, Dr. R said it couldn't be cured … I've done well for 16 months so far.” |
| 6 | CRC, 2nd | “We've already discussed everything. All information I think is helpful.” |
| 7 | CRC, 1st | “… happy that my life expectancy might be better than I thought.” At the end, he said “how good it was to talk and not hold things in.” |
| 8 | CRC, 3rd | “Verification of what I have been told.” |
| 9 | CRC, 2nd | “I know all this, but it was helpful. Especially for people who haven't heard it.” |
| 10 | CRC, 1st | |
| 11 | CRC, 2nd | “Helpful … just to think about my goals and that kind of thing.” |
| 12 | CRC, 1st | “Wouldn't know [about cure]. I can't answer those … [questions about cure rates, response rates after reviewing data]. Tell them to give people hope, not take away hope … not to 'just go smell the roses.' ” |
| 13 | CRC, 1st | “There were some things I didn't know—I didn't know about the 1–2 years—I'm not going to accept it though—I'm planning on more.” |
| 14 | BC, 1st | “Gave me info based on stats that I didn't know before.” |
| 15 | BC, 1st | “It's hard to explain. It's about what I have already known.” |
| 16 | BC, 2nd | “Helped me to understand … . That chemo is better than not having chemo.” |
| 17 | CRC, 1st | |
| 18 | CRC, 2nd | “Helpful to know what will happen, given strength, how to feel about things … to get to talk about things.” |
| 19 | BC, 1st | “Helpful. It opened my eyes, made me aware. I would want to know that.” |
| 20 | BC, 1st | Helpful. “It gave me a lot to think about. A whole lot of it I didn't know about.” |
| 21 | CRC, 1st | Helpful. “Knowing that I was doing something to help someone else. It made me think about what I have to look forward to in life.” |
| 22 | CRC, 2nd | “In a way, you're saying what the possibilities are. I just hope that I keep on trucking.” |
| 23 | BC, 3rd | “Always helpful to discuss prognosis.” |
| 24 | LC, 1st | “Helpful to know what chances I get, with or without (chemotherapy) treatment.” |
| 25 | PC, 1st | “Because the odds are a hell of a lot less than I thought, it's a bummer.” |
| 26 | BC, 1st | “It gave me a chance to see the percentage of things with breast cancer. I have a better understanding of the time line.” |
| 26 | CRC, 1st | “Made me understand some things.” On change in survival from >3 years to “don't know,” “I hope to live a right good while.” |
BC = breast cancer; CRC = colon or rectal cancer; LC = non-small-cell lung cancer; PC = hormone-refractory prostate cancer
In the pretest, almost all the patients, including the patient above, reported wanting full disclosure about cancer, prognosis, treatment, and side effects. In response to questions beginning “How much do you want to know about …” 27 of 27 answered “Tell me all” to the questions about “your cancer,” “your prognosis,” “treatment benefits,” and “treatment side effects.” Only one of 27 answered otherwise: “Tell me a little” about cancer, and “Tell me some” about prognosis.
Secondary Outcomes
Participants were overoptimistic about the results of palliative chemotherapy, as shown in Table 3. Most (14/27, 52%) people thought a person with “metastatic cancer (breast, colorectal, lung, prostate—specific to that person's disease) spread to the bones and lymph glands” could be cured. After the decision aid, more people recognized that their cancer could not be cured (17/25, 63%) but eight of 25 (32%, P = 0.15, Fischer's exact test) still thought a person with metastatic disease could be cured. Patients were particularly overoptimistic about the chance of their symptoms being helped by chemotherapy: 87% thought their symptoms would be helped by chemotherapy, and 60% thought a patient would have at least 50% shrinkage of their cancer before the exercise, which declined only slightly after the decision aid. (While the correct answer varies by disease, the number helped by chemotherapy is usually less than 50%, and response rates are always less than 50%.)
| PRE | POST | CHANGE | COMMENT | |
|---|---|---|---|---|
| Can this person with cancer in the bones and lymph nodes be cured by medical treatment? | Yes = 14 No = 11 Don't know = 2 | Yes = 8 No = 17 Don't know = 2 | Changed from yes to no = 6; changed from no to yes = 1 | Correct answer “no” P = 0.15 Fischer's exact test |
| 52.5 ± 32 | 47 ± 26 | −5.8 ± 28 | The correct answer is 0%; all overoptimistic | |
| What is the chance of her _____ cancer shrinking by half? In % | 60 ± 32 | 57.5 ± 17.6 | −4.2 ± 28 | All overoptimistic |
| What is the chance of _____ cancer symptoms being helped? In % | 87 ± 19 | 74.2 ± 21 | −6.7 ± 26 | All overoptimistic |
| How long does the average person live with advanced ____ cancer (using the choices from the breast cancer sheet for example)? | More realistic, but 2 people increased their expected length of survival | |||
| More than 3 years | 18 | 14 | −4 | |
| About 2 years | 6 | 11 | 5 | |
| About 6 months | 0 | 0 | 0 | |
| Just a few weeks | 1 | 0 | −1 | |
| Don't know/NA | 2 | 2 | 2 | |
| Distress observed by interviewer, nurse, or oncologist | No | No |
Categorical variables Yes and No analyzed by Fischer's exact test
Numerical variables analyzed by Student's t-test, unpaired; none significant
There was no change in responses to the HHI after the intervention as we have previously reported.18 Participants did not appear to be visibly distressed by the intervention. A psychologist and chaplain were made available, but no one requested their services. In our small clinic, the primary nurses and doctors have frequent interactions during visits and chemotherapy. No patient was reported to be distressed in any way, during that visit or subsequent visits.
The comments recorded by the patients or the interviewers at the end of the exercise showed that most patients would share the information, as shown in Table 4.
| Will you share it with anyone? | Yes = 20 No = 6 NA = 1 |
| If so, who? __ My family __ My oncologist __ My oncology nurse __ My primary care doctor __ Other ______ | All (family, ONC, PCP) = 14 Family only = 2 Oncologist = 12 PCP = 14, one said “not PCP” |
| Was this patient information sheet helpful to you? | Yes = 25 No = 1, “Bummer” NA = 2 |
NA = no answer; ONC = oncologist; PCP = primary care provider
In some cases, the average prognosis and treatment benefit, although small, was bigger than the person thought before the exercise. Nearly all found it helpful. Some illustrative comments are shown in Table 2.
We did not formally measure the time to complete the screening questions, pre- and posttests, pre- and post-HHI, and decision aid; but in most cases it took less than 20 minutes to complete the whole package including the pre- and post-tests. Review of the decision aid with the patient always took less than 5 minutes, even when we were reading it with the patient and family. This is consistent with work showing that oncologists state that completing an advance directive will take too much time but, in fact, it takes less than 10 minutes.[19] and [20]
Discussion
Historical data show that patients know little about their prognosis and the effect that treatment will have on their cancer. Yet, this knowledge is essential to making informed choices about treatment benefits, risks, and even costs. When tested in randomized controlled trials, decision aids led to more involvement in decision making.[21] and [22] However, there were no decision aids available about metastatic incurable disease, despite some promising early starts[23], [24], [25], [26], [27] and [28] and only one about first-line treatment,29 so we made a simple one. A successful decision aid may allow patients to discuss their situations with their physicians and develop management strategies that best concur with personal goals and preferences and help patients make plans in other areas of life.
Our findings suggest that most people do want honest information, even if the news is bad. We found that 27 of 27 enrolled patients initially reported wanting to know all the available information about their cancer, prognosis, treatment benefits, and treatment side effects. Also, 26 of 27 patients were able to complete the decision aid fully, our main outcome measure. While approximately 10% of available patients were excluded from accrual by their oncologists or oncology nurses due to preexisting distress, fear of distress in the patient or family member, uncontrolled symptoms, or psychiatric illness, in general there was excellent acceptance of the study by patients and oncologists. In this pilot study we did not investigate the attitudes of nonparticipants nor were we able to collect sociodemographic data to determine nonresponse bias, that is, whether certain types of patients are more likely to decline participation in the study.
Participants in the study were overoptimistic about their chances of cure, potential treatment response, symptom relief, and survival. None of these patients had curable disease, but 63% thought that a person with metastatic cancer of their type could be cured and gave the average chance of cure as 52%. Inaccurate assessment of cure rates decreased postintervention. At the pretest 14/27 (52%) believed a person with cancer similar to theirs could be cured, which changed to 8/26 (31%) at the posttest. This agrees with other studies that showed that patients mistook palliative radiation for curative radiation about one-third of the time, even when provided with accurate information.[1], [30] and [31]
Knowledge of prognosis and planning for the future is important as there is evidence of benefit to having the discussion about treatment outcomes. Recent data show improved quality of care, improved quality of life, and improved caregiver quality of life if the physician discusses death with the patient and family.5 Transplant patients with advanced directives had more than a twofold survival advantage over those without them.27 Conversely, over- or underestimating survival or treatment benefit can lead to bad health outcomes. Stem-cell transplant patients who were overoptimistic lived no longer than those with realistic views.[32], [33] and [34] Cancer patients who overestimated their survival were more likely to die a “bad” death (defined as death in an intensive care unit, on a ventilator, or with multiple hospitalizations and emergency room visits) without achieving life extension.35 It may be that the 16%–20% of patients with incurable solid tumors who start a new chemotherapy regimen within 2 weeks of death,36 when they are unlikely to benefit, simply do not know the prognosis or treatment effect or have different perspectives.37 Alternatively, we do not know how many patients decline second- and nth-line chemotherapy without knowing the full benefits and risks and who might choose chemotherapy if they knew second- or nth-line chemotherapy improved survival, pain scores, or quality of life. For instance, 40% of breast cancer patients will have some disease control from fourth-line chemotherapy for up to 4 months even if there is no evidence of improved survival.38
Patients consistently tell us to be truthful, compassionate, and clear and to stay the course with them.[39] and [40] Despite nearly all American patients stating that they want full disclosure about their prognosis, treatment options, and expected outcomes, most patients do not receive such information41 or receive such information far too late in their course.42 Even if terminally ill patients with cancer requested survival estimates, doctors would provide such estimates only 37% of the time, often an overestimate;7 and a recent meta-analysis showed that cancer physicians consistently overestimated prognosis by at least 30%.43 Honest information respects the autonomy of a patient to make decisions based on what is known about the outcomes of such decisions.44 Such information should not be forced on a patient, but the patient should be told that the information is available and that he or she has the right to accept or decline the information.45
When we started this project, colleagues were concerned about whether patients would want such information, that patients would be distressed by poor prognosis, that patients would give up hope, and that the procedures would take too much time. We also were concerned about the effect of giving such bad news on the provider, when prior research showed negative effects on the information-giver's mood and affect from such encounters46 and that doctors in general protect themselves by not giving bad news.47 Completion of the decision aid was difficult for the interviewers, too. Some commented on how hard it was to give “bad” information about chance of cure and expected survival, even for patients they did not know. While patients may be more comfortable having advance directive discussions with a doctor they do not know rather than their oncologist,48 it can still be hard for the provider. Surprisingly, it rarely took more than 20 minutes to discuss the information including the tests since the information was preprinted.
Patients vary in their approach to decision making, but the decisions should at least start with good information. Based on these preliminary findings, the piloted intervention is significant because it can lead to measurable impacts on knowledge about prognosis and appears to be judged helpful. We do not know the impact of full and truthful information on patient knowledge, decision making, hope, attendant choices about advanced medical directives, chemotherapy use, or hospice use. The next steps are to make the information available directly to patients on the Internet, which is in progress. The purpose is not to increase or decrease the use of palliative chemotherapy or hospice care; the lack of research into the decisions fully informed patients make precludes any such prediction. Since the intervention appears to be successful in this pilot trial, it will be tested in conjunction with standard care in a randomized clinical trial with measurement of quality of care, quality of life, and health-care cost outcomes.
Acknowledgments
This research was supported by VCU School of Medicine Research Year Out, GO8 LM0095259 from the National Library of Medicine (T. J. S., L. L., J. K.), and R01CA116227-01 (T. J. S.) from the National Cancer Institute.
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35 J.C. Weeks, E.F. Cook, S.J. O'Day, L.M. Peterson, N. Wenger and D. Reding et al., Relationship between cancer patients' predictions of prognosis and their treatment preferences, JAMA 279 (21) (1998), pp. 1709–1714. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (350)
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39 P. Kirk, I. Kirk and L.J. Kristjanson, What do patients receiving palliative care for cancer and their families want to be told?: A Canadian and Australian qualitative study, BMJ 328 (7452) (2004), p. 1343. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (110)
40 L.L. Emanuel, F.D. Ferris, C.F. von Gunten and J. Von Roenn, EPEC-O: Education in Palliative and End of Life Care for Oncology, EPEC Project, Chicago (2005).
41 M.J. Field and C.K. Cassel, Approaching Death: Improving Care at the End of Life, National Academy Press, Washington DC (1997), pp. 59–64.
42 H.A. Huskamp, N.L. Keating, J.L. Malin, A.M. Zaslavsky, J.C. Weeks, C.C. Earle, J.M. Teno, B.A. Virnig, K.L. Kahn, Y. He and J.Z. Ayanian, Discussions with physicians about hospice among patients with metastatic lung cancer, Arch Intern Med 169 (10) (2009), pp. 954–962. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (4)
43 P. Glare, K. Virik, M. Jones, M. Hudson, S. Eychmuller and J. Simes et al., A systematic review of physicians' survival predictions in terminally ill cancer patients, BMJ 327 (2003), pp. 195–200.
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45 T. Beauchamp and J. Childress, Principles of Biomedical Ethics (5th ed), Oxford University Press, New York (2001).
46 E. Panagopoulou, G. Mintziori, A. Montgomery, D. Kapoukranidou and A. Benos, Concealment of information in clinical practice: is lying less stressful than telling the truth?, J Clin Oncol 26 (7) (2008), pp. 1175–1177. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (9)
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48 L.A. Dow, R.K. Matsuyama, V. Ramakrishnan, L. Kuhn, E.B. Lamont, L. Lyckholm and T.J. Smith, Paradoxes in advance care planning: the complex relationship of oncology patients, their physicians, and advance medical directives, J Clin Oncol 28 (2) (2010), pp. 299–304. Full Text via CrossRef | View Record in Scopus | Cited By in Scopus (2)
Appendix A
Decision Aids
Patient Name: ___
Date: ___/___/___
Lung Cancer Second Line Chemotherapy
What is my chance of being alive at one year if I take chemotherapy, or do best supportive care such as hospice?
Chemotherapy with a drug like docetaxel (Taxotere®) or pemetrexed (Alimta®) improves the chance of being alive at one year by 18 out of 100 people. With chemotherapy, 37 of 100 people were alive at one year. Without chemotherapy, 11 of 100 were alive.
Patients receiving docetaxel (Taxotere®) chemotherapy lived an average of 7.5 months, versus 4.6 months if they did not take chemotherapy. In other words, they lived 2 to 3 months longer.
If you are having cancer-related symptoms that limit your daily activities, the chances of being alive at one year are less than that described above.
The numbers given here are what happens to the average person with this disease in this situation. Half the patients will do better than this, and half will do worse. Your situation could be better or worse. The numbers given for the chance of cure are very accurate. The numbers are given to help you with your own decision making.
What is the chance of my cancer shrinking by half?
About 6 of 100 people will have their cancer shrink by half.
If you are having cancer-related symptoms that limit your daily activities, the chances are less than that described above.
What is the chance of my being cured by chemotherapy?
In this setting, there is no chance of cure. The goal may change to controlling the disease and any symptoms for as long as possible. You may want to talk with your doctor about your own chances and goals of therapy.
How long will chemotherapy make my cancer shrink, if it does?
For all patients who did not get chemotherapy, the average time before the cancer grew was 7 weeks. For patients who got chemotherapy, the average time before the cancer grew was 11 weeks.
What did chemotherapy do to quality of life?
Chemotherapy helped reduce pain scores and did not make quality of life worse.
What are the most common side effects?
The most common side effects will vary with the type of treatment given.
Some of the most common ones include the following:
Mucositis (mouth sores).
Nausea/vomiting; usually controllable.
Alopecia (hair loss).
Neutropenia (low white blood cell count) and infection requiring antibiotics.
Neuropathy (numbness and pain in the hands and feet).
Are there other issues that I should address at this time?
Many people use this time to address a life review–what they have learned during life that they want to share with their families, and planning for events in the future like birthdays or weddings).
Some people address spiritual issues.
Some people address financial issues like a will.
Some people address Advance Directives (Living Wills).
For instance, if you could not speak for yourself, who would you want to make decisions about your care?
If your heart stopped beating, or you stopped breathing, due to the cancer worsening, would you want to have resuscitation (CPR), or be allowed to die naturally without resuscitation?
Some people use this time to discuss with their loved ones how they would like to spend the rest of their life. For instance, where do you want to spend your last days? Where do you want to die?
Do you want to have hospice involved?
These are all difficult issues, but important to discuss with your family and your health care professionals.
Correspondence to: Thomas J. Smith, MD, Virginia Commonwealth University, Division of Hematology/Oncology and Palliative Care, MCV Box 980230, Richmond, VA 23298–0230; telephone: (804) 828–9723; fax: (804) 828–8079
Original research
Thomas J. Smith MD, a,
Abstract
Most cancer patients do not have an explicit discussion about prognosis and treatment despite documented adverse outcomes. Few decision aids have been developed to assist the difficult discussions of palliative management. We developed decision aids for people with advanced incurable breast, colorectal, lung, and hormone-refractory prostate cancers facing first-, second-, third-, and fourth-line chemotherapy. We recruited patients from our urban oncology clinic after gaining the permission of their treating oncologist. We measured knowledge of curability and treatment benefit before and after the intervention. Twenty-six of 27 (96%) patients completed the aids, with a mean age of 63, 56% female, 56% married, 56% African American, and 67% with a high school education or more. Most patients (14/27, 52%) thought a person with their advanced cancer could be cured, which was reduced (to 8/26, 31%, P = 0.15) after the decision aid. Nearly all overestimated the effect of palliative chemotherapy. No distress was noted, and hope did not change. The majority (20/27, 74%) found the information helpful to them, and almost all (25/27, 93%) wanted to share the information with their family and physicians. It is possible to give incurable patients their prognosis, treatment options, and options for improving end-of-life care without causing distress or lack of hope. Almost all find the information helpful and want to share it with doctors and family. Research is needed to test the findings in a larger sample and measure the outcomes of truthful information on quality of life, quality of care, and costs.
Article Outline
We designed decision aids for patients with incurable cancer and attempted to determine if people would opt for full disclosure about prognosis and treatment. If they opted for full disclosure, we assessed current knowledge about chance of cure, survival, disease response rates, and symptom control, before and after. This pilot trial was done to see if patients would complete a decision aid about their advanced cancer, even if it contained truthful information about their limited prognosis and treatment benefits.
Methods
We created state-of-the-art tables of information for patients with advanced breast, lung, colon, and hormone-refractory prostate cancers, based on expert review, external review, and comparison with Up To Date© (available from the authors). The information was approved by all three oncologists involved. We used bar graphs to illustrate benefit, developed for patient education graphs for a randomized study of insurance types and treatment choices9 and in common use on the Web site Adjuvant Online (www.adjuvantonline.org).[10] and [11] It is similar to what we do with the written medical record, a concise review of diagnosis, prognosis, treatment options, side effects, and when to call the doctor.12
We tested the intervention in a heterogeneous sample of 27 patients recruited through the Dalton Oncology Clinic, which serves a mix of patients from the most discerning third-opinion clinical trial patient to the community cancer patient and provides most of the indigent cancer care in the central Virginia area. The study was done within 3 months in early 2009.
Our primary outcome was the number of patients who would opt for full disclosure once they viewed the decision aid. Our secondary outcomes included the following: the amount of information patients have about cure, response rates, and symptom control; the impact of truthful information on hope, as measured by the Herth Hope Index©13 (HHI) used to assess hope in clinical studies of adults;14 whether the information was deemed helpful to the patient; and whether the patient intended to share the information with a doctor.
Patients were accrued by reviewing the daily clinic list to find patients on treatment for incurable breast, colorectal, non-small-cell lung, or hormone-refractory prostate cancer. Treating oncologists were made aware of the study through e-mail, announcements, the Massey Cancer Center Web site, and individual meetings. All oncologists approached agreed to their chemotherapy patients participating in the study in general, and the primary nurse or treating oncologist was contacted about each eligible patient. Eligible patients were not contacted about the study when the treating oncologist or primary oncology nurse determined that a patient was experiencing significant distress or had significant psychiatric problems or difficulty with adjustment to illness or believed the patient would have great emotional difficulty with the information. The number of patients excluded by each oncologist due to concern about distress was estimated to be less than 10% of the total available but was not measured. Since these patients were not enrolled in the study, we did not collect information about them. A clinical psychologist and a chaplain were available to any patient who experienced distress during or after the interview process. The interview questions and intervention were administered by a member of the study team who was not the patient's oncologist or involved in his or her care. The interview team included a graduate student who was also a minister and chaplain (E. A. V.), a medical student with special training in empathic communication (L. A. D.), and/or the principal investigator (T. J. S.); usually one interviewer was present (L. A. D. or E. A. V.).
The interview sequence included screening questions to ensure that the patient wanted full information, sociodemographic questions, a pretest about the chance of cure and treatment effect for a patient with their illness, and the HHI. Next, the decision aid was administered. Immediately afterward, the patient completed a posttest, the HHI, and information about how he or she would use the information.
We modeled this approach on the Ottawa Decision Support Framework, a clinically tested decision-making tool designed to inform decisional conflict,[15] and [16] defined as uncertainty about which course of action to take when the choice involves balancing gain, risk, loss, regrets, or challenges to personal life.17
Our study was approved by the Massey Cancer Center Protocol Review and Monitoring System and the VCU Institutional Review Board for the Conduct of Human Research. Because it was not a clinical trial, no clinical trial registration was required.
Results
The patients were typical for our urban, tertiary referral, and safety net hospital and National Cancer Institute–designated cancer center, as shown in Table 1.
| Age (years) | |
| Mean | 63 ± 5 |
| Range | 46–74 |
| Gender | |
| Male | 12 (44%) |
| Female | 15 (56%) |
| Marital status | |
| Married or committed relationship | 15 (56%) |
| Divorced | 6 (22%) |
| Widowed | 2 (7%) |
| Single/never married | 2 (7%) |
| Ethnicity | |
| Caucasian | 12 (44%) |
| African American | 15 (56%) |
| Education completed | |
| Less than high school | 5 (9%) |
| Some high school | 1 (4%) |
| HS diploma/GED | 8 (30%) |
| Some college | 10 (37%) |
| Completed college | 1 (4%) |
| Completed postgrad | 2 (7%) |
| Total household income | |
| <$15,000 | 10 (37%) |
| $15,000–$34,999 | 8 (30%) |
| $35,000–$74,999 | 6 (22%) |
| >$75,000 | 1 (4%) |
| Don't know | 2 (7%) |
| Average number of people in household | 2.3 (SD 0.9) |
| Type of cancer and line of chemotherapy | |
| Breast 1st, 2nd, 3rd, 4th line | 5, 2, 1,1 = 9 total |
| Colorectal 1st, 2nd, 3rd, 4th line | 8, 5, 1, 0 = 14 total |
| Lung 1st, 2nd, 3rd, 4th line | 2, 0, 0, 0 = 2 total |
| Hormone-refractory prostate 1st, 2nd, 3rd, 4th line | 1, 1, 0, 0 = 2 |
Primary Outcome
Our primary outcome was to assess if patients would complete a decision aid with full disclosure. Of 27 patients, only one (4%) chose not to complete the decision aid after starting. She was a 55-year-old African American woman who had recently started first-line treatment for metastatic colorectal cancer. She had been told at another institution that she had lost too much weight and was too ill to benefit from chemotherapy, but with counseling she regained the weight and had a performance status of 2 at VCU. In her pretest, she answered that she thought a woman with metastatic colorectal cancer spread to bones and lymph glands could be cured, with a chance of cure of 50%. Once presented with the information (good treatments that prolong life and control symptoms but no chance of cure and 9% of patients with metastatic colorectal cancer alive at 5 years), she said that she did not want to finish the questions. She did complete her HHI, which did not change, and was not distressed (see Table 2, patient 12).
| PATIENT | SITUATION (1ST-, 2ND-, 3RD-, 4TH-LINE CHEMOTHERAPY) | COMMENT |
|---|---|---|
| 1 | CRC, 1st | “Feel little bit better.” “Didn't upset.” |
| 2 | BC, 2nd | “It gave me information on my condition.” |
| 3 | BC, 4th | “If I've got 6 months to live, I want to know so I can party” |
| 3 | LC, 1st | “It let me know I have longer than a year, possibly longer than that. Helpful.” |
| 5 | PC, 2nd | “Well, Dr. R said it couldn't be cured … I've done well for 16 months so far.” |
| 6 | CRC, 2nd | “We've already discussed everything. All information I think is helpful.” |
| 7 | CRC, 1st | “… happy that my life expectancy might be better than I thought.” At the end, he said “how good it was to talk and not hold things in.” |
| 8 | CRC, 3rd | “Verification of what I have been told.” |
| 9 | CRC, 2nd | “I know all this, but it was helpful. Especially for people who haven't heard it.” |
| 10 | CRC, 1st | |
| 11 | CRC, 2nd | “Helpful … just to think about my goals and that kind of thing.” |
| 12 | CRC, 1st | “Wouldn't know [about cure]. I can't answer those … [questions about cure rates, response rates after reviewing data]. Tell them to give people hope, not take away hope … not to 'just go smell the roses.' ” |
| 13 | CRC, 1st | “There were some things I didn't know—I didn't know about the 1–2 years—I'm not going to accept it though—I'm planning on more.” |
| 14 | BC, 1st | “Gave me info based on stats that I didn't know before.” |
| 15 | BC, 1st | “It's hard to explain. It's about what I have already known.” |
| 16 | BC, 2nd | “Helped me to understand … . That chemo is better than not having chemo.” |
| 17 | CRC, 1st | |
| 18 | CRC, 2nd | “Helpful to know what will happen, given strength, how to feel about things … to get to talk about things.” |
| 19 | BC, 1st | “Helpful. It opened my eyes, made me aware. I would want to know that.” |
| 20 | BC, 1st | Helpful. “It gave me a lot to think about. A whole lot of it I didn't know about.” |
| 21 | CRC, 1st | Helpful. “Knowing that I was doing something to help someone else. It made me think about what I have to look forward to in life.” |
| 22 | CRC, 2nd | “In a way, you're saying what the possibilities are. I just hope that I keep on trucking.” |
| 23 | BC, 3rd | “Always helpful to discuss prognosis.” |
| 24 | LC, 1st | “Helpful to know what chances I get, with or without (chemotherapy) treatment.” |
| 25 | PC, 1st | “Because the odds are a hell of a lot less than I thought, it's a bummer.” |
| 26 | BC, 1st | “It gave me a chance to see the percentage of things with breast cancer. I have a better understanding of the time line.” |
| 26 | CRC, 1st | “Made me understand some things.” On change in survival from >3 years to “don't know,” “I hope to live a right good while.” |
BC = breast cancer; CRC = colon or rectal cancer; LC = non-small-cell lung cancer; PC = hormone-refractory prostate cancer
In the pretest, almost all the patients, including the patient above, reported wanting full disclosure about cancer, prognosis, treatment, and side effects. In response to questions beginning “How much do you want to know about …” 27 of 27 answered “Tell me all” to the questions about “your cancer,” “your prognosis,” “treatment benefits,” and “treatment side effects.” Only one of 27 answered otherwise: “Tell me a little” about cancer, and “Tell me some” about prognosis.
Secondary Outcomes
Participants were overoptimistic about the results of palliative chemotherapy, as shown in Table 3. Most (14/27, 52%) people thought a person with “metastatic cancer (breast, colorectal, lung, prostate—specific to that person's disease) spread to the bones and lymph glands” could be cured. After the decision aid, more people recognized that their cancer could not be cured (17/25, 63%) but eight of 25 (32%, P = 0.15, Fischer's exact test) still thought a person with metastatic disease could be cured. Patients were particularly overoptimistic about the chance of their symptoms being helped by chemotherapy: 87% thought their symptoms would be helped by chemotherapy, and 60% thought a patient would have at least 50% shrinkage of their cancer before the exercise, which declined only slightly after the decision aid. (While the correct answer varies by disease, the number helped by chemotherapy is usually less than 50%, and response rates are always less than 50%.)
| PRE | POST | CHANGE | COMMENT | |
|---|---|---|---|---|
| Can this person with cancer in the bones and lymph nodes be cured by medical treatment? | Yes = 14 No = 11 Don't know = 2 | Yes = 8 No = 17 Don't know = 2 | Changed from yes to no = 6; changed from no to yes = 1 | Correct answer “no” P = 0.15 Fischer's exact test |
| 52.5 ± 32 | 47 ± 26 | −5.8 ± 28 | The correct answer is 0%; all overoptimistic | |
| What is the chance of her _____ cancer shrinking by half? In % | 60 ± 32 | 57.5 ± 17.6 | −4.2 ± 28 | All overoptimistic |
| What is the chance of _____ cancer symptoms being helped? In % | 87 ± 19 | 74.2 ± 21 | −6.7 ± 26 | All overoptimistic |
| How long does the average person live with advanced ____ cancer (using the choices from the breast cancer sheet for example)? | More realistic, but 2 people increased their expected length of survival | |||
| More than 3 years | 18 | 14 | −4 | |
| About 2 years | 6 | 11 | 5 | |
| About 6 months | 0 | 0 | 0 | |
| Just a few weeks | 1 | 0 | −1 | |
| Don't know/NA | 2 | 2 | 2 | |
| Distress observed by interviewer, nurse, or oncologist | No | No |
Categorical variables Yes and No analyzed by Fischer's exact test
Numerical variables analyzed by Student's t-test, unpaired; none significant
There was no change in responses to the HHI after the intervention as we have previously reported.18 Participants did not appear to be visibly distressed by the intervention. A psychologist and chaplain were made available, but no one requested their services. In our small clinic, the primary nurses and doctors have frequent interactions during visits and chemotherapy. No patient was reported to be distressed in any way, during that visit or subsequent visits.
The comments recorded by the patients or the interviewers at the end of the exercise showed that most patients would share the information, as shown in Table 4.
| Will you share it with anyone? | Yes = 20 No = 6 NA = 1 |
| If so, who? __ My family __ My oncologist __ My oncology nurse __ My primary care doctor __ Other ______ | All (family, ONC, PCP) = 14 Family only = 2 Oncologist = 12 PCP = 14, one said “not PCP” |
| Was this patient information sheet helpful to you? | Yes = 25 No = 1, “Bummer” NA = 2 |
NA = no answer; ONC = oncologist; PCP = primary care provider
In some cases, the average prognosis and treatment benefit, although small, was bigger than the person thought before the exercise. Nearly all found it helpful. Some illustrative comments are shown in Table 2.
We did not formally measure the time to complete the screening questions, pre- and posttests, pre- and post-HHI, and decision aid; but in most cases it took less than 20 minutes to complete the whole package including the pre- and post-tests. Review of the decision aid with the patient always took less than 5 minutes, even when we were reading it with the patient and family. This is consistent with work showing that oncologists state that completing an advance directive will take too much time but, in fact, it takes less than 10 minutes.[19] and [20]
Discussion
Historical data show that patients know little about their prognosis and the effect that treatment will have on their cancer. Yet, this knowledge is essential to making informed choices about treatment benefits, risks, and even costs. When tested in randomized controlled trials, decision aids led to more involvement in decision making.[21] and [22] However, there were no decision aids available about metastatic incurable disease, despite some promising early starts[23], [24], [25], [26], [27] and [28] and only one about first-line treatment,29 so we made a simple one. A successful decision aid may allow patients to discuss their situations with their physicians and develop management strategies that best concur with personal goals and preferences and help patients make plans in other areas of life.
Our findings suggest that most people do want honest information, even if the news is bad. We found that 27 of 27 enrolled patients initially reported wanting to know all the available information about their cancer, prognosis, treatment benefits, and treatment side effects. Also, 26 of 27 patients were able to complete the decision aid fully, our main outcome measure. While approximately 10% of available patients were excluded from accrual by their oncologists or oncology nurses due to preexisting distress, fear of distress in the patient or family member, uncontrolled symptoms, or psychiatric illness, in general there was excellent acceptance of the study by patients and oncologists. In this pilot study we did not investigate the attitudes of nonparticipants nor were we able to collect sociodemographic data to determine nonresponse bias, that is, whether certain types of patients are more likely to decline participation in the study.
Participants in the study were overoptimistic about their chances of cure, potential treatment response, symptom relief, and survival. None of these patients had curable disease, but 63% thought that a person with metastatic cancer of their type could be cured and gave the average chance of cure as 52%. Inaccurate assessment of cure rates decreased postintervention. At the pretest 14/27 (52%) believed a person with cancer similar to theirs could be cured, which changed to 8/26 (31%) at the posttest. This agrees with other studies that showed that patients mistook palliative radiation for curative radiation about one-third of the time, even when provided with accurate information.[1], [30] and [31]
Knowledge of prognosis and planning for the future is important as there is evidence of benefit to having the discussion about treatment outcomes. Recent data show improved quality of care, improved quality of life, and improved caregiver quality of life if the physician discusses death with the patient and family.5 Transplant patients with advanced directives had more than a twofold survival advantage over those without them.27 Conversely, over- or underestimating survival or treatment benefit can lead to bad health outcomes. Stem-cell transplant patients who were overoptimistic lived no longer than those with realistic views.[32], [33] and [34] Cancer patients who overestimated their survival were more likely to die a “bad” death (defined as death in an intensive care unit, on a ventilator, or with multiple hospitalizations and emergency room visits) without achieving life extension.35 It may be that the 16%–20% of patients with incurable solid tumors who start a new chemotherapy regimen within 2 weeks of death,36 when they are unlikely to benefit, simply do not know the prognosis or treatment effect or have different perspectives.37 Alternatively, we do not know how many patients decline second- and nth-line chemotherapy without knowing the full benefits and risks and who might choose chemotherapy if they knew second- or nth-line chemotherapy improved survival, pain scores, or quality of life. For instance, 40% of breast cancer patients will have some disease control from fourth-line chemotherapy for up to 4 months even if there is no evidence of improved survival.38
Patients consistently tell us to be truthful, compassionate, and clear and to stay the course with them.[39] and [40] Despite nearly all American patients stating that they want full disclosure about their prognosis, treatment options, and expected outcomes, most patients do not receive such information41 or receive such information far too late in their course.42 Even if terminally ill patients with cancer requested survival estimates, doctors would provide such estimates only 37% of the time, often an overestimate;7 and a recent meta-analysis showed that cancer physicians consistently overestimated prognosis by at least 30%.43 Honest information respects the autonomy of a patient to make decisions based on what is known about the outcomes of such decisions.44 Such information should not be forced on a patient, but the patient should be told that the information is available and that he or she has the right to accept or decline the information.45
When we started this project, colleagues were concerned about whether patients would want such information, that patients would be distressed by poor prognosis, that patients would give up hope, and that the procedures would take too much time. We also were concerned about the effect of giving such bad news on the provider, when prior research showed negative effects on the information-giver's mood and affect from such encounters46 and that doctors in general protect themselves by not giving bad news.47 Completion of the decision aid was difficult for the interviewers, too. Some commented on how hard it was to give “bad” information about chance of cure and expected survival, even for patients they did not know. While patients may be more comfortable having advance directive discussions with a doctor they do not know rather than their oncologist,48 it can still be hard for the provider. Surprisingly, it rarely took more than 20 minutes to discuss the information including the tests since the information was preprinted.
Patients vary in their approach to decision making, but the decisions should at least start with good information. Based on these preliminary findings, the piloted intervention is significant because it can lead to measurable impacts on knowledge about prognosis and appears to be judged helpful. We do not know the impact of full and truthful information on patient knowledge, decision making, hope, attendant choices about advanced medical directives, chemotherapy use, or hospice use. The next steps are to make the information available directly to patients on the Internet, which is in progress. The purpose is not to increase or decrease the use of palliative chemotherapy or hospice care; the lack of research into the decisions fully informed patients make precludes any such prediction. Since the intervention appears to be successful in this pilot trial, it will be tested in conjunction with standard care in a randomized clinical trial with measurement of quality of care, quality of life, and health-care cost outcomes.
Acknowledgments
This research was supported by VCU School of Medicine Research Year Out, GO8 LM0095259 from the National Library of Medicine (T. J. S., L. L., J. K.), and R01CA116227-01 (T. J. S.) from the National Cancer Institute.
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Appendix A
Decision Aids
Patient Name: ___
Date: ___/___/___
Lung Cancer Second Line Chemotherapy
What is my chance of being alive at one year if I take chemotherapy, or do best supportive care such as hospice?
Chemotherapy with a drug like docetaxel (Taxotere®) or pemetrexed (Alimta®) improves the chance of being alive at one year by 18 out of 100 people. With chemotherapy, 37 of 100 people were alive at one year. Without chemotherapy, 11 of 100 were alive.
Patients receiving docetaxel (Taxotere®) chemotherapy lived an average of 7.5 months, versus 4.6 months if they did not take chemotherapy. In other words, they lived 2 to 3 months longer.
If you are having cancer-related symptoms that limit your daily activities, the chances of being alive at one year are less than that described above.
The numbers given here are what happens to the average person with this disease in this situation. Half the patients will do better than this, and half will do worse. Your situation could be better or worse. The numbers given for the chance of cure are very accurate. The numbers are given to help you with your own decision making.
What is the chance of my cancer shrinking by half?
About 6 of 100 people will have their cancer shrink by half.
If you are having cancer-related symptoms that limit your daily activities, the chances are less than that described above.
What is the chance of my being cured by chemotherapy?
In this setting, there is no chance of cure. The goal may change to controlling the disease and any symptoms for as long as possible. You may want to talk with your doctor about your own chances and goals of therapy.
How long will chemotherapy make my cancer shrink, if it does?
For all patients who did not get chemotherapy, the average time before the cancer grew was 7 weeks. For patients who got chemotherapy, the average time before the cancer grew was 11 weeks.
What did chemotherapy do to quality of life?
Chemotherapy helped reduce pain scores and did not make quality of life worse.
What are the most common side effects?
The most common side effects will vary with the type of treatment given.
Some of the most common ones include the following:
Mucositis (mouth sores).
Nausea/vomiting; usually controllable.
Alopecia (hair loss).
Neutropenia (low white blood cell count) and infection requiring antibiotics.
Neuropathy (numbness and pain in the hands and feet).
Are there other issues that I should address at this time?
Many people use this time to address a life review–what they have learned during life that they want to share with their families, and planning for events in the future like birthdays or weddings).
Some people address spiritual issues.
Some people address financial issues like a will.
Some people address Advance Directives (Living Wills).
For instance, if you could not speak for yourself, who would you want to make decisions about your care?
If your heart stopped beating, or you stopped breathing, due to the cancer worsening, would you want to have resuscitation (CPR), or be allowed to die naturally without resuscitation?
Some people use this time to discuss with their loved ones how they would like to spend the rest of their life. For instance, where do you want to spend your last days? Where do you want to die?
Do you want to have hospice involved?
These are all difficult issues, but important to discuss with your family and your health care professionals.
Correspondence to: Thomas J. Smith, MD, Virginia Commonwealth University, Division of Hematology/Oncology and Palliative Care, MCV Box 980230, Richmond, VA 23298–0230; telephone: (804) 828–9723; fax: (804) 828–8079