William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Park et al)

Objective: To compare rates of adverse events and 30-day readmission among patients with dementia who undergo percutaneous coronary intervention (PCI) with those without dementia.

Design: This cohort study used a national database of hospital readmissions developed by the Agency for Healthcare Research and Quality.

Setting and participants: Data from State Inpatient Databases were used to derive this national readmissions database representing 80% of hospitals from 28 states that contribute data. The study included all individuals aged 18 years and older who were identified to have had a PCI procedure in the years 2017 and 2018. International Classification of Diseases, Tenth Revision (ICD-10) codes were used to identify PCI procedures, including drug-eluting stent placement, bare-metal stent placement, and balloon angioplasty, performed in patients who presented with myocardial infarction and unstable angina and those with stable ischemic heart disease. Patients were stratified into those with or without dementia, also defined using ICD-10 codes. A total of 755,406 index hospitalizations were included; 2.3% of the patients had dementia.

Main outcome measures: The primary study outcome was 30-day all-cause readmission, with the cause classified as cardiovascular or noncardiovascular. Secondary outcome measures examined were delirium, in-hospital mortality, cardiac arrest, blood transfusion, acute kidney injury, fall in hospital, length of hospital stay, and other adverse outcomes. Location at discharge was also examined. Other covariates included in the analysis were age, sex, comorbidities, hospital characteristics, primary payer, and median income. For analysis, a propensity score matching algorithm was applied to match patients with and without dementia. Kaplan-Meier curves were used to examine 30-day readmission rates, and a Cox proportional hazards model was used to calculate hazard ratios (HR) for those with and without dementia. For secondary outcomes, logistic regression models were used to calculate odds ratios (OR) of outcomes between those with and without dementia.

Main results: The average age of those with dementia was 78.8 years vs 64.9 years in those without dementia. Women made up 42.8% of those with dementia and 31.3% of those without dementia. Those with dementia also had higher rates of comorbidities, such as heart failure, renal failure, and depression. After propensity score matching, 17,309 and 17,187 patients with and without dementia, respectively, were included. Covariates were balanced between the 2 groups after matching. For the primary outcome, patients with dementia were more likely to be readmitted at 30 days (HR, 1.11; 95% CI, 1.05-1.18; P < .01) when compared to those without dementia. For other adverse outcomes, delirium was significantly more likely to occur for those with dementia (OR, 4.37; 95% CI, 3.69-5.16; P < .01). Patients with dementia were also more likely to die in hospital (OR, 1.15; 95% CI, 1.01-1.30; P = .03), have cardiac arrest (OR, 1.19; 95% CI, 1.01-1.39; P = .04), receive a blood transfusion (OR, 1.17; 95% CI, 1.00-1.36; P = .05), experience acute kidney injury (OR, 1.30; 95% CI, 1.21-1.39; P < .01), and fall in hospital (OR, 2.51; 95% CI, 2.06-3.07; P < .01). Hospital length of stay was higher for those with dementia, with a mean difference of 1.43 days. For discharge location, patients with dementia were more likely to be sent to a skilled nursing facility (30.1% vs 12.2%) and less likely to be discharged home.

Conclusion: Patients with dementia are more likely to experience adverse events, including delirium, mortality, kidney injury, and falls after PCI, and are more likely to be readmitted to the hospital in 30 days compared to those without dementia.

Study 2 Overview (Gilmore-Bykovskyi et al)

Objective: To examine the association between race and 30-day readmissions in Black and non-Hispanic White Medicare beneficiaries with dementia.

Design: This was a retrospective cohort study that used 100% Medicare fee-for service claims data from all hospitalizations between January 1, 2014, and November 30, 2014, for all enrollees with a dementia diagnosis. The claims data were linked to the patient, hospital stay, and hospital factors. Patients with dementia were identified using a validated algorithm that requires an inpatient, skilled nursing facility, home health, or Part B institutional or noninstitutional claim with a qualifying diagnostic code during a 3-year period. Persons enrolled in a health maintenance organization plan were excluded.

Main outcome measures: The primary outcome examined in this study was 30-day all-cause readmission. Self-reported race and ethnic identity was a baseline covariate. Persons who self-reported Black or non-Hispanic White race were included in the study; other categories of race and ethnicity were excluded because of prior evidence suggesting low accuracy of these categories in Medicare claims data. Other covariates included neighborhood disadvantage, measured using the Area Deprivation Index (ADI), and rurality; hospital-level and hospital stay–level characteristics such as for-profit status and number of annual discharges; and individual demographic characteristics and comorbidities. The ADI is constructed using variables of poverty, education, housing, and employment and is represented as a percentile ranking of level of disadvantage. Unadjusted and adjusted analyses of 30-day hospital readmission were conducted. Models using various levels of adjustment were constructed to examine the contributions of the identified covariates to the estimated association between 30-day readmission and race.

Main results: A total of 1,523,142 index hospital stays among 945,481 beneficiaries were included; 215,815 episodes were among Black beneficiaries and 1,307,327 episodes were among non-Hispanic White beneficiaries. Mean age was 81.5 years, and approximately 61% of beneficiaries were female. Black beneficiaries were younger but had higher rates of dual Medicare/Medicaid eligibility and disability; they were also more likely to reside in disadvantaged neighborhoods. Black beneficiaries had a 30-day readmission rate of 24.1% compared with 18.5% in non-Hispanic White beneficiaries (unadjusted OR, 1.37; 95% CI, 1.35-1.39). The differences in outcomes persisted after adjusting for geographic factors, social factors, hospital characteristics, hospital stay factors, demographics, and comorbidities, suggesting that unmeasured underlying racial disparities not included in this model accounted for the differences. The effects of certain variables, such as neighborhood, differed by race; for example, the protective effect of living in a less disadvantaged neighborhood was observed among White beneficiaries but not Black beneficiaries.

Conclusion: Racial and geographic disparities in 30-day readmission rates were observed among Medicare beneficiaries with dementia. Protective effects associated with neighborhood advantage may confer different levels of benefit for people of different race.

Commentary

Adults living with dementia are at higher risk of adverse outcomes across settings. In the first study, by Park et al, among adults who underwent a cardiac procedure (PCI), those with dementia were more likely to experience adverse events compared to those without dementia. These outcomes include increased rates of 30-day readmissions, delirium, cardiac arrest, and falls. These findings are consistent with other studies that found a similar association among patients who underwent other cardiac procedures, such as transcatheter aortic valve replacement.¹ Because dementia is a strong predisposing factor for delirium, it is not surprising that delirium is observed across patients who underwent different procedures or hospitalization episodes.² Because of the potential hazards for inpatients with dementia, hospitals have developed risk-reduction programs, such as those that promote recognition of dementia, and management strategies that reduce the risk of delirium.³ Delirium prevention may also impact other adverse outcomes, such as falls, discharge to institutional care, and readmissions.

Racial disparities in care outcomes have been documented across settings, including hospital⁴ and hospice care settings.⁵ In study 2, by Gilmore-Bykovskyi et al, the findings of higher rates of hospital readmission among Black patients when compared to non-Hispanic White patients were not surprising. The central finding of this study is that even when accounting for various levels of factors, including hospital-level, hospital stay–level, individual (demographics, comorbidities), and neighborhood characteristics (disadvantage), the observed disparity diminished but persisted, suggesting that while these various levels of factors contributed to the observed disparity, other unmeasured factors also contributed. Another key finding is that the effect of the various factors examined in this study may affect different subgroups in different ways, suggesting underlying factors, and thus potential solutions to reduce disparities in care outcomes, could differ among subgroups.

Applications for Clinical Practice and System Implementation

These 2 studies add to the literature on factors that can affect 30-day hospital readmission rates in patients with dementia. These data could allow for more robust discussions of what to anticipate when adults with dementia undergo specific procedures, and also further build the case that improvements in care, such as delirium prevention programs, could offer benefits. The observation about racial and ethnic disparities in care outcomes among patients with dementia highlights the continued need to better understand the drivers of these disparities so that hospital systems and policy makers can consider and test possible solutions. Future studies should further disentangle the relationships among the various levels of factors and observed disparities in outcomes, especially for this vulnerable population of adults living with dementia.

Practice Points

• Clinicians should be aware of the additional risks for poor outcomes that dementia confers.
• Awareness of this increased risk will inform discussions of risks and benefits for older adults considered for procedures.

–William W. Hung, MD, MPH

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Bhasin et al)

Objective: To examine the effect of a multifactorial intervention for fall prevention on fall injury in community-dwelling older adults.

Design: This was a pragmatic, cluster randomized trial conducted in 86 primary care practices across 10 health care systems.

Setting and participants: The primary care sites were selected based on the prespecified criteria of size, ability to implement the intervention, proximity to other practices, accessibility to electronic health records, and access to community-based exercise programs. The primary care practices were randomly assigned to intervention or control.

Eligibility criteria for participants at those practices included age 70 years or older, dwelling in the community, and having an increased risk of falls, as determined by a history of fall-related injury in the past year, 2 or more falls in the past year, or being afraid of falling because of problems with balance or walking. Exclusion criteria were inability to provide consent or lack of proxy consent for participants who were determined to have cognitive impairment based on screening, and inability to speak English or Spanish. A total of 2802 participants were enrolled in the intervention group, and 2649 participants were enrolled in the control group.

Intervention: The intervention contained 5 components: a standardized assessment of 7 modifiable risk factors for fall injuries; standardized protocol-driven recommendations for management of risk factors; an individualized care plan focused on 1 to 3 risk factors; implementation of care plans, including referrals to community-based programs; and follow-up care conducted by telephone or in person. The modifiable risk factors included impairment of strength, gait, or balance; use of medications related to falls; postural hypotension; problems with feet or footwear; visual impairment; osteoporosis or vitamin D deficiency; and home safety hazards. The intervention was delivered by nurses who had completed online training modules and face-to-face training sessions focused on the intervention and motivational interviewing along with continuing education, in partnership with participants and their primary care providers. In the control group, participants received enhanced usual care, including an informational pamphlet, and were encouraged to discuss fall prevention with their primary care provider, including the results of their screening evaluation.

Main outcome measures: The primary outcome of the study was the first serious fall injury in a time-to-event analysis, defined as a fall resulting in a fracture (other than thoracic or lumbar vertebral fracture), joint dislocation, cut requiring closure, head injury requiring hospitalization, sprain or strain, bruising or swelling, or other serious injury. The secondary outcome was first patient-reported fall injury, also in a time-to-event analysis, ascertained by telephone interviews conducted every 4 months. Other outcomes included hospital admissions, emergency department visits, and other health care utilization. Adjudication of fall events and injuries was conducted by a team blinded to treatment assignment and verified using administrative claims data, encounter data, or electronic health record review.

Main results: The intervention and control groups were similar in terms of sex and age: 62.5% vs 61.5% of participants were women, and mean (SD) age was 79.9 (5.7) years and 79.5 (5.8) years, respectively. Other demographic characteristics were similar between groups. For the primary outcome, the rate of first serious injury was 4.9 per 100 person-years in the intervention group and 5.3 per 100 person-years in the control group, with a hazard ratio of 0.92 (95% CI, 0.80-1.06; P = .25). For the secondary outcome of patient-reported fall injury, there were 25.6 events per 100 person-years in the intervention group and 28.6 in the control group, with a hazard ratio of 0.90 (95% CI, 0.83-0.99; P =0.004). Rates of hospitalization and other secondary outcomes were similar between groups.

Conclusion: The multifactorial STRIDE intervention did not reduce the rate of serious fall injury when compared to enhanced usual care. The intervention did result in lower rates of fall injury by patient report, but no other significant outcomes were seen.

Study 2 Overview (Stark et al)

Objective: To examine the effect of a behavioral home hazard removal intervention for fall prevention on risk of fall in community-dwelling older adults.

Design: This randomized clinical trial was conducted at a single site in St. Louis, Missouri. Participants were community-dwelling older adults who received services from the Area Agency on Aging (AAA). Inclusion criteria included age 65 years and older, having 1 or more falls in the previous 12 months or being worried about falling by self report, and currently receiving services from an AAA. Exclusion criteria included living in an institution or being severely cognitively impaired and unable to follow directions or report falls. Participants who met the criteria were contacted by phone and invited to participate. A total of 310 participants were enrolled in the study, with an equal number of participants assigned to the intervention and control groups.

Intervention: The intervention included hazard identification and removal after a comprehensive assessment of participants, their behaviors, and the environment; this assessment took place during the first visit, which lasted approximately 80 minutes. A home hazard removal plan was developed, and in the second session, which lasted approximately 40 minutes, remediation of hazards was carried out. A third session for home modification that lasted approximately 30 minutes was conducted, if needed. At 6 months after the intervention, a booster session to identify and remediate any new home hazards and address issues was conducted. Specific interventions, as identified by the assessment, included minor home repair such as grab bars, adaptive equipment, task modification, and education. Shared decision making that enabled older adults to control changes in their homes, self-management strategies to improve awareness, and motivational enhancement strategies to improve acceptance were employed. Scripted algorithms and checklists were used to deliver the intervention. For usual care, an annual assessment and referrals to community services, if needed, were conducted in the AAA.

Main outcome measures: The primary outcome of the study was the number of days to first fall in 12 months. Falls were defined as unintentional movements to the floor, ground, or object below knee level, and falls were recorded through a daily journal for 12 months. Participants were contacted by phone if they did not return the journal or reported a fall. Participants were interviewed to verify falls and determine whether a fall was injurious. Secondary outcomes included rate of falls per person per 12 months; daily activity performance measured using the Older Americans Resources and Services Activities of Daily Living scale; falls self-efficacy, which measures confidence performing daily activities without falling; and quality of life using the SF-36 at 12 months.

Main results: Most of the study participants were women (74%), and mean (SD) age was 75 (7.4) years. Study retention was similar between the intervention and control groups, with 82% completing the study in the intervention group compared with 81% in the control group. Fidelity to the intervention, as measured by a checklist by the interventionist, was 99%, and adherence to home modification, as measured by number of home modifications in use by self report, was high at 92% at 6 months and 91% at 12 months. For the primary outcome, fall hazard was not different between the intervention and control groups (hazard ratio, 0.9; 95% CI, 0.66-1.27). For the secondary outcomes, the rate of falling was lower in the intervention group compared with the control group, with a relative risk of 0.62 (95% CI, 0.40-0.95). There was no difference in other secondary outcomes of daily activity performance, falls self-efficacy, or quality of life.

Conclusion: Despite high adherence to home modifications and fidelity to the intervention, this home hazard removal program did not reduce the risk of falling when compared to usual care. It did reduce the rate of falls, although no other effects were observed.

Commentary

Observational studies have identified factors that contribute to falls,¹ and over the past 30 years a number of intervention trials designed to reduce the risk of falling have been conducted. A recent Cochrane review, published prior to the Bhasin et al and Stark et al trials, looked at the effect of multifactorial interventions for fall prevention across 62 trials that included 19,935 older adults living in the community. The review concluded that multifactorial interventions may reduce the rate of falls, but this conclusion was based on low-quality evidence and there was significant heterogeneity across the studies.²

The STRIDE randomized trial represents the latest effort to address the evidence gap around fall prevention, with the STRIDE investigators hoping this would be the definitive trial that leads to practice change in fall prevention. Smaller trials that have demonstrated effectiveness were brought to scale in this large randomized trial that included 86 practices and more than 5000 participants. The investigators used risk of injurious falls as the primary outcome, as this outcome is considered the most clinically meaningful for the study population. The results, however, were disappointing: the multifactorial intervention in STRIDE did not result in a reduction of risk of injurious falls. Challenges in the implementation of this large trial may have contributed to its results; falls care managers, key to this multifactorial intervention, reported difficulties in navigating complex relationships with patients, families, study staff, and primary care practices during the study. Barriers reported included clinical space limitations, variable buy-in from providers, and turnover of practice staff and providers.³ Such implementation factors may have resulted in the divergent results between smaller clinical trials and this large-scale trial conducted across multiple settings.

The second study, by Stark et al, examined a home modification program and its effect on risk of falls. A prior Cochrane review examining the effect of home safety assessment and modification indicates that these strategies are effective in reducing the rate of falls as well as the risk of falling.⁴ The results of the current trial showed a reduction in the rate of falls but not in the risk of falling; however, this study did not examine outcomes of serious injurious falls, which may be more clinically meaningful. The Stark et al study adds to the existing literature showing that home modification may have an impact on fall rates. One noteworthy aspect of the Stark et al trial is the high adherence rate to home modification in a community-based approach; perhaps the investigators’ approach can be translated to real-world use.

Applications for Clinical Practice and System Implementation

The role of exercise programs in reducing fall rates is well established,⁵ but neither of these studies focused on exercise interventions. STRIDE offered community-based exercise program referral, but there is variability in such programs and study staff reported challenges in matching participants with appropriate exercise programs.³ Further studies that examine combinations of multifactorial falls risk reduction, exercise, and home safety, with careful consideration of implementation challenges to assure fidelity and adherence to the intervention, are needed to ascertain the best strategy for fall prevention for older adults at risk.

Given the results of these trials, it is difficult to recommend one falls prevention intervention over another. Clinicians should continue to identify falls risk factors using standardized assessments and determine which factors are modifiable.

Practice Points

• Incorporating assessments of falls risk in primary care is feasible, and such assessments can identify important risk factors.
• Clinicians and health systems should identify avenues, such as developing programmatic approaches, to providing home safety assessment and intervention, exercise options, medication review, and modification of other risk factors.
• Ensuring delivery of these elements reliably through programmatic approaches with adequate follow-up is key to preventing falls in this population.

—William W. Hung, MD, MPH

Study 1 Overview (Bhasin et al)

Objective: To examine the effect of a multifactorial intervention for fall prevention on fall injury in community-dwelling older adults.

Design: This was a pragmatic, cluster randomized trial conducted in 86 primary care practices across 10 health care systems.

Setting and participants: The primary care sites were selected based on the prespecified criteria of size, ability to implement the intervention, proximity to other practices, accessibility to electronic health records, and access to community-based exercise programs. The primary care practices were randomly assigned to intervention or control.

Eligibility criteria for participants at those practices included age 70 years or older, dwelling in the community, and having an increased risk of falls, as determined by a history of fall-related injury in the past year, 2 or more falls in the past year, or being afraid of falling because of problems with balance or walking. Exclusion criteria were inability to provide consent or lack of proxy consent for participants who were determined to have cognitive impairment based on screening, and inability to speak English or Spanish. A total of 2802 participants were enrolled in the intervention group, and 2649 participants were enrolled in the control group.

Intervention: The intervention contained 5 components: a standardized assessment of 7 modifiable risk factors for fall injuries; standardized protocol-driven recommendations for management of risk factors; an individualized care plan focused on 1 to 3 risk factors; implementation of care plans, including referrals to community-based programs; and follow-up care conducted by telephone or in person. The modifiable risk factors included impairment of strength, gait, or balance; use of medications related to falls; postural hypotension; problems with feet or footwear; visual impairment; osteoporosis or vitamin D deficiency; and home safety hazards. The intervention was delivered by nurses who had completed online training modules and face-to-face training sessions focused on the intervention and motivational interviewing along with continuing education, in partnership with participants and their primary care providers. In the control group, participants received enhanced usual care, including an informational pamphlet, and were encouraged to discuss fall prevention with their primary care provider, including the results of their screening evaluation.

Main outcome measures: The primary outcome of the study was the first serious fall injury in a time-to-event analysis, defined as a fall resulting in a fracture (other than thoracic or lumbar vertebral fracture), joint dislocation, cut requiring closure, head injury requiring hospitalization, sprain or strain, bruising or swelling, or other serious injury. The secondary outcome was first patient-reported fall injury, also in a time-to-event analysis, ascertained by telephone interviews conducted every 4 months. Other outcomes included hospital admissions, emergency department visits, and other health care utilization. Adjudication of fall events and injuries was conducted by a team blinded to treatment assignment and verified using administrative claims data, encounter data, or electronic health record review.

Main results: The intervention and control groups were similar in terms of sex and age: 62.5% vs 61.5% of participants were women, and mean (SD) age was 79.9 (5.7) years and 79.5 (5.8) years, respectively. Other demographic characteristics were similar between groups. For the primary outcome, the rate of first serious injury was 4.9 per 100 person-years in the intervention group and 5.3 per 100 person-years in the control group, with a hazard ratio of 0.92 (95% CI, 0.80-1.06; P = .25). For the secondary outcome of patient-reported fall injury, there were 25.6 events per 100 person-years in the intervention group and 28.6 in the control group, with a hazard ratio of 0.90 (95% CI, 0.83-0.99; P =0.004). Rates of hospitalization and other secondary outcomes were similar between groups.

Conclusion: The multifactorial STRIDE intervention did not reduce the rate of serious fall injury when compared to enhanced usual care. The intervention did result in lower rates of fall injury by patient report, but no other significant outcomes were seen.

Study 2 Overview (Stark et al)

Objective: To examine the effect of a behavioral home hazard removal intervention for fall prevention on risk of fall in community-dwelling older adults.

Design: This randomized clinical trial was conducted at a single site in St. Louis, Missouri. Participants were community-dwelling older adults who received services from the Area Agency on Aging (AAA). Inclusion criteria included age 65 years and older, having 1 or more falls in the previous 12 months or being worried about falling by self report, and currently receiving services from an AAA. Exclusion criteria included living in an institution or being severely cognitively impaired and unable to follow directions or report falls. Participants who met the criteria were contacted by phone and invited to participate. A total of 310 participants were enrolled in the study, with an equal number of participants assigned to the intervention and control groups.

Intervention: The intervention included hazard identification and removal after a comprehensive assessment of participants, their behaviors, and the environment; this assessment took place during the first visit, which lasted approximately 80 minutes. A home hazard removal plan was developed, and in the second session, which lasted approximately 40 minutes, remediation of hazards was carried out. A third session for home modification that lasted approximately 30 minutes was conducted, if needed. At 6 months after the intervention, a booster session to identify and remediate any new home hazards and address issues was conducted. Specific interventions, as identified by the assessment, included minor home repair such as grab bars, adaptive equipment, task modification, and education. Shared decision making that enabled older adults to control changes in their homes, self-management strategies to improve awareness, and motivational enhancement strategies to improve acceptance were employed. Scripted algorithms and checklists were used to deliver the intervention. For usual care, an annual assessment and referrals to community services, if needed, were conducted in the AAA.

Main outcome measures: The primary outcome of the study was the number of days to first fall in 12 months. Falls were defined as unintentional movements to the floor, ground, or object below knee level, and falls were recorded through a daily journal for 12 months. Participants were contacted by phone if they did not return the journal or reported a fall. Participants were interviewed to verify falls and determine whether a fall was injurious. Secondary outcomes included rate of falls per person per 12 months; daily activity performance measured using the Older Americans Resources and Services Activities of Daily Living scale; falls self-efficacy, which measures confidence performing daily activities without falling; and quality of life using the SF-36 at 12 months.

Main results: Most of the study participants were women (74%), and mean (SD) age was 75 (7.4) years. Study retention was similar between the intervention and control groups, with 82% completing the study in the intervention group compared with 81% in the control group. Fidelity to the intervention, as measured by a checklist by the interventionist, was 99%, and adherence to home modification, as measured by number of home modifications in use by self report, was high at 92% at 6 months and 91% at 12 months. For the primary outcome, fall hazard was not different between the intervention and control groups (hazard ratio, 0.9; 95% CI, 0.66-1.27). For the secondary outcomes, the rate of falling was lower in the intervention group compared with the control group, with a relative risk of 0.62 (95% CI, 0.40-0.95). There was no difference in other secondary outcomes of daily activity performance, falls self-efficacy, or quality of life.

Conclusion: Despite high adherence to home modifications and fidelity to the intervention, this home hazard removal program did not reduce the risk of falling when compared to usual care. It did reduce the rate of falls, although no other effects were observed.

Commentary

Observational studies have identified factors that contribute to falls,¹ and over the past 30 years a number of intervention trials designed to reduce the risk of falling have been conducted. A recent Cochrane review, published prior to the Bhasin et al and Stark et al trials, looked at the effect of multifactorial interventions for fall prevention across 62 trials that included 19,935 older adults living in the community. The review concluded that multifactorial interventions may reduce the rate of falls, but this conclusion was based on low-quality evidence and there was significant heterogeneity across the studies.²

The STRIDE randomized trial represents the latest effort to address the evidence gap around fall prevention, with the STRIDE investigators hoping this would be the definitive trial that leads to practice change in fall prevention. Smaller trials that have demonstrated effectiveness were brought to scale in this large randomized trial that included 86 practices and more than 5000 participants. The investigators used risk of injurious falls as the primary outcome, as this outcome is considered the most clinically meaningful for the study population. The results, however, were disappointing: the multifactorial intervention in STRIDE did not result in a reduction of risk of injurious falls. Challenges in the implementation of this large trial may have contributed to its results; falls care managers, key to this multifactorial intervention, reported difficulties in navigating complex relationships with patients, families, study staff, and primary care practices during the study. Barriers reported included clinical space limitations, variable buy-in from providers, and turnover of practice staff and providers.³ Such implementation factors may have resulted in the divergent results between smaller clinical trials and this large-scale trial conducted across multiple settings.

The second study, by Stark et al, examined a home modification program and its effect on risk of falls. A prior Cochrane review examining the effect of home safety assessment and modification indicates that these strategies are effective in reducing the rate of falls as well as the risk of falling.⁴ The results of the current trial showed a reduction in the rate of falls but not in the risk of falling; however, this study did not examine outcomes of serious injurious falls, which may be more clinically meaningful. The Stark et al study adds to the existing literature showing that home modification may have an impact on fall rates. One noteworthy aspect of the Stark et al trial is the high adherence rate to home modification in a community-based approach; perhaps the investigators’ approach can be translated to real-world use.

Applications for Clinical Practice and System Implementation

The role of exercise programs in reducing fall rates is well established,⁵ but neither of these studies focused on exercise interventions. STRIDE offered community-based exercise program referral, but there is variability in such programs and study staff reported challenges in matching participants with appropriate exercise programs.³ Further studies that examine combinations of multifactorial falls risk reduction, exercise, and home safety, with careful consideration of implementation challenges to assure fidelity and adherence to the intervention, are needed to ascertain the best strategy for fall prevention for older adults at risk.

Given the results of these trials, it is difficult to recommend one falls prevention intervention over another. Clinicians should continue to identify falls risk factors using standardized assessments and determine which factors are modifiable.

Practice Points

• Incorporating assessments of falls risk in primary care is feasible, and such assessments can identify important risk factors.
• Clinicians and health systems should identify avenues, such as developing programmatic approaches, to providing home safety assessment and intervention, exercise options, medication review, and modification of other risk factors.
• Ensuring delivery of these elements reliably through programmatic approaches with adequate follow-up is key to preventing falls in this population.

—William W. Hung, MD, MPH

Study 1 Overview (Bhasin et al)

Objective: To examine the effect of a multifactorial intervention for fall prevention on fall injury in community-dwelling older adults.

Design: This was a pragmatic, cluster randomized trial conducted in 86 primary care practices across 10 health care systems.

Setting and participants: The primary care sites were selected based on the prespecified criteria of size, ability to implement the intervention, proximity to other practices, accessibility to electronic health records, and access to community-based exercise programs. The primary care practices were randomly assigned to intervention or control.

Eligibility criteria for participants at those practices included age 70 years or older, dwelling in the community, and having an increased risk of falls, as determined by a history of fall-related injury in the past year, 2 or more falls in the past year, or being afraid of falling because of problems with balance or walking. Exclusion criteria were inability to provide consent or lack of proxy consent for participants who were determined to have cognitive impairment based on screening, and inability to speak English or Spanish. A total of 2802 participants were enrolled in the intervention group, and 2649 participants were enrolled in the control group.

Intervention: The intervention contained 5 components: a standardized assessment of 7 modifiable risk factors for fall injuries; standardized protocol-driven recommendations for management of risk factors; an individualized care plan focused on 1 to 3 risk factors; implementation of care plans, including referrals to community-based programs; and follow-up care conducted by telephone or in person. The modifiable risk factors included impairment of strength, gait, or balance; use of medications related to falls; postural hypotension; problems with feet or footwear; visual impairment; osteoporosis or vitamin D deficiency; and home safety hazards. The intervention was delivered by nurses who had completed online training modules and face-to-face training sessions focused on the intervention and motivational interviewing along with continuing education, in partnership with participants and their primary care providers. In the control group, participants received enhanced usual care, including an informational pamphlet, and were encouraged to discuss fall prevention with their primary care provider, including the results of their screening evaluation.

Main outcome measures: The primary outcome of the study was the first serious fall injury in a time-to-event analysis, defined as a fall resulting in a fracture (other than thoracic or lumbar vertebral fracture), joint dislocation, cut requiring closure, head injury requiring hospitalization, sprain or strain, bruising or swelling, or other serious injury. The secondary outcome was first patient-reported fall injury, also in a time-to-event analysis, ascertained by telephone interviews conducted every 4 months. Other outcomes included hospital admissions, emergency department visits, and other health care utilization. Adjudication of fall events and injuries was conducted by a team blinded to treatment assignment and verified using administrative claims data, encounter data, or electronic health record review.

Main results: The intervention and control groups were similar in terms of sex and age: 62.5% vs 61.5% of participants were women, and mean (SD) age was 79.9 (5.7) years and 79.5 (5.8) years, respectively. Other demographic characteristics were similar between groups. For the primary outcome, the rate of first serious injury was 4.9 per 100 person-years in the intervention group and 5.3 per 100 person-years in the control group, with a hazard ratio of 0.92 (95% CI, 0.80-1.06; P = .25). For the secondary outcome of patient-reported fall injury, there were 25.6 events per 100 person-years in the intervention group and 28.6 in the control group, with a hazard ratio of 0.90 (95% CI, 0.83-0.99; P =0.004). Rates of hospitalization and other secondary outcomes were similar between groups.

Conclusion: The multifactorial STRIDE intervention did not reduce the rate of serious fall injury when compared to enhanced usual care. The intervention did result in lower rates of fall injury by patient report, but no other significant outcomes were seen.

Study 2 Overview (Stark et al)

Objective: To examine the effect of a behavioral home hazard removal intervention for fall prevention on risk of fall in community-dwelling older adults.

Design: This randomized clinical trial was conducted at a single site in St. Louis, Missouri. Participants were community-dwelling older adults who received services from the Area Agency on Aging (AAA). Inclusion criteria included age 65 years and older, having 1 or more falls in the previous 12 months or being worried about falling by self report, and currently receiving services from an AAA. Exclusion criteria included living in an institution or being severely cognitively impaired and unable to follow directions or report falls. Participants who met the criteria were contacted by phone and invited to participate. A total of 310 participants were enrolled in the study, with an equal number of participants assigned to the intervention and control groups.

Intervention: The intervention included hazard identification and removal after a comprehensive assessment of participants, their behaviors, and the environment; this assessment took place during the first visit, which lasted approximately 80 minutes. A home hazard removal plan was developed, and in the second session, which lasted approximately 40 minutes, remediation of hazards was carried out. A third session for home modification that lasted approximately 30 minutes was conducted, if needed. At 6 months after the intervention, a booster session to identify and remediate any new home hazards and address issues was conducted. Specific interventions, as identified by the assessment, included minor home repair such as grab bars, adaptive equipment, task modification, and education. Shared decision making that enabled older adults to control changes in their homes, self-management strategies to improve awareness, and motivational enhancement strategies to improve acceptance were employed. Scripted algorithms and checklists were used to deliver the intervention. For usual care, an annual assessment and referrals to community services, if needed, were conducted in the AAA.

Main outcome measures: The primary outcome of the study was the number of days to first fall in 12 months. Falls were defined as unintentional movements to the floor, ground, or object below knee level, and falls were recorded through a daily journal for 12 months. Participants were contacted by phone if they did not return the journal or reported a fall. Participants were interviewed to verify falls and determine whether a fall was injurious. Secondary outcomes included rate of falls per person per 12 months; daily activity performance measured using the Older Americans Resources and Services Activities of Daily Living scale; falls self-efficacy, which measures confidence performing daily activities without falling; and quality of life using the SF-36 at 12 months.

Main results: Most of the study participants were women (74%), and mean (SD) age was 75 (7.4) years. Study retention was similar between the intervention and control groups, with 82% completing the study in the intervention group compared with 81% in the control group. Fidelity to the intervention, as measured by a checklist by the interventionist, was 99%, and adherence to home modification, as measured by number of home modifications in use by self report, was high at 92% at 6 months and 91% at 12 months. For the primary outcome, fall hazard was not different between the intervention and control groups (hazard ratio, 0.9; 95% CI, 0.66-1.27). For the secondary outcomes, the rate of falling was lower in the intervention group compared with the control group, with a relative risk of 0.62 (95% CI, 0.40-0.95). There was no difference in other secondary outcomes of daily activity performance, falls self-efficacy, or quality of life.

Conclusion: Despite high adherence to home modifications and fidelity to the intervention, this home hazard removal program did not reduce the risk of falling when compared to usual care. It did reduce the rate of falls, although no other effects were observed.

Commentary

Observational studies have identified factors that contribute to falls,¹ and over the past 30 years a number of intervention trials designed to reduce the risk of falling have been conducted. A recent Cochrane review, published prior to the Bhasin et al and Stark et al trials, looked at the effect of multifactorial interventions for fall prevention across 62 trials that included 19,935 older adults living in the community. The review concluded that multifactorial interventions may reduce the rate of falls, but this conclusion was based on low-quality evidence and there was significant heterogeneity across the studies.²

The STRIDE randomized trial represents the latest effort to address the evidence gap around fall prevention, with the STRIDE investigators hoping this would be the definitive trial that leads to practice change in fall prevention. Smaller trials that have demonstrated effectiveness were brought to scale in this large randomized trial that included 86 practices and more than 5000 participants. The investigators used risk of injurious falls as the primary outcome, as this outcome is considered the most clinically meaningful for the study population. The results, however, were disappointing: the multifactorial intervention in STRIDE did not result in a reduction of risk of injurious falls. Challenges in the implementation of this large trial may have contributed to its results; falls care managers, key to this multifactorial intervention, reported difficulties in navigating complex relationships with patients, families, study staff, and primary care practices during the study. Barriers reported included clinical space limitations, variable buy-in from providers, and turnover of practice staff and providers.³ Such implementation factors may have resulted in the divergent results between smaller clinical trials and this large-scale trial conducted across multiple settings.

The second study, by Stark et al, examined a home modification program and its effect on risk of falls. A prior Cochrane review examining the effect of home safety assessment and modification indicates that these strategies are effective in reducing the rate of falls as well as the risk of falling.⁴ The results of the current trial showed a reduction in the rate of falls but not in the risk of falling; however, this study did not examine outcomes of serious injurious falls, which may be more clinically meaningful. The Stark et al study adds to the existing literature showing that home modification may have an impact on fall rates. One noteworthy aspect of the Stark et al trial is the high adherence rate to home modification in a community-based approach; perhaps the investigators’ approach can be translated to real-world use.

Applications for Clinical Practice and System Implementation

The role of exercise programs in reducing fall rates is well established,⁵ but neither of these studies focused on exercise interventions. STRIDE offered community-based exercise program referral, but there is variability in such programs and study staff reported challenges in matching participants with appropriate exercise programs.³ Further studies that examine combinations of multifactorial falls risk reduction, exercise, and home safety, with careful consideration of implementation challenges to assure fidelity and adherence to the intervention, are needed to ascertain the best strategy for fall prevention for older adults at risk.

Given the results of these trials, it is difficult to recommend one falls prevention intervention over another. Clinicians should continue to identify falls risk factors using standardized assessments and determine which factors are modifiable.

Practice Points

• Incorporating assessments of falls risk in primary care is feasible, and such assessments can identify important risk factors.
• Clinicians and health systems should identify avenues, such as developing programmatic approaches, to providing home safety assessment and intervention, exercise options, medication review, and modification of other risk factors.
• Ensuring delivery of these elements reliably through programmatic approaches with adequate follow-up is key to preventing falls in this population.

—William W. Hung, MD, MPH

Study Overview

Objective: To examine the effect of the intervention “Eat Walk Engage,” a program that is designed to more consistently deliver age-friendly principles of care to older individuals in acute medical and surgical wards.

Design: This cluster randomized trial to examine the effect of an intervention in acute medical and surgical wards on older adults was conducted in 8 acute medical and surgical wards in 4 public hospitals in Australia from 2016 to 2017. To be eligible to participate in this trial, wards had to have the following: a patient population with 50% of patients aged 65 years and older; perceived alignment with hospital priorities; and nurse manager agreement to participation. Randomization was stratified by hospital, resulting in 4 wards with the intervention (a general medicine ward, an orthopedic ward, a general surgery ward, and a respiratory medicine ward) and 4 control wards (2 general medicine wards, a respiratory medicine ward, and a general surgery ward). Participants were consecutive inpatients aged 65 years or older who were admitted to the ward for at least 3 consecutive days during the study time period. Exclusion criteria included terminal or critical illness, severe cognitive impairment without a surrogate decision-maker, non-English speaking, or previously enrolled in the trial. Of a total of 453 patients who were eligible from the intervention wards, 188 were excluded and 6 died, yielding 259 participants in the intervention group. There were 413 patients eligible from the control wards, with 139 excluded and 3 deaths, yielding 271 participants in the control group.

Intervention: The intervention, called “Eat Walk Engage,” was developed to target older adults at risk for hospital-associated complications of delirium, functional decline, pressure injuries, falls, and incontinence, and aimed to improve care practices, environment, and culture to support age-friendly principles. This ward-based program delivered a structured improvement intervention through a site facilitator who is a nurse or allied health professional. The site facilitator identified opportunities for improvement using structured assessments of context, patient-experience interviews, and audits of care processes, and engaged an interdisciplinary working group from the intervention wards to participate in an hour-per-month meeting to develop plans for iterative improvements. Each site developed their own intervention plan; examples of interventions include shifting priorities to enable staff to increase the proportion of patients sitting in a chair for meals; designating the patient lounge as a walking destination to increase the proportion of time patients spend mobile; and using orientation boards and small groups to engage older patients in meaningful activities.

Main outcome measures: Study outcome measures included hospital-associated complications for older people, which is a composite of hospital-associated delirium, hospital-associated disability, hospital-associated incontinence, and fall or pressure injury during hospitalization. Delirium was assessed using the 3-minute diagnostic interview for Confusion Assessment Method (3D-CAM); hospital-associated disability was defined as new disability at discharge compared to 2 weeks prior to hospitalization. The primary outcome was defined as incidence of any complications and hospital length of stay. Secondary outcomes included incidence of individual complications, hospital discharge to facility, mortality at 6 months, and readmission for any cause at 6 months.

Main results: Patient characteristics for the intervention and control groups, respectively, were: 47% women with a mean age of 75.9 years (SD, 7.3), and 53% women with a mean age of 78.0 years (SD, 8.2). For the primary outcome, 46.4% of participants in the intervention group experienced any hospital complications compared with 51.8% in the control group (odds ratio [OR], 1.07; 95% CI, 0.71-1.61). The incidence of delirium was lower in the intervention group as compared with the control group (15.9% vs 31.4%; OR, 0.53; 95% CI, 0.31-0.90), while there were no other differences in the incidence rates of other complications. There was also no difference in hospital length of stay; median length of stay in the intervention group was 6 days (interquartile range [IQR], 4-9 days) compared with 7 days in the control group (IQR, 5-10), with an estimated mean difference in length of stay of 0.16 days (95% CI, –0.43 to 0.78 days). There was also no significant difference in mortality or all-cause readmission at 6 months.

Conclusion: The intervention “Eat Walk Engage” did not reduce hospital-associated complications overall or hospital length of stay, but it did reduce the incidence of hospital-associated delirium.

Older adults, often with reduced physiologic reserve, when admitted to the hospital with an acute illness may be vulnerable to potential hazards of hospitalization, such as complications from prolonged periods of immobility, pressure injury, and delirium.¹ Models of care in the inpatient setting to reduce these hazards, including the Acute Care for the Elderly model and the Mobile Acute Care for the Elderly Team model, have been examined in clinical trials.^2,3 Specifically, models of care to prevent and treat delirium have been developed and tested over the past decade.⁴ The effect of these models in improving function, reducing complications, and reducing delirium incidence has been well documented. The present study adds to the literature by testing a model that utilizes implementation science methods to take into account real-world settings. In contrast with prior models-of-care studies, the implementation of the intervention at each ward was not prescriptive, but rather was developed in each ward in an iterative manner with stakeholder input. The advantage of this approach is that engagement of stakeholders at each intervention ward obtains buy-in from staff, mobilizing staff in a way that a prescriptive model of care may not; this ultimately may lead to longer-lasting change. The iterative approach also allows for the intervention to be adapted to conditions and settings over time. Other studies have taken this approach of using implementation science to drive change.⁵ Although the intervention in the present study failed to improve the primary outcome, it did reduce the incidence of delirium, which is a significant outcome and one that may confer considerable benefits to older adults under the model’s care.

A limitation of the intervention’s nonprescriptive approach is that, because of the variation of the interventions across sites, it is difficult to discern what elements drove the clinical outcomes. In addition, it would be challenging to consider what aspects of the intervention did not work should refinement or changes be needed. How one may measure fidelity to the intervention or how well a site implements the intervention and its relationship with clinical outcomes will need to be examined further.

Application for Clinical Practice

Clinicians look to effective models of care to improve clinical outcomes for older adults in the hospital. The intervention described in this study offers a real-world approach that may need less upfront investment than other recently studied models, such as the Acute Care for the Elderly model, which requires structural and staffing enhancements. Clinicians and health system leaders may consider implementing this model to improve the care delivered to older adults in the hospital as it may help reduce the incidence of delirium among the older adults they serve.

–William W. Hung, MD, MPH

Disclosures: None.

 

Study Overview

Objective: To examine the effect of the intervention “Eat Walk Engage,” a program that is designed to more consistently deliver age-friendly principles of care to older individuals in acute medical and surgical wards.

Design: This cluster randomized trial to examine the effect of an intervention in acute medical and surgical wards on older adults was conducted in 8 acute medical and surgical wards in 4 public hospitals in Australia from 2016 to 2017. To be eligible to participate in this trial, wards had to have the following: a patient population with 50% of patients aged 65 years and older; perceived alignment with hospital priorities; and nurse manager agreement to participation. Randomization was stratified by hospital, resulting in 4 wards with the intervention (a general medicine ward, an orthopedic ward, a general surgery ward, and a respiratory medicine ward) and 4 control wards (2 general medicine wards, a respiratory medicine ward, and a general surgery ward). Participants were consecutive inpatients aged 65 years or older who were admitted to the ward for at least 3 consecutive days during the study time period. Exclusion criteria included terminal or critical illness, severe cognitive impairment without a surrogate decision-maker, non-English speaking, or previously enrolled in the trial. Of a total of 453 patients who were eligible from the intervention wards, 188 were excluded and 6 died, yielding 259 participants in the intervention group. There were 413 patients eligible from the control wards, with 139 excluded and 3 deaths, yielding 271 participants in the control group.

Intervention: The intervention, called “Eat Walk Engage,” was developed to target older adults at risk for hospital-associated complications of delirium, functional decline, pressure injuries, falls, and incontinence, and aimed to improve care practices, environment, and culture to support age-friendly principles. This ward-based program delivered a structured improvement intervention through a site facilitator who is a nurse or allied health professional. The site facilitator identified opportunities for improvement using structured assessments of context, patient-experience interviews, and audits of care processes, and engaged an interdisciplinary working group from the intervention wards to participate in an hour-per-month meeting to develop plans for iterative improvements. Each site developed their own intervention plan; examples of interventions include shifting priorities to enable staff to increase the proportion of patients sitting in a chair for meals; designating the patient lounge as a walking destination to increase the proportion of time patients spend mobile; and using orientation boards and small groups to engage older patients in meaningful activities.

Main outcome measures: Study outcome measures included hospital-associated complications for older people, which is a composite of hospital-associated delirium, hospital-associated disability, hospital-associated incontinence, and fall or pressure injury during hospitalization. Delirium was assessed using the 3-minute diagnostic interview for Confusion Assessment Method (3D-CAM); hospital-associated disability was defined as new disability at discharge compared to 2 weeks prior to hospitalization. The primary outcome was defined as incidence of any complications and hospital length of stay. Secondary outcomes included incidence of individual complications, hospital discharge to facility, mortality at 6 months, and readmission for any cause at 6 months.

Main results: Patient characteristics for the intervention and control groups, respectively, were: 47% women with a mean age of 75.9 years (SD, 7.3), and 53% women with a mean age of 78.0 years (SD, 8.2). For the primary outcome, 46.4% of participants in the intervention group experienced any hospital complications compared with 51.8% in the control group (odds ratio [OR], 1.07; 95% CI, 0.71-1.61). The incidence of delirium was lower in the intervention group as compared with the control group (15.9% vs 31.4%; OR, 0.53; 95% CI, 0.31-0.90), while there were no other differences in the incidence rates of other complications. There was also no difference in hospital length of stay; median length of stay in the intervention group was 6 days (interquartile range [IQR], 4-9 days) compared with 7 days in the control group (IQR, 5-10), with an estimated mean difference in length of stay of 0.16 days (95% CI, –0.43 to 0.78 days). There was also no significant difference in mortality or all-cause readmission at 6 months.

Conclusion: The intervention “Eat Walk Engage” did not reduce hospital-associated complications overall or hospital length of stay, but it did reduce the incidence of hospital-associated delirium.

Older adults, often with reduced physiologic reserve, when admitted to the hospital with an acute illness may be vulnerable to potential hazards of hospitalization, such as complications from prolonged periods of immobility, pressure injury, and delirium.¹ Models of care in the inpatient setting to reduce these hazards, including the Acute Care for the Elderly model and the Mobile Acute Care for the Elderly Team model, have been examined in clinical trials.^2,3 Specifically, models of care to prevent and treat delirium have been developed and tested over the past decade.⁴ The effect of these models in improving function, reducing complications, and reducing delirium incidence has been well documented. The present study adds to the literature by testing a model that utilizes implementation science methods to take into account real-world settings. In contrast with prior models-of-care studies, the implementation of the intervention at each ward was not prescriptive, but rather was developed in each ward in an iterative manner with stakeholder input. The advantage of this approach is that engagement of stakeholders at each intervention ward obtains buy-in from staff, mobilizing staff in a way that a prescriptive model of care may not; this ultimately may lead to longer-lasting change. The iterative approach also allows for the intervention to be adapted to conditions and settings over time. Other studies have taken this approach of using implementation science to drive change.⁵ Although the intervention in the present study failed to improve the primary outcome, it did reduce the incidence of delirium, which is a significant outcome and one that may confer considerable benefits to older adults under the model’s care.

A limitation of the intervention’s nonprescriptive approach is that, because of the variation of the interventions across sites, it is difficult to discern what elements drove the clinical outcomes. In addition, it would be challenging to consider what aspects of the intervention did not work should refinement or changes be needed. How one may measure fidelity to the intervention or how well a site implements the intervention and its relationship with clinical outcomes will need to be examined further.

Application for Clinical Practice

Clinicians look to effective models of care to improve clinical outcomes for older adults in the hospital. The intervention described in this study offers a real-world approach that may need less upfront investment than other recently studied models, such as the Acute Care for the Elderly model, which requires structural and staffing enhancements. Clinicians and health system leaders may consider implementing this model to improve the care delivered to older adults in the hospital as it may help reduce the incidence of delirium among the older adults they serve.

–William W. Hung, MD, MPH

Disclosures: None.

 

Study Overview

Objective: To examine the effect of the intervention “Eat Walk Engage,” a program that is designed to more consistently deliver age-friendly principles of care to older individuals in acute medical and surgical wards.

Design: This cluster randomized trial to examine the effect of an intervention in acute medical and surgical wards on older adults was conducted in 8 acute medical and surgical wards in 4 public hospitals in Australia from 2016 to 2017. To be eligible to participate in this trial, wards had to have the following: a patient population with 50% of patients aged 65 years and older; perceived alignment with hospital priorities; and nurse manager agreement to participation. Randomization was stratified by hospital, resulting in 4 wards with the intervention (a general medicine ward, an orthopedic ward, a general surgery ward, and a respiratory medicine ward) and 4 control wards (2 general medicine wards, a respiratory medicine ward, and a general surgery ward). Participants were consecutive inpatients aged 65 years or older who were admitted to the ward for at least 3 consecutive days during the study time period. Exclusion criteria included terminal or critical illness, severe cognitive impairment without a surrogate decision-maker, non-English speaking, or previously enrolled in the trial. Of a total of 453 patients who were eligible from the intervention wards, 188 were excluded and 6 died, yielding 259 participants in the intervention group. There were 413 patients eligible from the control wards, with 139 excluded and 3 deaths, yielding 271 participants in the control group.

Intervention: The intervention, called “Eat Walk Engage,” was developed to target older adults at risk for hospital-associated complications of delirium, functional decline, pressure injuries, falls, and incontinence, and aimed to improve care practices, environment, and culture to support age-friendly principles. This ward-based program delivered a structured improvement intervention through a site facilitator who is a nurse or allied health professional. The site facilitator identified opportunities for improvement using structured assessments of context, patient-experience interviews, and audits of care processes, and engaged an interdisciplinary working group from the intervention wards to participate in an hour-per-month meeting to develop plans for iterative improvements. Each site developed their own intervention plan; examples of interventions include shifting priorities to enable staff to increase the proportion of patients sitting in a chair for meals; designating the patient lounge as a walking destination to increase the proportion of time patients spend mobile; and using orientation boards and small groups to engage older patients in meaningful activities.

Main outcome measures: Study outcome measures included hospital-associated complications for older people, which is a composite of hospital-associated delirium, hospital-associated disability, hospital-associated incontinence, and fall or pressure injury during hospitalization. Delirium was assessed using the 3-minute diagnostic interview for Confusion Assessment Method (3D-CAM); hospital-associated disability was defined as new disability at discharge compared to 2 weeks prior to hospitalization. The primary outcome was defined as incidence of any complications and hospital length of stay. Secondary outcomes included incidence of individual complications, hospital discharge to facility, mortality at 6 months, and readmission for any cause at 6 months.

Main results: Patient characteristics for the intervention and control groups, respectively, were: 47% women with a mean age of 75.9 years (SD, 7.3), and 53% women with a mean age of 78.0 years (SD, 8.2). For the primary outcome, 46.4% of participants in the intervention group experienced any hospital complications compared with 51.8% in the control group (odds ratio [OR], 1.07; 95% CI, 0.71-1.61). The incidence of delirium was lower in the intervention group as compared with the control group (15.9% vs 31.4%; OR, 0.53; 95% CI, 0.31-0.90), while there were no other differences in the incidence rates of other complications. There was also no difference in hospital length of stay; median length of stay in the intervention group was 6 days (interquartile range [IQR], 4-9 days) compared with 7 days in the control group (IQR, 5-10), with an estimated mean difference in length of stay of 0.16 days (95% CI, –0.43 to 0.78 days). There was also no significant difference in mortality or all-cause readmission at 6 months.

Conclusion: The intervention “Eat Walk Engage” did not reduce hospital-associated complications overall or hospital length of stay, but it did reduce the incidence of hospital-associated delirium.

Older adults, often with reduced physiologic reserve, when admitted to the hospital with an acute illness may be vulnerable to potential hazards of hospitalization, such as complications from prolonged periods of immobility, pressure injury, and delirium.¹ Models of care in the inpatient setting to reduce these hazards, including the Acute Care for the Elderly model and the Mobile Acute Care for the Elderly Team model, have been examined in clinical trials.^2,3 Specifically, models of care to prevent and treat delirium have been developed and tested over the past decade.⁴ The effect of these models in improving function, reducing complications, and reducing delirium incidence has been well documented. The present study adds to the literature by testing a model that utilizes implementation science methods to take into account real-world settings. In contrast with prior models-of-care studies, the implementation of the intervention at each ward was not prescriptive, but rather was developed in each ward in an iterative manner with stakeholder input. The advantage of this approach is that engagement of stakeholders at each intervention ward obtains buy-in from staff, mobilizing staff in a way that a prescriptive model of care may not; this ultimately may lead to longer-lasting change. The iterative approach also allows for the intervention to be adapted to conditions and settings over time. Other studies have taken this approach of using implementation science to drive change.⁵ Although the intervention in the present study failed to improve the primary outcome, it did reduce the incidence of delirium, which is a significant outcome and one that may confer considerable benefits to older adults under the model’s care.

A limitation of the intervention’s nonprescriptive approach is that, because of the variation of the interventions across sites, it is difficult to discern what elements drove the clinical outcomes. In addition, it would be challenging to consider what aspects of the intervention did not work should refinement or changes be needed. How one may measure fidelity to the intervention or how well a site implements the intervention and its relationship with clinical outcomes will need to be examined further.

Application for Clinical Practice

Clinicians look to effective models of care to improve clinical outcomes for older adults in the hospital. The intervention described in this study offers a real-world approach that may need less upfront investment than other recently studied models, such as the Acute Care for the Elderly model, which requires structural and staffing enhancements. Clinicians and health system leaders may consider implementing this model to improve the care delivered to older adults in the hospital as it may help reduce the incidence of delirium among the older adults they serve.

–William W. Hung, MD, MPH

Disclosures: None.

 

Study Overview

Objective. To examine differences in care, specifically hospitalization towards the end of life, among nursing home residents with dementia who were Black compared with those who were White.

Design. Population based cohort study in the US. The study included all decedents with Alzheimer’s disease or related dementia (ADRD) who resided in a nursing home from 2014 to 2017. Decedents from nursing homes were identified by death within 1 day of an identified nursing home stay or within 8 days of a hospital transfer from nursing home. Data were obtained from Minimum Data Set 3.0 (MDS) which contains clinical data from all Medicaid or Medicare certified nursing homes, and from the Medicare Beneficiary Summary File (MBSF) and Medicare Provider and Analysis and Review (MedPAR) which contains hospitalization events for all Medicare Beneficiaries. These files were linked to identify nursing home residents with ADRD who were hospitalized at the end of life. ADRD diagnosis was identified from the chronic condition list from the MBSF and from MDS diagnosis list.

Setting and participants. The study included 665 033 residents from 14 595 nursing homes who died during the study period. Resident race was categorized as White or Black based on the MBSF. Severe cognitive impairment was identified using the MDS that categorized residents as severe or not using the Brief Interview for Mental Status and the Cognitive Performance Scale. The mean (SD) age of the study population was 86.7 (9.2) years for White residents and 82.6 (11.1) years for Black residents. Of the participants, 68.8% and 61.2% were female for Black and White residents, respectively. Approximately 23.4% of White and 32.5% of Black residents had severe cognitive impairment. For nursing home characteristics, 71.5% of the 14 595 nursing homes represented were for profit; average bedside was 109.5 (57.0) and occupancy rate was on average 81.2% (14.3%).

Main outcome measures. The study outcome measure was any hospitalization within 30 days prior to death. The outcome was selected as an indicator of quality of care because as older adults living with ADRD experience progressive worsening of cognitive symptoms, at the end of life when dementia is severe, advance care planning and communication with health care proxies and surrogates often result in coordinated care that avoids acute hospitalizations, which are often burdensome to both patient and family and may yield poorer quality of life.

Main results. The study found that approximately 29.5% of White decedents and 40.7% of Black decedents were hospitalized towards the end of life. Nursing homes with a higher proportion of Black residents were more likely to have residents hospitalized towards the end of life with 35% of residents hospitalized in the highest quartile (27% Black) compared with 17% hospitalized for nursing homes in the lowest quartile (0% Black).After adjusting for covariates, Black residents were 7.9% more likely to be hospitalized in the last 30 days of life compared with White residents. Blacks with severe cognitive impairment has elevated risk of hospitalization by 4.9% when compared with White residents. After accounting for nursing home facility–level characteristics, nursing homes with a low proportion of Black residents had a 5.2% higher risk of hospitalizations compared with nursing homes with no Black residents, and nursing homes with a higher percentage of Black residents had a 13.3% higher risk of hospitalization compared with nursing homes with no Black residents.

Conclusion. Race is associated with care disparities in older nursing home residents with dementia. This study suggests that hospitalization towards the end of life as a quality of care marker differs across nursing homes, and nursing homes with a higher proportion of Black residents were more likely to be hospitalized. This suggests that these nursing homes may have fewer resources and delivered poorer quality of care, and that disparities in health systems or institutions contribute to differences in quality of care for this vulnerable group.

Commentary

Disparities of health status, health care, and affordability across race and ethnicity have persisted throughout the past 20 years.¹ There is further evidence to support systemic differences that can contribute to differences in health outcomes.² Although changes in health care policy such as the Affordable Care Act have expanded health care coverage, and instituted changes that aims to improve health care quality and reduce disparities, it is clear that factors contributing to disparities in care are structural and perhaps systemic. The latest evidence comes in this study that examines racial disparities in health care quality in one of the most vulnerable populations—older adults with Alzheimer’s disease and dementia. The finding that Black nursing home residents, when compared with White residents, often has higher risk of hospitalization at the end of life, even among those with severe dementia where better coordinated care, clear goals of care and perhaps instituting palliative care would result in lower rate of hospitalization. The disparities were observed across nursing homes as well, where nursing homes with higher proportion of Black residents appear to have lower quality of care.

These findings are consistent with prior work that has examined differences in Black and White population on uptake of palliative care, discussion, and the documentation of advance care planning.³ Factors that may contribute to these differences include mistrust of the health care system among minorities, and not being connected to adequate health care resources. Family members and surrogate health care decision makers may consider receiving more aggressive care as advocating for better health care for their family members.⁴ These differences may contribute to the differences in hospitalization rates among residents within the same nursing home; however, the differences between nursing homes even after accounting for individual differences may indicate more widespread systemic differences that is associated with race. Policy changes that will address these differences are needed to level these differences so that quality care can be delivered regardless of race.⁵ For this vulnerable population with a terminal illness, approaches to enhance uptake of palliative approaches and care delivery for dementia patients at terminal stage are needed and understanding and targeting factors that contribute to low uptake of these approaches will enhance end of life care. Understanding the differences in resources and systems of care in nursing homes and perhaps how palliative care is integrated in these settings will be important to address care disparities that occurs across nursing homes.

Applications for Clinical Practice

Clinicians who take care of this population of older adults with advanced dementia should be aware of the potential for racial disparities that may lead to differences in the quality of care. The underlying reasons for these differences could be targeted so that older adults in all racial groups may have equal access to quality care including palliative approaches that avoid aggressive care for terminal illnesses across settings that may yield better care and quality of life. Policy makers and health systems leaders need to consider the current realities with racial disparities that policies need to address these differences so that they may not continue to persist in our systems of care.

Financial disclosures: None.

Study Overview

Objective. To examine differences in care, specifically hospitalization towards the end of life, among nursing home residents with dementia who were Black compared with those who were White.

Design. Population based cohort study in the US. The study included all decedents with Alzheimer’s disease or related dementia (ADRD) who resided in a nursing home from 2014 to 2017. Decedents from nursing homes were identified by death within 1 day of an identified nursing home stay or within 8 days of a hospital transfer from nursing home. Data were obtained from Minimum Data Set 3.0 (MDS) which contains clinical data from all Medicaid or Medicare certified nursing homes, and from the Medicare Beneficiary Summary File (MBSF) and Medicare Provider and Analysis and Review (MedPAR) which contains hospitalization events for all Medicare Beneficiaries. These files were linked to identify nursing home residents with ADRD who were hospitalized at the end of life. ADRD diagnosis was identified from the chronic condition list from the MBSF and from MDS diagnosis list.

Setting and participants. The study included 665 033 residents from 14 595 nursing homes who died during the study period. Resident race was categorized as White or Black based on the MBSF. Severe cognitive impairment was identified using the MDS that categorized residents as severe or not using the Brief Interview for Mental Status and the Cognitive Performance Scale. The mean (SD) age of the study population was 86.7 (9.2) years for White residents and 82.6 (11.1) years for Black residents. Of the participants, 68.8% and 61.2% were female for Black and White residents, respectively. Approximately 23.4% of White and 32.5% of Black residents had severe cognitive impairment. For nursing home characteristics, 71.5% of the 14 595 nursing homes represented were for profit; average bedside was 109.5 (57.0) and occupancy rate was on average 81.2% (14.3%).

Main outcome measures. The study outcome measure was any hospitalization within 30 days prior to death. The outcome was selected as an indicator of quality of care because as older adults living with ADRD experience progressive worsening of cognitive symptoms, at the end of life when dementia is severe, advance care planning and communication with health care proxies and surrogates often result in coordinated care that avoids acute hospitalizations, which are often burdensome to both patient and family and may yield poorer quality of life.

Main results. The study found that approximately 29.5% of White decedents and 40.7% of Black decedents were hospitalized towards the end of life. Nursing homes with a higher proportion of Black residents were more likely to have residents hospitalized towards the end of life with 35% of residents hospitalized in the highest quartile (27% Black) compared with 17% hospitalized for nursing homes in the lowest quartile (0% Black).After adjusting for covariates, Black residents were 7.9% more likely to be hospitalized in the last 30 days of life compared with White residents. Blacks with severe cognitive impairment has elevated risk of hospitalization by 4.9% when compared with White residents. After accounting for nursing home facility–level characteristics, nursing homes with a low proportion of Black residents had a 5.2% higher risk of hospitalizations compared with nursing homes with no Black residents, and nursing homes with a higher percentage of Black residents had a 13.3% higher risk of hospitalization compared with nursing homes with no Black residents.

Conclusion. Race is associated with care disparities in older nursing home residents with dementia. This study suggests that hospitalization towards the end of life as a quality of care marker differs across nursing homes, and nursing homes with a higher proportion of Black residents were more likely to be hospitalized. This suggests that these nursing homes may have fewer resources and delivered poorer quality of care, and that disparities in health systems or institutions contribute to differences in quality of care for this vulnerable group.

Commentary

Disparities of health status, health care, and affordability across race and ethnicity have persisted throughout the past 20 years.¹ There is further evidence to support systemic differences that can contribute to differences in health outcomes.² Although changes in health care policy such as the Affordable Care Act have expanded health care coverage, and instituted changes that aims to improve health care quality and reduce disparities, it is clear that factors contributing to disparities in care are structural and perhaps systemic. The latest evidence comes in this study that examines racial disparities in health care quality in one of the most vulnerable populations—older adults with Alzheimer’s disease and dementia. The finding that Black nursing home residents, when compared with White residents, often has higher risk of hospitalization at the end of life, even among those with severe dementia where better coordinated care, clear goals of care and perhaps instituting palliative care would result in lower rate of hospitalization. The disparities were observed across nursing homes as well, where nursing homes with higher proportion of Black residents appear to have lower quality of care.

These findings are consistent with prior work that has examined differences in Black and White population on uptake of palliative care, discussion, and the documentation of advance care planning.³ Factors that may contribute to these differences include mistrust of the health care system among minorities, and not being connected to adequate health care resources. Family members and surrogate health care decision makers may consider receiving more aggressive care as advocating for better health care for their family members.⁴ These differences may contribute to the differences in hospitalization rates among residents within the same nursing home; however, the differences between nursing homes even after accounting for individual differences may indicate more widespread systemic differences that is associated with race. Policy changes that will address these differences are needed to level these differences so that quality care can be delivered regardless of race.⁵ For this vulnerable population with a terminal illness, approaches to enhance uptake of palliative approaches and care delivery for dementia patients at terminal stage are needed and understanding and targeting factors that contribute to low uptake of these approaches will enhance end of life care. Understanding the differences in resources and systems of care in nursing homes and perhaps how palliative care is integrated in these settings will be important to address care disparities that occurs across nursing homes.

Applications for Clinical Practice

Clinicians who take care of this population of older adults with advanced dementia should be aware of the potential for racial disparities that may lead to differences in the quality of care. The underlying reasons for these differences could be targeted so that older adults in all racial groups may have equal access to quality care including palliative approaches that avoid aggressive care for terminal illnesses across settings that may yield better care and quality of life. Policy makers and health systems leaders need to consider the current realities with racial disparities that policies need to address these differences so that they may not continue to persist in our systems of care.

Financial disclosures: None.

Study Overview

Objective. To examine differences in care, specifically hospitalization towards the end of life, among nursing home residents with dementia who were Black compared with those who were White.

Design. Population based cohort study in the US. The study included all decedents with Alzheimer’s disease or related dementia (ADRD) who resided in a nursing home from 2014 to 2017. Decedents from nursing homes were identified by death within 1 day of an identified nursing home stay or within 8 days of a hospital transfer from nursing home. Data were obtained from Minimum Data Set 3.0 (MDS) which contains clinical data from all Medicaid or Medicare certified nursing homes, and from the Medicare Beneficiary Summary File (MBSF) and Medicare Provider and Analysis and Review (MedPAR) which contains hospitalization events for all Medicare Beneficiaries. These files were linked to identify nursing home residents with ADRD who were hospitalized at the end of life. ADRD diagnosis was identified from the chronic condition list from the MBSF and from MDS diagnosis list.

Setting and participants. The study included 665 033 residents from 14 595 nursing homes who died during the study period. Resident race was categorized as White or Black based on the MBSF. Severe cognitive impairment was identified using the MDS that categorized residents as severe or not using the Brief Interview for Mental Status and the Cognitive Performance Scale. The mean (SD) age of the study population was 86.7 (9.2) years for White residents and 82.6 (11.1) years for Black residents. Of the participants, 68.8% and 61.2% were female for Black and White residents, respectively. Approximately 23.4% of White and 32.5% of Black residents had severe cognitive impairment. For nursing home characteristics, 71.5% of the 14 595 nursing homes represented were for profit; average bedside was 109.5 (57.0) and occupancy rate was on average 81.2% (14.3%).

Main outcome measures. The study outcome measure was any hospitalization within 30 days prior to death. The outcome was selected as an indicator of quality of care because as older adults living with ADRD experience progressive worsening of cognitive symptoms, at the end of life when dementia is severe, advance care planning and communication with health care proxies and surrogates often result in coordinated care that avoids acute hospitalizations, which are often burdensome to both patient and family and may yield poorer quality of life.

Main results. The study found that approximately 29.5% of White decedents and 40.7% of Black decedents were hospitalized towards the end of life. Nursing homes with a higher proportion of Black residents were more likely to have residents hospitalized towards the end of life with 35% of residents hospitalized in the highest quartile (27% Black) compared with 17% hospitalized for nursing homes in the lowest quartile (0% Black).After adjusting for covariates, Black residents were 7.9% more likely to be hospitalized in the last 30 days of life compared with White residents. Blacks with severe cognitive impairment has elevated risk of hospitalization by 4.9% when compared with White residents. After accounting for nursing home facility–level characteristics, nursing homes with a low proportion of Black residents had a 5.2% higher risk of hospitalizations compared with nursing homes with no Black residents, and nursing homes with a higher percentage of Black residents had a 13.3% higher risk of hospitalization compared with nursing homes with no Black residents.

Conclusion. Race is associated with care disparities in older nursing home residents with dementia. This study suggests that hospitalization towards the end of life as a quality of care marker differs across nursing homes, and nursing homes with a higher proportion of Black residents were more likely to be hospitalized. This suggests that these nursing homes may have fewer resources and delivered poorer quality of care, and that disparities in health systems or institutions contribute to differences in quality of care for this vulnerable group.

Commentary

Disparities of health status, health care, and affordability across race and ethnicity have persisted throughout the past 20 years.¹ There is further evidence to support systemic differences that can contribute to differences in health outcomes.² Although changes in health care policy such as the Affordable Care Act have expanded health care coverage, and instituted changes that aims to improve health care quality and reduce disparities, it is clear that factors contributing to disparities in care are structural and perhaps systemic. The latest evidence comes in this study that examines racial disparities in health care quality in one of the most vulnerable populations—older adults with Alzheimer’s disease and dementia. The finding that Black nursing home residents, when compared with White residents, often has higher risk of hospitalization at the end of life, even among those with severe dementia where better coordinated care, clear goals of care and perhaps instituting palliative care would result in lower rate of hospitalization. The disparities were observed across nursing homes as well, where nursing homes with higher proportion of Black residents appear to have lower quality of care.

These findings are consistent with prior work that has examined differences in Black and White population on uptake of palliative care, discussion, and the documentation of advance care planning.³ Factors that may contribute to these differences include mistrust of the health care system among minorities, and not being connected to adequate health care resources. Family members and surrogate health care decision makers may consider receiving more aggressive care as advocating for better health care for their family members.⁴ These differences may contribute to the differences in hospitalization rates among residents within the same nursing home; however, the differences between nursing homes even after accounting for individual differences may indicate more widespread systemic differences that is associated with race. Policy changes that will address these differences are needed to level these differences so that quality care can be delivered regardless of race.⁵ For this vulnerable population with a terminal illness, approaches to enhance uptake of palliative approaches and care delivery for dementia patients at terminal stage are needed and understanding and targeting factors that contribute to low uptake of these approaches will enhance end of life care. Understanding the differences in resources and systems of care in nursing homes and perhaps how palliative care is integrated in these settings will be important to address care disparities that occurs across nursing homes.

Applications for Clinical Practice

Clinicians who take care of this population of older adults with advanced dementia should be aware of the potential for racial disparities that may lead to differences in the quality of care. The underlying reasons for these differences could be targeted so that older adults in all racial groups may have equal access to quality care including palliative approaches that avoid aggressive care for terminal illnesses across settings that may yield better care and quality of life. Policy makers and health systems leaders need to consider the current realities with racial disparities that policies need to address these differences so that they may not continue to persist in our systems of care.

Financial disclosures: None.

Study Overview

Objective. To examine the patterns in palliative care delivery in the last year of life among adults with cancer compared with adults with a noncancer terminal diagnosis.

Design. Population-based cohort study in Ontario, Canada, using linked administrative and clinical databases. The study included all adults ages 18 and over who died of cancer or noncancer terminal illnesses and received physician-delivered palliative care that was initiated in the last year of life between January 2010 and December 2017. These palliative care services are identified through the use of claims fee codes by physicians that account for delivery of palliative care, such as symptom management and counseling, that are intended to be palliative rather than curative. Exclusion criteria include patients who had 2 or more palliative care service claims the year prior to the last year of life, which may indicate existing palliative care services rather than initiation of new palliative care services in the last year of life. Other patients who were excluded from the study had palliative care services initiated within 7 days of death, as it is less likely that services and support would be arranged prior to death given the short time frame. The types of noncancer illnesses included heart failure, chronic obstructive pulmonary disease, end-stage renal disease, cirrhosis, stroke, and dementia. For the comparison of palliative care services, types of illnesses were divided into cancer, chronic organ failure (heart failure, chronic pulmonary disease, end-stage renal disease, cirrhosis, or stroke), and dementia, as they may represent different trajectories of illnesses and needs.

Setting and participants. The study included 145 709 adults who died during the study period, among 351 941 adults who died from illnesses described above. Another 105 587 were excluded because there were no palliative care services before death, 48 525 were excluded because of existing palliative care services prior to the last year of life, and 44 164 were excluded because palliative care was initiated within 7 days of death. Among the study population included, 21 054 died of chronic organ failure, 14 033 died of dementia, and 110 622 died of cancer. The median age of the study population was 78 years, with an interquartile range of 67 to 86 years, and 50.7% were female. Approximately 12.8% of the study population reside in rural areas; median frailty score (hospital frailty risk score) among those who died of chronic organ failure was 10, and the score among those who died of dementia was 13. The frailty score among those who died of cancer was 3, indicating less frailty. Those who died of organ failure and dementia also had a high mean number of prescription medications (18 and 16, respectively) compared with those with cancer (11).

Main outcome measures. Study outcome measures include the timing of palliative care initiation (primary outcome), categorized into time frames of ≤ 30 days, 31 to 90 days, and > 90 days before death; location of initiation of palliative care services, categorized into clinic, home, hospital, subacute care, and case management; models of care, categorized as generalist, consultative, or specialist palliative care; total number of palliative care visits before death; and location of death. The models of palliative care delivery were categorized based on the proportion of palliative care fee codes claimed by physicians. Physicians whose annual billing included more than 10% of palliative care service codes were considered palliative care specialists. Using this designation, models of palliative care were categorized into those delivered by palliative care specialists, generalists (nonpalliative care specialists), or both.

Main results. The study found that the timing of palliative care initiation was earlier among those who died of cancer compared with those with organ failure or dementia (28.9% vs 15.9% and 15.3%, respectively). After adjustment, those who died of organ failure and those who died of dementia were less likely to have palliative care services initiated > 90 days prior to death (odds ratio [OR] 0.48 and 0.42, respectively) and between 31 to 90 days prior to death (OR 0.77 and 0.60, respectively), when compared with those who died of cancer (who served as the reference group). Regarding location of palliative care initiation, adults who died of cancer were less likely to have palliative care services initiated at home (14.5%) compared with those who died of organ failure (32.8%) or dementia (27.9%). Overall, those who died of cancer received more palliative care visits from initiation to death (median of 11 visits) compared with those who died oforgan failure (median 4 visits) and dementia (median 4 visits). Regarding models of palliative care delivery, a higher proportion of palliative care was delivered by palliative care specialists rather than generalists among cancer patients (72.9%) compared with those with organ failure (43.3%) or dementia (40.1%). The proportion of patients with cancer who died at home was 62.6%, which was higher than those with organ failure (53.3%) but lower than those with dementia (75%).

Conclusion. There are differences in the delivery of palliative care among patients with cancer and other noncancer terminal illnesses, including timing of initiation of palliative care services, location of services, number of visits, and delivery by types of practitioners of palliative care. Understanding these disparities and targeting them are potentially important steps to ensuring appropriate access to palliative care across settings and disease types.

Commentary

Palliative care improves the quality of life of patients with serious illnesses and reduces symptom burden, and results in better satisfaction and less burdensome care.¹ Although palliative care approaches have been championed for cancer management, there is increasing evidence that palliative care also improves outcomes for patients with noncancer illnesses such as heart failure.² This study highlights the differences in palliative care delivery for patients who have cancer and noncancer diagnoses, demonstrating that timing, location, and care delivery models differ among patients with different diagnoses. The finding that noncancer terminal illness often has later palliative care initiation is a significant one, as early palliative care has been associated with improved patient outcomes³; thus, efforts to initiate palliative care earlier in the course of illness may benefit these patients.

A particular challenge in determining when to initiate palliative care lies in predicting outcomes,⁴ particularly for different types of illnesses, which may have different trajectories of advancing disease and functional change. Recent research has tested novel prognostic approaches, such as using machine learning to generate mortality estimates and integrating them into clinical decision support.⁵ These approaches may have the potential to enhance palliative care delivery and may be adapted to be used in managing patients with noncancer illnesses as well. The study also found that patients with cancer were more likely to receive palliative care from specialists rather than generalists, although this could be due to how palliative care is integrated in hospitals, clinics, and systems of care that serve patients with cancer. Identifying approaches that yield better palliative care models and delivery may help to further enhance care for patients with noncancer illnesses.

Applications for Clinical Practice

Identifying differences in patterns of palliative care delivery among those with cancer and other diagnoses may be an important step towards identifying gaps and avenues to improve palliative care delivery. The underlying reasons for these differences could be targeted so that patients across settings and diagnoses may have equal access to palliative care to improve their symptoms and quality of life. Policy makers and health system leaders may consider learning from how palliative care has been integrated into oncology care, to help transform care delivery for other noncancer terminal illnesses. It may also involve broadening education to providers in different specialties, so that the value and importance of palliative care may be recognized beyond oncological care.

Study Overview

Objective. To examine the patterns in palliative care delivery in the last year of life among adults with cancer compared with adults with a noncancer terminal diagnosis.

Design. Population-based cohort study in Ontario, Canada, using linked administrative and clinical databases. The study included all adults ages 18 and over who died of cancer or noncancer terminal illnesses and received physician-delivered palliative care that was initiated in the last year of life between January 2010 and December 2017. These palliative care services are identified through the use of claims fee codes by physicians that account for delivery of palliative care, such as symptom management and counseling, that are intended to be palliative rather than curative. Exclusion criteria include patients who had 2 or more palliative care service claims the year prior to the last year of life, which may indicate existing palliative care services rather than initiation of new palliative care services in the last year of life. Other patients who were excluded from the study had palliative care services initiated within 7 days of death, as it is less likely that services and support would be arranged prior to death given the short time frame. The types of noncancer illnesses included heart failure, chronic obstructive pulmonary disease, end-stage renal disease, cirrhosis, stroke, and dementia. For the comparison of palliative care services, types of illnesses were divided into cancer, chronic organ failure (heart failure, chronic pulmonary disease, end-stage renal disease, cirrhosis, or stroke), and dementia, as they may represent different trajectories of illnesses and needs.

Setting and participants. The study included 145 709 adults who died during the study period, among 351 941 adults who died from illnesses described above. Another 105 587 were excluded because there were no palliative care services before death, 48 525 were excluded because of existing palliative care services prior to the last year of life, and 44 164 were excluded because palliative care was initiated within 7 days of death. Among the study population included, 21 054 died of chronic organ failure, 14 033 died of dementia, and 110 622 died of cancer. The median age of the study population was 78 years, with an interquartile range of 67 to 86 years, and 50.7% were female. Approximately 12.8% of the study population reside in rural areas; median frailty score (hospital frailty risk score) among those who died of chronic organ failure was 10, and the score among those who died of dementia was 13. The frailty score among those who died of cancer was 3, indicating less frailty. Those who died of organ failure and dementia also had a high mean number of prescription medications (18 and 16, respectively) compared with those with cancer (11).

Main outcome measures. Study outcome measures include the timing of palliative care initiation (primary outcome), categorized into time frames of ≤ 30 days, 31 to 90 days, and > 90 days before death; location of initiation of palliative care services, categorized into clinic, home, hospital, subacute care, and case management; models of care, categorized as generalist, consultative, or specialist palliative care; total number of palliative care visits before death; and location of death. The models of palliative care delivery were categorized based on the proportion of palliative care fee codes claimed by physicians. Physicians whose annual billing included more than 10% of palliative care service codes were considered palliative care specialists. Using this designation, models of palliative care were categorized into those delivered by palliative care specialists, generalists (nonpalliative care specialists), or both.

Main results. The study found that the timing of palliative care initiation was earlier among those who died of cancer compared with those with organ failure or dementia (28.9% vs 15.9% and 15.3%, respectively). After adjustment, those who died of organ failure and those who died of dementia were less likely to have palliative care services initiated > 90 days prior to death (odds ratio [OR] 0.48 and 0.42, respectively) and between 31 to 90 days prior to death (OR 0.77 and 0.60, respectively), when compared with those who died of cancer (who served as the reference group). Regarding location of palliative care initiation, adults who died of cancer were less likely to have palliative care services initiated at home (14.5%) compared with those who died of organ failure (32.8%) or dementia (27.9%). Overall, those who died of cancer received more palliative care visits from initiation to death (median of 11 visits) compared with those who died oforgan failure (median 4 visits) and dementia (median 4 visits). Regarding models of palliative care delivery, a higher proportion of palliative care was delivered by palliative care specialists rather than generalists among cancer patients (72.9%) compared with those with organ failure (43.3%) or dementia (40.1%). The proportion of patients with cancer who died at home was 62.6%, which was higher than those with organ failure (53.3%) but lower than those with dementia (75%).

Conclusion. There are differences in the delivery of palliative care among patients with cancer and other noncancer terminal illnesses, including timing of initiation of palliative care services, location of services, number of visits, and delivery by types of practitioners of palliative care. Understanding these disparities and targeting them are potentially important steps to ensuring appropriate access to palliative care across settings and disease types.

Commentary

Palliative care improves the quality of life of patients with serious illnesses and reduces symptom burden, and results in better satisfaction and less burdensome care.¹ Although palliative care approaches have been championed for cancer management, there is increasing evidence that palliative care also improves outcomes for patients with noncancer illnesses such as heart failure.² This study highlights the differences in palliative care delivery for patients who have cancer and noncancer diagnoses, demonstrating that timing, location, and care delivery models differ among patients with different diagnoses. The finding that noncancer terminal illness often has later palliative care initiation is a significant one, as early palliative care has been associated with improved patient outcomes³; thus, efforts to initiate palliative care earlier in the course of illness may benefit these patients.

A particular challenge in determining when to initiate palliative care lies in predicting outcomes,⁴ particularly for different types of illnesses, which may have different trajectories of advancing disease and functional change. Recent research has tested novel prognostic approaches, such as using machine learning to generate mortality estimates and integrating them into clinical decision support.⁵ These approaches may have the potential to enhance palliative care delivery and may be adapted to be used in managing patients with noncancer illnesses as well. The study also found that patients with cancer were more likely to receive palliative care from specialists rather than generalists, although this could be due to how palliative care is integrated in hospitals, clinics, and systems of care that serve patients with cancer. Identifying approaches that yield better palliative care models and delivery may help to further enhance care for patients with noncancer illnesses.

Applications for Clinical Practice

Identifying differences in patterns of palliative care delivery among those with cancer and other diagnoses may be an important step towards identifying gaps and avenues to improve palliative care delivery. The underlying reasons for these differences could be targeted so that patients across settings and diagnoses may have equal access to palliative care to improve their symptoms and quality of life. Policy makers and health system leaders may consider learning from how palliative care has been integrated into oncology care, to help transform care delivery for other noncancer terminal illnesses. It may also involve broadening education to providers in different specialties, so that the value and importance of palliative care may be recognized beyond oncological care.

Study Overview

Objective. To examine the patterns in palliative care delivery in the last year of life among adults with cancer compared with adults with a noncancer terminal diagnosis.

Design. Population-based cohort study in Ontario, Canada, using linked administrative and clinical databases. The study included all adults ages 18 and over who died of cancer or noncancer terminal illnesses and received physician-delivered palliative care that was initiated in the last year of life between January 2010 and December 2017. These palliative care services are identified through the use of claims fee codes by physicians that account for delivery of palliative care, such as symptom management and counseling, that are intended to be palliative rather than curative. Exclusion criteria include patients who had 2 or more palliative care service claims the year prior to the last year of life, which may indicate existing palliative care services rather than initiation of new palliative care services in the last year of life. Other patients who were excluded from the study had palliative care services initiated within 7 days of death, as it is less likely that services and support would be arranged prior to death given the short time frame. The types of noncancer illnesses included heart failure, chronic obstructive pulmonary disease, end-stage renal disease, cirrhosis, stroke, and dementia. For the comparison of palliative care services, types of illnesses were divided into cancer, chronic organ failure (heart failure, chronic pulmonary disease, end-stage renal disease, cirrhosis, or stroke), and dementia, as they may represent different trajectories of illnesses and needs.

Setting and participants. The study included 145 709 adults who died during the study period, among 351 941 adults who died from illnesses described above. Another 105 587 were excluded because there were no palliative care services before death, 48 525 were excluded because of existing palliative care services prior to the last year of life, and 44 164 were excluded because palliative care was initiated within 7 days of death. Among the study population included, 21 054 died of chronic organ failure, 14 033 died of dementia, and 110 622 died of cancer. The median age of the study population was 78 years, with an interquartile range of 67 to 86 years, and 50.7% were female. Approximately 12.8% of the study population reside in rural areas; median frailty score (hospital frailty risk score) among those who died of chronic organ failure was 10, and the score among those who died of dementia was 13. The frailty score among those who died of cancer was 3, indicating less frailty. Those who died of organ failure and dementia also had a high mean number of prescription medications (18 and 16, respectively) compared with those with cancer (11).

Main outcome measures. Study outcome measures include the timing of palliative care initiation (primary outcome), categorized into time frames of ≤ 30 days, 31 to 90 days, and > 90 days before death; location of initiation of palliative care services, categorized into clinic, home, hospital, subacute care, and case management; models of care, categorized as generalist, consultative, or specialist palliative care; total number of palliative care visits before death; and location of death. The models of palliative care delivery were categorized based on the proportion of palliative care fee codes claimed by physicians. Physicians whose annual billing included more than 10% of palliative care service codes were considered palliative care specialists. Using this designation, models of palliative care were categorized into those delivered by palliative care specialists, generalists (nonpalliative care specialists), or both.

Main results. The study found that the timing of palliative care initiation was earlier among those who died of cancer compared with those with organ failure or dementia (28.9% vs 15.9% and 15.3%, respectively). After adjustment, those who died of organ failure and those who died of dementia were less likely to have palliative care services initiated > 90 days prior to death (odds ratio [OR] 0.48 and 0.42, respectively) and between 31 to 90 days prior to death (OR 0.77 and 0.60, respectively), when compared with those who died of cancer (who served as the reference group). Regarding location of palliative care initiation, adults who died of cancer were less likely to have palliative care services initiated at home (14.5%) compared with those who died of organ failure (32.8%) or dementia (27.9%). Overall, those who died of cancer received more palliative care visits from initiation to death (median of 11 visits) compared with those who died oforgan failure (median 4 visits) and dementia (median 4 visits). Regarding models of palliative care delivery, a higher proportion of palliative care was delivered by palliative care specialists rather than generalists among cancer patients (72.9%) compared with those with organ failure (43.3%) or dementia (40.1%). The proportion of patients with cancer who died at home was 62.6%, which was higher than those with organ failure (53.3%) but lower than those with dementia (75%).

Conclusion. There are differences in the delivery of palliative care among patients with cancer and other noncancer terminal illnesses, including timing of initiation of palliative care services, location of services, number of visits, and delivery by types of practitioners of palliative care. Understanding these disparities and targeting them are potentially important steps to ensuring appropriate access to palliative care across settings and disease types.

Commentary

Palliative care improves the quality of life of patients with serious illnesses and reduces symptom burden, and results in better satisfaction and less burdensome care.¹ Although palliative care approaches have been championed for cancer management, there is increasing evidence that palliative care also improves outcomes for patients with noncancer illnesses such as heart failure.² This study highlights the differences in palliative care delivery for patients who have cancer and noncancer diagnoses, demonstrating that timing, location, and care delivery models differ among patients with different diagnoses. The finding that noncancer terminal illness often has later palliative care initiation is a significant one, as early palliative care has been associated with improved patient outcomes³; thus, efforts to initiate palliative care earlier in the course of illness may benefit these patients.

A particular challenge in determining when to initiate palliative care lies in predicting outcomes,⁴ particularly for different types of illnesses, which may have different trajectories of advancing disease and functional change. Recent research has tested novel prognostic approaches, such as using machine learning to generate mortality estimates and integrating them into clinical decision support.⁵ These approaches may have the potential to enhance palliative care delivery and may be adapted to be used in managing patients with noncancer illnesses as well. The study also found that patients with cancer were more likely to receive palliative care from specialists rather than generalists, although this could be due to how palliative care is integrated in hospitals, clinics, and systems of care that serve patients with cancer. Identifying approaches that yield better palliative care models and delivery may help to further enhance care for patients with noncancer illnesses.

Applications for Clinical Practice

Identifying differences in patterns of palliative care delivery among those with cancer and other diagnoses may be an important step towards identifying gaps and avenues to improve palliative care delivery. The underlying reasons for these differences could be targeted so that patients across settings and diagnoses may have equal access to palliative care to improve their symptoms and quality of life. Policy makers and health system leaders may consider learning from how palliative care has been integrated into oncology care, to help transform care delivery for other noncancer terminal illnesses. It may also involve broadening education to providers in different specialties, so that the value and importance of palliative care may be recognized beyond oncological care.

Study Overview

Objective. To examine the trend of noninvasive and invasive mechanical ventilation at the end of life from 2000 to 2017.

Design. Observational population-based cohort study.

Setting and participants. The study was a population-based cohort study to examine the use of noninvasive and invasive mechanical ventilation among decedents. The study included a random 20% sample of Medicare beneficiaries older than 65 years who were hospitalized in the last 30 days of life and died between January 1, 2000, and December 31, 2017, except for the period October 1, 2015, to December 31, 2015, when the transition from International Classification of Diseases, Ninth Revision (ICD-9) to ICD-10 occurred. Beneficiaries with the primary admitting diagnosis of cardiac arrest or with preexisting tracheostomy were excluded because of expected requirements for ventilatory support. The sample included a total of 2,470,735 Medicare beneficiaries; mean age was 82.2 years, and 54.8% were female. Primary admitting diagnosis codes were used to identify 3 subcohorts: congestive heart failure, chronic obstructive pulmonary disease, and cancer; a fourth subcohort of dementia was identified using the primary admitting diagnosis code or the first 9 secondary diagnosis codes.

Main outcome measures. The study used procedure codes to identify the use of noninvasive ventilation, invasive mechanical ventilation, or none among decedents who were hospitalized in the last 30 days of life. Descriptive statistics to characterize variables by year of hospitalization and ventilatory support were calculated, and the rates of noninvasive and invasive mechanical ventilation use were tabulated. Other outcomes of interest include site of death (in-hospital death), hospice enrollment at death, and hospice enrollment in the last 3 days of life as measures of end-of- life care use. Multivariable logistic regressions were used to examine noninvasive and invasive mechanical ventilation use among decedents, and time trends were examined, with the pattern of use in year 2000 as reference. Subgroup analysis with the subcohort of patients with different diagnoses were conducted to examine trends.

Main results. From 2000 to 2017, 16.3% of decedents had invasive mechanical ventilation, 3.7% had noninvasive ventilation, and 1.0% had both noninvasive and invasive ventilation during their hospital stay. Compared to the reference year 2000, there was a 9-fold increase in noninvasive ventilation use, from 0.8% to 7.1% in 2017, and invasive mechanical ventilation use also increased slightly, from 15.0% to 18.5%. Compared to year 2000, decedents were 2.63 times and 1.04 times (adjusted odds ratio [OR]) more likely to receive noninvasive ventilation and invasive mechanical ventilation, respectively, in 2005, 7.87 times and 1.39 times more likely in 2011, and 11.84 times and 1.63 times more likely in 2017.

Subgroup analysis showed that for congestive heart failure and chronic obstructive pulmonary disease, the increase in noninvasive ventilation use mirrored the trend observed for the overall population, but the use of invasive mechanical ventilation did not increase from 2000 to 2017, with a rate of use of 11.1% versus 7.8% (adjusted OR, 1.07; 95% confidence interval [CI], 0.95-1.19) for congestive heart failure and 17.4% vs 13.2% (OR 1.03, 95% CI, 0.88-1.21) for chronic obstructive pulmonary disease. For the cancer and dementia subgroups, the increase in noninvasive ventilation use from 2000 to 2017 was accompanied by an increase in the use of invasive mechanical ventilation, with a rate of 6.2% versus 7.4% (OR, 1.40; 95% CI, 1.26-1.55) for decedents with cancer and a rate of 5.7% versus 6.2% (OR, 1.28; 95% CI, 1.17-1.41) for decedents with dementia. For other measures of end-of-life care, noninvasive ventilation use when compared to invasive mechanical ventilation use was associated with lower rates of in-hospital (acute care) deaths (50.3% vs 76.7%), hospice enrollment in the last 3 days of life (late hospice enrollment; 57.7% vs 63.0%), and higher rates of hospice enrollment at death (41.3% vs 20.0%).

Conclusion. There was an increase in the use of noninvasive ventilation from 2000 through 2017 among Medicare beneficiaries who died. The findings also suggest that the use of invasive mechanical ventilation did not increase among decedents with congestive heart failure and chronic obstructive pulmonary disease but increased among decedents with cancer and dementia.

Noninvasive ventilation offers an alternative to invasive mechanical ventilation for providing ventilatory support for respiratory failure, and may offer benefits as it could avert adverse effects associated with invasive mechanical ventilation, particularly in the management of respiratory failure due to congestive heart failure and chronic obstructive pulmonary disease.¹ There is evidence for potential benefits of use of noninvasive ventilation in other clinical scenarios, such as pneumonia in older adults with comorbidities, though its clinical utility is not as well established for other diseases.²

As noninvasive ventilation is introduced into clinical practice, it is not surprising that over the period of the study (2000 to 2017) that its use increased substantially. Advance directives that involve discussion of life-sustaining treatments, including in scenarios with respiratory failure, may also result in physician orders that specify whether an individual desires invasive mechanical ventilation versus other medical treatments, including noninvasive ventilation.^3,4 By examining the temporal trends of use of noninvasive and invasive ventilation, this study reveals that invasive mechanical ventilation use among decedents with dementia and cancer has increased, despite increases in the use of noninvasive ventilation. It is important to understand further what would explain these temporal trends and whether the use of noninvasive and also invasive mechanical ventilation at the end of life represents appropriate care with clear goals or whether it may represent overuse. It is also less clear in the end-of-life care scenario what the goals of treatment with noninvasive ventilation would be, especially if it does not avert the use of invasive mechanical ventilation.

The study includes decedents only, thus limiting the ability to draw conclusions about clinically appropriate care.⁵ Further studies should examine a cohort of patients who have serious and life-threatening illness to examine the trends and potential effects of noninvasive ventilation on outcomes and utilization, as individuals who have improved and survived would not be included in this present decedent cohort.

Applications for Clinical Practice

This study highlights changes in the use of noninvasive and invasive ventilation over time and the different trends seen among subgroups with different diagnoses. For older adults with serious comorbid illness such as dementia, it is especially important to have discussions on advance directives so that care at the end of life is concordant with the patient’s wishes and that unnecessary, burdensome care can be averted. Further studies to understand and define the appropriate use of noninvasive and invasive mechanical ventilation for older adults with significant comorbidities who have serious, life-threatening illness are needed to ensure appropriate clinical treatment at the end of life.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the trend of noninvasive and invasive mechanical ventilation at the end of life from 2000 to 2017.

Design. Observational population-based cohort study.

Setting and participants. The study was a population-based cohort study to examine the use of noninvasive and invasive mechanical ventilation among decedents. The study included a random 20% sample of Medicare beneficiaries older than 65 years who were hospitalized in the last 30 days of life and died between January 1, 2000, and December 31, 2017, except for the period October 1, 2015, to December 31, 2015, when the transition from International Classification of Diseases, Ninth Revision (ICD-9) to ICD-10 occurred. Beneficiaries with the primary admitting diagnosis of cardiac arrest or with preexisting tracheostomy were excluded because of expected requirements for ventilatory support. The sample included a total of 2,470,735 Medicare beneficiaries; mean age was 82.2 years, and 54.8% were female. Primary admitting diagnosis codes were used to identify 3 subcohorts: congestive heart failure, chronic obstructive pulmonary disease, and cancer; a fourth subcohort of dementia was identified using the primary admitting diagnosis code or the first 9 secondary diagnosis codes.

Main outcome measures. The study used procedure codes to identify the use of noninvasive ventilation, invasive mechanical ventilation, or none among decedents who were hospitalized in the last 30 days of life. Descriptive statistics to characterize variables by year of hospitalization and ventilatory support were calculated, and the rates of noninvasive and invasive mechanical ventilation use were tabulated. Other outcomes of interest include site of death (in-hospital death), hospice enrollment at death, and hospice enrollment in the last 3 days of life as measures of end-of- life care use. Multivariable logistic regressions were used to examine noninvasive and invasive mechanical ventilation use among decedents, and time trends were examined, with the pattern of use in year 2000 as reference. Subgroup analysis with the subcohort of patients with different diagnoses were conducted to examine trends.

Main results. From 2000 to 2017, 16.3% of decedents had invasive mechanical ventilation, 3.7% had noninvasive ventilation, and 1.0% had both noninvasive and invasive ventilation during their hospital stay. Compared to the reference year 2000, there was a 9-fold increase in noninvasive ventilation use, from 0.8% to 7.1% in 2017, and invasive mechanical ventilation use also increased slightly, from 15.0% to 18.5%. Compared to year 2000, decedents were 2.63 times and 1.04 times (adjusted odds ratio [OR]) more likely to receive noninvasive ventilation and invasive mechanical ventilation, respectively, in 2005, 7.87 times and 1.39 times more likely in 2011, and 11.84 times and 1.63 times more likely in 2017.

Subgroup analysis showed that for congestive heart failure and chronic obstructive pulmonary disease, the increase in noninvasive ventilation use mirrored the trend observed for the overall population, but the use of invasive mechanical ventilation did not increase from 2000 to 2017, with a rate of use of 11.1% versus 7.8% (adjusted OR, 1.07; 95% confidence interval [CI], 0.95-1.19) for congestive heart failure and 17.4% vs 13.2% (OR 1.03, 95% CI, 0.88-1.21) for chronic obstructive pulmonary disease. For the cancer and dementia subgroups, the increase in noninvasive ventilation use from 2000 to 2017 was accompanied by an increase in the use of invasive mechanical ventilation, with a rate of 6.2% versus 7.4% (OR, 1.40; 95% CI, 1.26-1.55) for decedents with cancer and a rate of 5.7% versus 6.2% (OR, 1.28; 95% CI, 1.17-1.41) for decedents with dementia. For other measures of end-of-life care, noninvasive ventilation use when compared to invasive mechanical ventilation use was associated with lower rates of in-hospital (acute care) deaths (50.3% vs 76.7%), hospice enrollment in the last 3 days of life (late hospice enrollment; 57.7% vs 63.0%), and higher rates of hospice enrollment at death (41.3% vs 20.0%).

Conclusion. There was an increase in the use of noninvasive ventilation from 2000 through 2017 among Medicare beneficiaries who died. The findings also suggest that the use of invasive mechanical ventilation did not increase among decedents with congestive heart failure and chronic obstructive pulmonary disease but increased among decedents with cancer and dementia.

Noninvasive ventilation offers an alternative to invasive mechanical ventilation for providing ventilatory support for respiratory failure, and may offer benefits as it could avert adverse effects associated with invasive mechanical ventilation, particularly in the management of respiratory failure due to congestive heart failure and chronic obstructive pulmonary disease.¹ There is evidence for potential benefits of use of noninvasive ventilation in other clinical scenarios, such as pneumonia in older adults with comorbidities, though its clinical utility is not as well established for other diseases.²

As noninvasive ventilation is introduced into clinical practice, it is not surprising that over the period of the study (2000 to 2017) that its use increased substantially. Advance directives that involve discussion of life-sustaining treatments, including in scenarios with respiratory failure, may also result in physician orders that specify whether an individual desires invasive mechanical ventilation versus other medical treatments, including noninvasive ventilation.^3,4 By examining the temporal trends of use of noninvasive and invasive ventilation, this study reveals that invasive mechanical ventilation use among decedents with dementia and cancer has increased, despite increases in the use of noninvasive ventilation. It is important to understand further what would explain these temporal trends and whether the use of noninvasive and also invasive mechanical ventilation at the end of life represents appropriate care with clear goals or whether it may represent overuse. It is also less clear in the end-of-life care scenario what the goals of treatment with noninvasive ventilation would be, especially if it does not avert the use of invasive mechanical ventilation.

The study includes decedents only, thus limiting the ability to draw conclusions about clinically appropriate care.⁵ Further studies should examine a cohort of patients who have serious and life-threatening illness to examine the trends and potential effects of noninvasive ventilation on outcomes and utilization, as individuals who have improved and survived would not be included in this present decedent cohort.

Applications for Clinical Practice

This study highlights changes in the use of noninvasive and invasive ventilation over time and the different trends seen among subgroups with different diagnoses. For older adults with serious comorbid illness such as dementia, it is especially important to have discussions on advance directives so that care at the end of life is concordant with the patient’s wishes and that unnecessary, burdensome care can be averted. Further studies to understand and define the appropriate use of noninvasive and invasive mechanical ventilation for older adults with significant comorbidities who have serious, life-threatening illness are needed to ensure appropriate clinical treatment at the end of life.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the trend of noninvasive and invasive mechanical ventilation at the end of life from 2000 to 2017.

Design. Observational population-based cohort study.

Setting and participants. The study was a population-based cohort study to examine the use of noninvasive and invasive mechanical ventilation among decedents. The study included a random 20% sample of Medicare beneficiaries older than 65 years who were hospitalized in the last 30 days of life and died between January 1, 2000, and December 31, 2017, except for the period October 1, 2015, to December 31, 2015, when the transition from International Classification of Diseases, Ninth Revision (ICD-9) to ICD-10 occurred. Beneficiaries with the primary admitting diagnosis of cardiac arrest or with preexisting tracheostomy were excluded because of expected requirements for ventilatory support. The sample included a total of 2,470,735 Medicare beneficiaries; mean age was 82.2 years, and 54.8% were female. Primary admitting diagnosis codes were used to identify 3 subcohorts: congestive heart failure, chronic obstructive pulmonary disease, and cancer; a fourth subcohort of dementia was identified using the primary admitting diagnosis code or the first 9 secondary diagnosis codes.

Main outcome measures. The study used procedure codes to identify the use of noninvasive ventilation, invasive mechanical ventilation, or none among decedents who were hospitalized in the last 30 days of life. Descriptive statistics to characterize variables by year of hospitalization and ventilatory support were calculated, and the rates of noninvasive and invasive mechanical ventilation use were tabulated. Other outcomes of interest include site of death (in-hospital death), hospice enrollment at death, and hospice enrollment in the last 3 days of life as measures of end-of- life care use. Multivariable logistic regressions were used to examine noninvasive and invasive mechanical ventilation use among decedents, and time trends were examined, with the pattern of use in year 2000 as reference. Subgroup analysis with the subcohort of patients with different diagnoses were conducted to examine trends.

Main results. From 2000 to 2017, 16.3% of decedents had invasive mechanical ventilation, 3.7% had noninvasive ventilation, and 1.0% had both noninvasive and invasive ventilation during their hospital stay. Compared to the reference year 2000, there was a 9-fold increase in noninvasive ventilation use, from 0.8% to 7.1% in 2017, and invasive mechanical ventilation use also increased slightly, from 15.0% to 18.5%. Compared to year 2000, decedents were 2.63 times and 1.04 times (adjusted odds ratio [OR]) more likely to receive noninvasive ventilation and invasive mechanical ventilation, respectively, in 2005, 7.87 times and 1.39 times more likely in 2011, and 11.84 times and 1.63 times more likely in 2017.

Subgroup analysis showed that for congestive heart failure and chronic obstructive pulmonary disease, the increase in noninvasive ventilation use mirrored the trend observed for the overall population, but the use of invasive mechanical ventilation did not increase from 2000 to 2017, with a rate of use of 11.1% versus 7.8% (adjusted OR, 1.07; 95% confidence interval [CI], 0.95-1.19) for congestive heart failure and 17.4% vs 13.2% (OR 1.03, 95% CI, 0.88-1.21) for chronic obstructive pulmonary disease. For the cancer and dementia subgroups, the increase in noninvasive ventilation use from 2000 to 2017 was accompanied by an increase in the use of invasive mechanical ventilation, with a rate of 6.2% versus 7.4% (OR, 1.40; 95% CI, 1.26-1.55) for decedents with cancer and a rate of 5.7% versus 6.2% (OR, 1.28; 95% CI, 1.17-1.41) for decedents with dementia. For other measures of end-of-life care, noninvasive ventilation use when compared to invasive mechanical ventilation use was associated with lower rates of in-hospital (acute care) deaths (50.3% vs 76.7%), hospice enrollment in the last 3 days of life (late hospice enrollment; 57.7% vs 63.0%), and higher rates of hospice enrollment at death (41.3% vs 20.0%).

Conclusion. There was an increase in the use of noninvasive ventilation from 2000 through 2017 among Medicare beneficiaries who died. The findings also suggest that the use of invasive mechanical ventilation did not increase among decedents with congestive heart failure and chronic obstructive pulmonary disease but increased among decedents with cancer and dementia.

Noninvasive ventilation offers an alternative to invasive mechanical ventilation for providing ventilatory support for respiratory failure, and may offer benefits as it could avert adverse effects associated with invasive mechanical ventilation, particularly in the management of respiratory failure due to congestive heart failure and chronic obstructive pulmonary disease.¹ There is evidence for potential benefits of use of noninvasive ventilation in other clinical scenarios, such as pneumonia in older adults with comorbidities, though its clinical utility is not as well established for other diseases.²

As noninvasive ventilation is introduced into clinical practice, it is not surprising that over the period of the study (2000 to 2017) that its use increased substantially. Advance directives that involve discussion of life-sustaining treatments, including in scenarios with respiratory failure, may also result in physician orders that specify whether an individual desires invasive mechanical ventilation versus other medical treatments, including noninvasive ventilation.^3,4 By examining the temporal trends of use of noninvasive and invasive ventilation, this study reveals that invasive mechanical ventilation use among decedents with dementia and cancer has increased, despite increases in the use of noninvasive ventilation. It is important to understand further what would explain these temporal trends and whether the use of noninvasive and also invasive mechanical ventilation at the end of life represents appropriate care with clear goals or whether it may represent overuse. It is also less clear in the end-of-life care scenario what the goals of treatment with noninvasive ventilation would be, especially if it does not avert the use of invasive mechanical ventilation.

The study includes decedents only, thus limiting the ability to draw conclusions about clinically appropriate care.⁵ Further studies should examine a cohort of patients who have serious and life-threatening illness to examine the trends and potential effects of noninvasive ventilation on outcomes and utilization, as individuals who have improved and survived would not be included in this present decedent cohort.

Applications for Clinical Practice

This study highlights changes in the use of noninvasive and invasive ventilation over time and the different trends seen among subgroups with different diagnoses. For older adults with serious comorbid illness such as dementia, it is especially important to have discussions on advance directives so that care at the end of life is concordant with the patient’s wishes and that unnecessary, burdensome care can be averted. Further studies to understand and define the appropriate use of noninvasive and invasive mechanical ventilation for older adults with significant comorbidities who have serious, life-threatening illness are needed to ensure appropriate clinical treatment at the end of life.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine whether discontinuation of nonsteroidal anti-inflammatory drug (NSAID) therapy and initiation of telephone-based cognitive behavioral therapy (CBT) is not worse than continuation of NSAIDs in the management of arthritis pain.

Design. Randomized controlled trial with noninferiority design.

Setting and participants. This study was a multicenter trial conducted across 4 Veterans Affairs health care systems in Boston, Providence, Connecticut, and North Florida/South Georgia that started September 2013 and ended September 2018. Eligibility criteria included being age 20 years or older, radiographic evidence of knee osteoarthritis, and use of an NSAID for knee pain on most days of the month for at least the past 3 months. Exclusion criteria included significant hearing impairments that may impede the conduct of the trial, current opioid prescriptions excluding tramadol, contraindications to NSAID use, recent or scheduled intra-articular injections or surgery, comorbid conditions other than knee pain that limited walking, and bilateral knee replacements or pain only in the replaced knee. Concurrent use of tramadol and other non-NSAID analgesics was permitted.

A total of 490 participants took part in the 2-week run-in period where their NSAID regimen was discontinued and they were started on a standardized dose of the NSAID meloxicam 15 mg daily. During the run-in period, 126 participants were excluded for several reasons, including worsening pain and patient withdrawal, yielding 364 participants who were randomized to continue meloxicam treatment or placebo for 4 weeks with blinding.

Intervention. Subsequent to the 4-week phase 1 placebo controlled trial, participants in the placebo group were given CBT via telephone (unblinded) for 10 weeks, and the meloxicam group continued treatment with meloxicam for phase 2. The CBT group received 10 modules over 10 weeks in 30- to 45-minute telephone contacts with a psychologist using a treatment manual modified for knee osteoarthritis. These modules consisted of 1 introductory module, 8 pain coping skills modules (eg, deep breathing and visual imagery, progressive muscle relaxation, physical activity and bodily mechanics, identifying unhealthy thoughts, balancing unhealthy thoughts, managing stress, time-based pacing, and sleep hygiene), and a final module emphasizing skill consolidation and relapse prevention. Outcomes were assessed at the end of the phase 1 and phase 2 periods.

Main outcome measures. Main study outcome measures included pain as measured with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at 4 weeks. Secondary outcomes included the WOMAC pain score, disability score, and global impression of change after treatment at 14 weeks. The WOMAC pain scale ranges from 0 to 20, and consists of 5 questions regarding severity of pain during walking, stair use, lying in bed at night, sitting, and standing, with 0 indicating no pain; 1, mild pain; 2, moderate pain; 3, severe pain; and 4, very severe pain for each item. The WOMAC disability scale measures self-reported difficulty in performing tasks that reflect lower-extremity physical function, including climbing stairs, rising from a chair, walking, and other activities of daily living. The global impression of change after treatment was measured on a 5-point scale (where 1 indicates much better and 5 indicates much worse). The minimum clinically important difference of the WOMAC pain scale is 2, based on prior literature. With the noninferiority design, the margin was set as a score of 1.

Main results. The placebo group consisted of 180 participants, with an average age of 58.2 years (SD, 11.8 years); 89% of them were male. The meloxicam group consisted of 184 participants, with an average age of 58.6 years (SD, 10 years); 84% of them were male. The average body mass index was 33.9 and 33.4 in each group, respectively. For the primary outcome, the placebo group had a worse pain score than the meloxicam group at 4 weeks (difference of 1.4; 95% confidence interval, 0.8- 2.0). At 14 weeks, the placebo group (with CBT) had a worse pain score than the meloxicam group (difference of 0.8; 95% CI, 0.2-1.4). There was no statistically significant difference in the disability score or global impression of change after treatment score between the 2 groups. The observed difference in pain score did not, however, exceed the minimum clinically important difference.

Conclusion. Placebo treatment and CBT are inferior to NSAIDs in managing pain for patients with knee osteoarthritis. The difference in pain may not be clinically important, and there were no differences in function at 14 weeks.

Commentary

Osteoarthritis is a common chronic condition that causes pain and disability and is often treated with oral analgesics. NSAIDs, despite few high-quality trials demonstrating their efficacy, are among the most commonly used treatment for osteoarthritis pain.¹ NSAID therapy, however, does have potential side effects, such as gastric reflux and renal dysfunction.² This withdrawal trial with placebo control contributes further evidence of the effectiveness of NSAIDs on knee osteoarthritis, demonstrating that indeed NSAIDs improve pain scores to a greater degree than placebo treatment. Augmenting placebo treatment with nonpharmacologic CBT was inferior to NSAIDs in pain management. The authors pointed out that the difference in pain score may not be clinically important, and that lower-extremity function was not different between the groups, concluding that, despite the higher pain score, CBT could be a treatment option, particularly for those who may have difficulty tolerating NSAID treatment.

The study population had a number of chronic conditions in addition to having knee arthritis, and thus likely were taking multiple medications for chronic disease management. Use of multiple medications is associated with an increased risk of rug interactions and adverse effects of medications.³ Thus, this attempt to assess whether a nonpharmacologic alternative treatment is noninferior to a drug treatment is a step toward building the evidence base for deprescribing and enhancing medication safety.⁴ Previous studies have examined other nonpharmacologic treatments for knee arthritis, such as acupuncture,⁵ and it is worthwhile to consider combining nonpharmacological approaches as an alternative to oral analgesic medication use.

Applications for Clinical Practice

This study advances our understanding of the effect of NSAID use on knee osteoarthritis when compared to placebo with CBT. Although this is a negative study that failed to show that placebo combined with CBT is noninferior to NSAIDs, it did quantify the expected treatment effect of NSAIDs and showed that this effect likely is not clinically important and/or does not alter lower-extremity function. Further studies are needed to identify other nonpharmacologic approaches and test whether combinations of approaches are effective in the management of chronic pain from osteoarthritis.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine whether discontinuation of nonsteroidal anti-inflammatory drug (NSAID) therapy and initiation of telephone-based cognitive behavioral therapy (CBT) is not worse than continuation of NSAIDs in the management of arthritis pain.

Design. Randomized controlled trial with noninferiority design.

Setting and participants. This study was a multicenter trial conducted across 4 Veterans Affairs health care systems in Boston, Providence, Connecticut, and North Florida/South Georgia that started September 2013 and ended September 2018. Eligibility criteria included being age 20 years or older, radiographic evidence of knee osteoarthritis, and use of an NSAID for knee pain on most days of the month for at least the past 3 months. Exclusion criteria included significant hearing impairments that may impede the conduct of the trial, current opioid prescriptions excluding tramadol, contraindications to NSAID use, recent or scheduled intra-articular injections or surgery, comorbid conditions other than knee pain that limited walking, and bilateral knee replacements or pain only in the replaced knee. Concurrent use of tramadol and other non-NSAID analgesics was permitted.

A total of 490 participants took part in the 2-week run-in period where their NSAID regimen was discontinued and they were started on a standardized dose of the NSAID meloxicam 15 mg daily. During the run-in period, 126 participants were excluded for several reasons, including worsening pain and patient withdrawal, yielding 364 participants who were randomized to continue meloxicam treatment or placebo for 4 weeks with blinding.

Intervention. Subsequent to the 4-week phase 1 placebo controlled trial, participants in the placebo group were given CBT via telephone (unblinded) for 10 weeks, and the meloxicam group continued treatment with meloxicam for phase 2. The CBT group received 10 modules over 10 weeks in 30- to 45-minute telephone contacts with a psychologist using a treatment manual modified for knee osteoarthritis. These modules consisted of 1 introductory module, 8 pain coping skills modules (eg, deep breathing and visual imagery, progressive muscle relaxation, physical activity and bodily mechanics, identifying unhealthy thoughts, balancing unhealthy thoughts, managing stress, time-based pacing, and sleep hygiene), and a final module emphasizing skill consolidation and relapse prevention. Outcomes were assessed at the end of the phase 1 and phase 2 periods.

Main outcome measures. Main study outcome measures included pain as measured with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at 4 weeks. Secondary outcomes included the WOMAC pain score, disability score, and global impression of change after treatment at 14 weeks. The WOMAC pain scale ranges from 0 to 20, and consists of 5 questions regarding severity of pain during walking, stair use, lying in bed at night, sitting, and standing, with 0 indicating no pain; 1, mild pain; 2, moderate pain; 3, severe pain; and 4, very severe pain for each item. The WOMAC disability scale measures self-reported difficulty in performing tasks that reflect lower-extremity physical function, including climbing stairs, rising from a chair, walking, and other activities of daily living. The global impression of change after treatment was measured on a 5-point scale (where 1 indicates much better and 5 indicates much worse). The minimum clinically important difference of the WOMAC pain scale is 2, based on prior literature. With the noninferiority design, the margin was set as a score of 1.

Main results. The placebo group consisted of 180 participants, with an average age of 58.2 years (SD, 11.8 years); 89% of them were male. The meloxicam group consisted of 184 participants, with an average age of 58.6 years (SD, 10 years); 84% of them were male. The average body mass index was 33.9 and 33.4 in each group, respectively. For the primary outcome, the placebo group had a worse pain score than the meloxicam group at 4 weeks (difference of 1.4; 95% confidence interval, 0.8- 2.0). At 14 weeks, the placebo group (with CBT) had a worse pain score than the meloxicam group (difference of 0.8; 95% CI, 0.2-1.4). There was no statistically significant difference in the disability score or global impression of change after treatment score between the 2 groups. The observed difference in pain score did not, however, exceed the minimum clinically important difference.

Conclusion. Placebo treatment and CBT are inferior to NSAIDs in managing pain for patients with knee osteoarthritis. The difference in pain may not be clinically important, and there were no differences in function at 14 weeks.

Commentary

Osteoarthritis is a common chronic condition that causes pain and disability and is often treated with oral analgesics. NSAIDs, despite few high-quality trials demonstrating their efficacy, are among the most commonly used treatment for osteoarthritis pain.¹ NSAID therapy, however, does have potential side effects, such as gastric reflux and renal dysfunction.² This withdrawal trial with placebo control contributes further evidence of the effectiveness of NSAIDs on knee osteoarthritis, demonstrating that indeed NSAIDs improve pain scores to a greater degree than placebo treatment. Augmenting placebo treatment with nonpharmacologic CBT was inferior to NSAIDs in pain management. The authors pointed out that the difference in pain score may not be clinically important, and that lower-extremity function was not different between the groups, concluding that, despite the higher pain score, CBT could be a treatment option, particularly for those who may have difficulty tolerating NSAID treatment.

The study population had a number of chronic conditions in addition to having knee arthritis, and thus likely were taking multiple medications for chronic disease management. Use of multiple medications is associated with an increased risk of rug interactions and adverse effects of medications.³ Thus, this attempt to assess whether a nonpharmacologic alternative treatment is noninferior to a drug treatment is a step toward building the evidence base for deprescribing and enhancing medication safety.⁴ Previous studies have examined other nonpharmacologic treatments for knee arthritis, such as acupuncture,⁵ and it is worthwhile to consider combining nonpharmacological approaches as an alternative to oral analgesic medication use.

Applications for Clinical Practice

This study advances our understanding of the effect of NSAID use on knee osteoarthritis when compared to placebo with CBT. Although this is a negative study that failed to show that placebo combined with CBT is noninferior to NSAIDs, it did quantify the expected treatment effect of NSAIDs and showed that this effect likely is not clinically important and/or does not alter lower-extremity function. Further studies are needed to identify other nonpharmacologic approaches and test whether combinations of approaches are effective in the management of chronic pain from osteoarthritis.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine whether discontinuation of nonsteroidal anti-inflammatory drug (NSAID) therapy and initiation of telephone-based cognitive behavioral therapy (CBT) is not worse than continuation of NSAIDs in the management of arthritis pain.

Design. Randomized controlled trial with noninferiority design.

Setting and participants. This study was a multicenter trial conducted across 4 Veterans Affairs health care systems in Boston, Providence, Connecticut, and North Florida/South Georgia that started September 2013 and ended September 2018. Eligibility criteria included being age 20 years or older, radiographic evidence of knee osteoarthritis, and use of an NSAID for knee pain on most days of the month for at least the past 3 months. Exclusion criteria included significant hearing impairments that may impede the conduct of the trial, current opioid prescriptions excluding tramadol, contraindications to NSAID use, recent or scheduled intra-articular injections or surgery, comorbid conditions other than knee pain that limited walking, and bilateral knee replacements or pain only in the replaced knee. Concurrent use of tramadol and other non-NSAID analgesics was permitted.

A total of 490 participants took part in the 2-week run-in period where their NSAID regimen was discontinued and they were started on a standardized dose of the NSAID meloxicam 15 mg daily. During the run-in period, 126 participants were excluded for several reasons, including worsening pain and patient withdrawal, yielding 364 participants who were randomized to continue meloxicam treatment or placebo for 4 weeks with blinding.

Intervention. Subsequent to the 4-week phase 1 placebo controlled trial, participants in the placebo group were given CBT via telephone (unblinded) for 10 weeks, and the meloxicam group continued treatment with meloxicam for phase 2. The CBT group received 10 modules over 10 weeks in 30- to 45-minute telephone contacts with a psychologist using a treatment manual modified for knee osteoarthritis. These modules consisted of 1 introductory module, 8 pain coping skills modules (eg, deep breathing and visual imagery, progressive muscle relaxation, physical activity and bodily mechanics, identifying unhealthy thoughts, balancing unhealthy thoughts, managing stress, time-based pacing, and sleep hygiene), and a final module emphasizing skill consolidation and relapse prevention. Outcomes were assessed at the end of the phase 1 and phase 2 periods.

Main outcome measures. Main study outcome measures included pain as measured with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at 4 weeks. Secondary outcomes included the WOMAC pain score, disability score, and global impression of change after treatment at 14 weeks. The WOMAC pain scale ranges from 0 to 20, and consists of 5 questions regarding severity of pain during walking, stair use, lying in bed at night, sitting, and standing, with 0 indicating no pain; 1, mild pain; 2, moderate pain; 3, severe pain; and 4, very severe pain for each item. The WOMAC disability scale measures self-reported difficulty in performing tasks that reflect lower-extremity physical function, including climbing stairs, rising from a chair, walking, and other activities of daily living. The global impression of change after treatment was measured on a 5-point scale (where 1 indicates much better and 5 indicates much worse). The minimum clinically important difference of the WOMAC pain scale is 2, based on prior literature. With the noninferiority design, the margin was set as a score of 1.

Main results. The placebo group consisted of 180 participants, with an average age of 58.2 years (SD, 11.8 years); 89% of them were male. The meloxicam group consisted of 184 participants, with an average age of 58.6 years (SD, 10 years); 84% of them were male. The average body mass index was 33.9 and 33.4 in each group, respectively. For the primary outcome, the placebo group had a worse pain score than the meloxicam group at 4 weeks (difference of 1.4; 95% confidence interval, 0.8- 2.0). At 14 weeks, the placebo group (with CBT) had a worse pain score than the meloxicam group (difference of 0.8; 95% CI, 0.2-1.4). There was no statistically significant difference in the disability score or global impression of change after treatment score between the 2 groups. The observed difference in pain score did not, however, exceed the minimum clinically important difference.

Conclusion. Placebo treatment and CBT are inferior to NSAIDs in managing pain for patients with knee osteoarthritis. The difference in pain may not be clinically important, and there were no differences in function at 14 weeks.

Commentary

Osteoarthritis is a common chronic condition that causes pain and disability and is often treated with oral analgesics. NSAIDs, despite few high-quality trials demonstrating their efficacy, are among the most commonly used treatment for osteoarthritis pain.¹ NSAID therapy, however, does have potential side effects, such as gastric reflux and renal dysfunction.² This withdrawal trial with placebo control contributes further evidence of the effectiveness of NSAIDs on knee osteoarthritis, demonstrating that indeed NSAIDs improve pain scores to a greater degree than placebo treatment. Augmenting placebo treatment with nonpharmacologic CBT was inferior to NSAIDs in pain management. The authors pointed out that the difference in pain score may not be clinically important, and that lower-extremity function was not different between the groups, concluding that, despite the higher pain score, CBT could be a treatment option, particularly for those who may have difficulty tolerating NSAID treatment.

The study population had a number of chronic conditions in addition to having knee arthritis, and thus likely were taking multiple medications for chronic disease management. Use of multiple medications is associated with an increased risk of rug interactions and adverse effects of medications.³ Thus, this attempt to assess whether a nonpharmacologic alternative treatment is noninferior to a drug treatment is a step toward building the evidence base for deprescribing and enhancing medication safety.⁴ Previous studies have examined other nonpharmacologic treatments for knee arthritis, such as acupuncture,⁵ and it is worthwhile to consider combining nonpharmacological approaches as an alternative to oral analgesic medication use.

Applications for Clinical Practice

This study advances our understanding of the effect of NSAID use on knee osteoarthritis when compared to placebo with CBT. Although this is a negative study that failed to show that placebo combined with CBT is noninferior to NSAIDs, it did quantify the expected treatment effect of NSAIDs and showed that this effect likely is not clinically important and/or does not alter lower-extremity function. Further studies are needed to identify other nonpharmacologic approaches and test whether combinations of approaches are effective in the management of chronic pain from osteoarthritis.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the effect of an advance care planning video intervention in nursing homes on resident outcomes of hospital transfer, burdensome treatment, and hospice enrollment.

Design. Pragmatic cluster randomized controlled trial.

Setting and participants. The study was conducted in 360 nursing homes located in 32 states across the United States. The facilities were owned by 2 for-profit nursing home chains; facilities with more than 50 beds were eligible to be included in the study. Facilities deemed by corporate leaders to have serious organizational problems or that lacked the ability to transfer electronic health records were excluded. The facilities, stratified by the primary outcome hospitalizations per 1000 person-days, were then randomized to intervention and control in a 1:2 ratio. Leaders from facilities in the intervention group received letters describing their selection to participate in the advance care planning video program, and all facilities invited agreed to participate. Participants (residents in nursing homes) were enrolled from February 1, 2016, to May 31, 2018. Each participant was followed for 12 months after enrollment. All residents living in intervention facilities were offered the opportunity to watch intervention videos. The target population of the study was residents with advanced illness, including advanced dementia or advanced cardiopulmonary disease, as defined by the Minimum Data Set (MDS) variables, who were aged 65 and older, were long-stay residents (100 days or more), and were enrolled as Medicare fee-for-service beneficiaries. Secondary analysis included residents without advanced illness meeting other criteria.

Intervention. The intervention consisted of a selection of 5 short videos (6 to 10 minutes each), which had been previously developed and tested in smaller randomized trials. These videos cover the topics of general goals of care, goals of care for advanced dementia, hospice, hospitalization, and advance care planning for healthy patients, and use narration and images of typical treatments representing intensive medical care, basic medical care, and comfort care. The video for goals of care for advanced dementia targeted proxies of residents rather than residents themselves.

The implementation strategy for the video program included using a program manager to oversee the organization of the program’s rollout (a manager for each for-profit nursing home chain) and 2 champions at each facility (typically social workers were tasked with showing videos to patients and families). Champions received training from the study investigators and the manager and were asked to choose and offer selected videos to residents or proxies within 7 days of admission or readmission, every 6 months during a resident’s stay, and when specific decisions occurred, such as transition to hospice care, and on special occasions, such as out-of-town family visits.

Video offering and use were captured through documentation by a facility champion using a report tool embedded in the facility’s electronic health record. Champions met with the facility’s program manager and study team to review reports of video use, identify residents who had not been shown a video, and problem-solve on how to reach these residents. Facilities in the control group used their usual procedures for advance care planning.

Main outcome measures. Study outcomes included hospitalization transfers per 1000 person-days alive among long-stay residents with advanced illness (primary outcome); proportion of residents with at least 1 hospital transfer; proportion of residents with at least 1 burdensome treatment; and hospice enrollment (secondary outcomes). Secondary outcomes also included hospitalization transfers for long-stay residents without advanced illness. Hospital transfers were identified using Medicare claims for admissions, emergency department visits, and observation stays. Burdensome treatments were identified from Medicare claims and MDS, including tube feeding, parenteral therapy, invasive mechanical intervention, and intensive care unit admission. Fidelity to video intervention was measured by the proportion of residents offered the videos and the proportion of residents shown the videos at least once during the study period.

Main results. A total of 360 facilities were included in the study, 119 intervention and 241 control facilities. For the primary outcome, 4171 residents with advanced illness were included in the intervention group and 8308 residents with advanced illness were included in the control group. The average age was 83.6 years in both groups. In the intervention and control groups, respectively, 71.2% and 70.5% were female, 78.4% and 81.5% were White, 68.6% and 70.1% had advanced dementia at baseline, and 35.4% and 33.4% had advanced congestive heart failure or chronic obstructive pulmonary disease at baseline. Approximately 34% of residents received hospice care at baseline. In the intervention and control groups, 43.9% and 45.3% of residents died during follow-up, and the average length of follow-up in each group was 253.1 days and 252.6 days, respectively.

For the primary outcome of hospital transfers per 1000 person-days alive, there were 3.7 episodes (standard error 0.2) in the intervention group and 3.9 episodes in the control group (standard error 0.3); the difference was not statistically significant. For residents without advanced illness, there also was no difference in the hospital transfer rate. For other secondary outcomes, the proportion of residents in the intervention and control groups with 1 or more hospital transfer was 40.9% and 41.6%, respectively; the proportion with 1 or more burdensome treatment was 9.6% and 10.7%; and hospice enrollment was 24.9% and 25.5%. None of these differences was statistically significant. In the intervention group, 55.6% of residents or proxies were offered the video intervention and 21.9% were shown the videos at least once. There was substantial variability in the proportion of residents in the intervention group who were shown videos.

Conclusion. The advance planning video program did not lead to a reduction in hospital transfer, burdensome treatment, or changes in hospice enrollment. Acceptance of the intervention by residents was variable, and this may have contributed to the null finding.

Commentary

Nursing home residents often have advanced illness and limited functional ability. Hospital transfers may be burdensome and of limited clinical benefit for these patients, particularly for those with advanced illness and limited life expectancy, and are associated with markers of poor quality of end-of-life care, such as increased rates of stage IV decubitus ulcer and feeding-tube use towards the end of life.¹ Advance care planning is associated with less aggressive care towards the end of life for persons with advanced illness,² which ultimately improves the quality of end-of-life care for these individuals. Prior interventions to improve advance care planning have had variable effects, while video-based interventions to improve advance care planning have shown promise.³

This pragmatic randomized trial assessed the effect of an advance care planning video program on important clinical outcomes for nursing home residents, particularly those with advanced illness. The results, however, are disappointing, as the video intervention failed to improve hospital transfer rate and burdensome treatment in this population. The negative results could be attributed to the limited adoption of the video intervention in the study, as only 21.9% of residents in the intervention group were actually exposed to the intervention. What is not reported, and is difficult to assess, is whether the video intervention led to advance care planning, as would be demonstrated by advance directive documentation and acceptance of goals of care of comfort. A per-protocol analysis may be considered to demonstrate if there is an effect on residents who were exposed to the intervention. Nonetheless, the low adoption rate of the intervention may prompt further investigation of factors limiting adoption and perhaps lead to a redesigned trial aimed at enhancing adoption, with consideration of use of implementation trial designs.

As pointed out by the study investigators, other changes to nursing home practices, specifically on hospital transfer, likely occurred during the study period. A number of national initiatives to reduce unnecessary hospital transfer from nursing homes have been introduced, and a reduction in hospital transfers occurred between 2011 and 2017⁴; these initiatives could have impacted staff priorities and adoption of the study intervention relative to other co-occurring initiatives.

Applications for Clinical Practice

The authors of this study reported negative trial results, but their findings highlight important issues in conducting trials in the nursing home setting. Additional demonstration of actual effect on advance care planning discussions and documentation will further enhance our understanding of whether the intervention, as tested, yields changes in practice on advance care planning in nursing homes. The pragmatic clinical trial design used in this study accounts for real-world settings, but may have limited the study’s ability to account for and adjust for differences in staff, settings, and other conditions and factors that may impact adoption of and fidelity to the intervention. Quality improvement approaches, such as INTERACT, have targeted unnecessary hospital transfers and may yield positive results.⁵ Quality improvement approaches like INTERACT allow for a high degree of adaptation to local procedures and settings, which in clinical trials is difficult to do. However, in a real-world setting, such approaches may be necessary to improve care.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the effect of an advance care planning video intervention in nursing homes on resident outcomes of hospital transfer, burdensome treatment, and hospice enrollment.

Design. Pragmatic cluster randomized controlled trial.

Setting and participants. The study was conducted in 360 nursing homes located in 32 states across the United States. The facilities were owned by 2 for-profit nursing home chains; facilities with more than 50 beds were eligible to be included in the study. Facilities deemed by corporate leaders to have serious organizational problems or that lacked the ability to transfer electronic health records were excluded. The facilities, stratified by the primary outcome hospitalizations per 1000 person-days, were then randomized to intervention and control in a 1:2 ratio. Leaders from facilities in the intervention group received letters describing their selection to participate in the advance care planning video program, and all facilities invited agreed to participate. Participants (residents in nursing homes) were enrolled from February 1, 2016, to May 31, 2018. Each participant was followed for 12 months after enrollment. All residents living in intervention facilities were offered the opportunity to watch intervention videos. The target population of the study was residents with advanced illness, including advanced dementia or advanced cardiopulmonary disease, as defined by the Minimum Data Set (MDS) variables, who were aged 65 and older, were long-stay residents (100 days or more), and were enrolled as Medicare fee-for-service beneficiaries. Secondary analysis included residents without advanced illness meeting other criteria.

Intervention. The intervention consisted of a selection of 5 short videos (6 to 10 minutes each), which had been previously developed and tested in smaller randomized trials. These videos cover the topics of general goals of care, goals of care for advanced dementia, hospice, hospitalization, and advance care planning for healthy patients, and use narration and images of typical treatments representing intensive medical care, basic medical care, and comfort care. The video for goals of care for advanced dementia targeted proxies of residents rather than residents themselves.

The implementation strategy for the video program included using a program manager to oversee the organization of the program’s rollout (a manager for each for-profit nursing home chain) and 2 champions at each facility (typically social workers were tasked with showing videos to patients and families). Champions received training from the study investigators and the manager and were asked to choose and offer selected videos to residents or proxies within 7 days of admission or readmission, every 6 months during a resident’s stay, and when specific decisions occurred, such as transition to hospice care, and on special occasions, such as out-of-town family visits.

Video offering and use were captured through documentation by a facility champion using a report tool embedded in the facility’s electronic health record. Champions met with the facility’s program manager and study team to review reports of video use, identify residents who had not been shown a video, and problem-solve on how to reach these residents. Facilities in the control group used their usual procedures for advance care planning.

Main outcome measures. Study outcomes included hospitalization transfers per 1000 person-days alive among long-stay residents with advanced illness (primary outcome); proportion of residents with at least 1 hospital transfer; proportion of residents with at least 1 burdensome treatment; and hospice enrollment (secondary outcomes). Secondary outcomes also included hospitalization transfers for long-stay residents without advanced illness. Hospital transfers were identified using Medicare claims for admissions, emergency department visits, and observation stays. Burdensome treatments were identified from Medicare claims and MDS, including tube feeding, parenteral therapy, invasive mechanical intervention, and intensive care unit admission. Fidelity to video intervention was measured by the proportion of residents offered the videos and the proportion of residents shown the videos at least once during the study period.

Main results. A total of 360 facilities were included in the study, 119 intervention and 241 control facilities. For the primary outcome, 4171 residents with advanced illness were included in the intervention group and 8308 residents with advanced illness were included in the control group. The average age was 83.6 years in both groups. In the intervention and control groups, respectively, 71.2% and 70.5% were female, 78.4% and 81.5% were White, 68.6% and 70.1% had advanced dementia at baseline, and 35.4% and 33.4% had advanced congestive heart failure or chronic obstructive pulmonary disease at baseline. Approximately 34% of residents received hospice care at baseline. In the intervention and control groups, 43.9% and 45.3% of residents died during follow-up, and the average length of follow-up in each group was 253.1 days and 252.6 days, respectively.

For the primary outcome of hospital transfers per 1000 person-days alive, there were 3.7 episodes (standard error 0.2) in the intervention group and 3.9 episodes in the control group (standard error 0.3); the difference was not statistically significant. For residents without advanced illness, there also was no difference in the hospital transfer rate. For other secondary outcomes, the proportion of residents in the intervention and control groups with 1 or more hospital transfer was 40.9% and 41.6%, respectively; the proportion with 1 or more burdensome treatment was 9.6% and 10.7%; and hospice enrollment was 24.9% and 25.5%. None of these differences was statistically significant. In the intervention group, 55.6% of residents or proxies were offered the video intervention and 21.9% were shown the videos at least once. There was substantial variability in the proportion of residents in the intervention group who were shown videos.

Conclusion. The advance planning video program did not lead to a reduction in hospital transfer, burdensome treatment, or changes in hospice enrollment. Acceptance of the intervention by residents was variable, and this may have contributed to the null finding.

Commentary

Nursing home residents often have advanced illness and limited functional ability. Hospital transfers may be burdensome and of limited clinical benefit for these patients, particularly for those with advanced illness and limited life expectancy, and are associated with markers of poor quality of end-of-life care, such as increased rates of stage IV decubitus ulcer and feeding-tube use towards the end of life.¹ Advance care planning is associated with less aggressive care towards the end of life for persons with advanced illness,² which ultimately improves the quality of end-of-life care for these individuals. Prior interventions to improve advance care planning have had variable effects, while video-based interventions to improve advance care planning have shown promise.³

This pragmatic randomized trial assessed the effect of an advance care planning video program on important clinical outcomes for nursing home residents, particularly those with advanced illness. The results, however, are disappointing, as the video intervention failed to improve hospital transfer rate and burdensome treatment in this population. The negative results could be attributed to the limited adoption of the video intervention in the study, as only 21.9% of residents in the intervention group were actually exposed to the intervention. What is not reported, and is difficult to assess, is whether the video intervention led to advance care planning, as would be demonstrated by advance directive documentation and acceptance of goals of care of comfort. A per-protocol analysis may be considered to demonstrate if there is an effect on residents who were exposed to the intervention. Nonetheless, the low adoption rate of the intervention may prompt further investigation of factors limiting adoption and perhaps lead to a redesigned trial aimed at enhancing adoption, with consideration of use of implementation trial designs.

As pointed out by the study investigators, other changes to nursing home practices, specifically on hospital transfer, likely occurred during the study period. A number of national initiatives to reduce unnecessary hospital transfer from nursing homes have been introduced, and a reduction in hospital transfers occurred between 2011 and 2017⁴; these initiatives could have impacted staff priorities and adoption of the study intervention relative to other co-occurring initiatives.

Applications for Clinical Practice

The authors of this study reported negative trial results, but their findings highlight important issues in conducting trials in the nursing home setting. Additional demonstration of actual effect on advance care planning discussions and documentation will further enhance our understanding of whether the intervention, as tested, yields changes in practice on advance care planning in nursing homes. The pragmatic clinical trial design used in this study accounts for real-world settings, but may have limited the study’s ability to account for and adjust for differences in staff, settings, and other conditions and factors that may impact adoption of and fidelity to the intervention. Quality improvement approaches, such as INTERACT, have targeted unnecessary hospital transfers and may yield positive results.⁵ Quality improvement approaches like INTERACT allow for a high degree of adaptation to local procedures and settings, which in clinical trials is difficult to do. However, in a real-world setting, such approaches may be necessary to improve care.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the effect of an advance care planning video intervention in nursing homes on resident outcomes of hospital transfer, burdensome treatment, and hospice enrollment.

Design. Pragmatic cluster randomized controlled trial.

Setting and participants. The study was conducted in 360 nursing homes located in 32 states across the United States. The facilities were owned by 2 for-profit nursing home chains; facilities with more than 50 beds were eligible to be included in the study. Facilities deemed by corporate leaders to have serious organizational problems or that lacked the ability to transfer electronic health records were excluded. The facilities, stratified by the primary outcome hospitalizations per 1000 person-days, were then randomized to intervention and control in a 1:2 ratio. Leaders from facilities in the intervention group received letters describing their selection to participate in the advance care planning video program, and all facilities invited agreed to participate. Participants (residents in nursing homes) were enrolled from February 1, 2016, to May 31, 2018. Each participant was followed for 12 months after enrollment. All residents living in intervention facilities were offered the opportunity to watch intervention videos. The target population of the study was residents with advanced illness, including advanced dementia or advanced cardiopulmonary disease, as defined by the Minimum Data Set (MDS) variables, who were aged 65 and older, were long-stay residents (100 days or more), and were enrolled as Medicare fee-for-service beneficiaries. Secondary analysis included residents without advanced illness meeting other criteria.

Intervention. The intervention consisted of a selection of 5 short videos (6 to 10 minutes each), which had been previously developed and tested in smaller randomized trials. These videos cover the topics of general goals of care, goals of care for advanced dementia, hospice, hospitalization, and advance care planning for healthy patients, and use narration and images of typical treatments representing intensive medical care, basic medical care, and comfort care. The video for goals of care for advanced dementia targeted proxies of residents rather than residents themselves.

The implementation strategy for the video program included using a program manager to oversee the organization of the program’s rollout (a manager for each for-profit nursing home chain) and 2 champions at each facility (typically social workers were tasked with showing videos to patients and families). Champions received training from the study investigators and the manager and were asked to choose and offer selected videos to residents or proxies within 7 days of admission or readmission, every 6 months during a resident’s stay, and when specific decisions occurred, such as transition to hospice care, and on special occasions, such as out-of-town family visits.

Video offering and use were captured through documentation by a facility champion using a report tool embedded in the facility’s electronic health record. Champions met with the facility’s program manager and study team to review reports of video use, identify residents who had not been shown a video, and problem-solve on how to reach these residents. Facilities in the control group used their usual procedures for advance care planning.

Main outcome measures. Study outcomes included hospitalization transfers per 1000 person-days alive among long-stay residents with advanced illness (primary outcome); proportion of residents with at least 1 hospital transfer; proportion of residents with at least 1 burdensome treatment; and hospice enrollment (secondary outcomes). Secondary outcomes also included hospitalization transfers for long-stay residents without advanced illness. Hospital transfers were identified using Medicare claims for admissions, emergency department visits, and observation stays. Burdensome treatments were identified from Medicare claims and MDS, including tube feeding, parenteral therapy, invasive mechanical intervention, and intensive care unit admission. Fidelity to video intervention was measured by the proportion of residents offered the videos and the proportion of residents shown the videos at least once during the study period.

Main results. A total of 360 facilities were included in the study, 119 intervention and 241 control facilities. For the primary outcome, 4171 residents with advanced illness were included in the intervention group and 8308 residents with advanced illness were included in the control group. The average age was 83.6 years in both groups. In the intervention and control groups, respectively, 71.2% and 70.5% were female, 78.4% and 81.5% were White, 68.6% and 70.1% had advanced dementia at baseline, and 35.4% and 33.4% had advanced congestive heart failure or chronic obstructive pulmonary disease at baseline. Approximately 34% of residents received hospice care at baseline. In the intervention and control groups, 43.9% and 45.3% of residents died during follow-up, and the average length of follow-up in each group was 253.1 days and 252.6 days, respectively.

For the primary outcome of hospital transfers per 1000 person-days alive, there were 3.7 episodes (standard error 0.2) in the intervention group and 3.9 episodes in the control group (standard error 0.3); the difference was not statistically significant. For residents without advanced illness, there also was no difference in the hospital transfer rate. For other secondary outcomes, the proportion of residents in the intervention and control groups with 1 or more hospital transfer was 40.9% and 41.6%, respectively; the proportion with 1 or more burdensome treatment was 9.6% and 10.7%; and hospice enrollment was 24.9% and 25.5%. None of these differences was statistically significant. In the intervention group, 55.6% of residents or proxies were offered the video intervention and 21.9% were shown the videos at least once. There was substantial variability in the proportion of residents in the intervention group who were shown videos.

Conclusion. The advance planning video program did not lead to a reduction in hospital transfer, burdensome treatment, or changes in hospice enrollment. Acceptance of the intervention by residents was variable, and this may have contributed to the null finding.

Commentary

Nursing home residents often have advanced illness and limited functional ability. Hospital transfers may be burdensome and of limited clinical benefit for these patients, particularly for those with advanced illness and limited life expectancy, and are associated with markers of poor quality of end-of-life care, such as increased rates of stage IV decubitus ulcer and feeding-tube use towards the end of life.¹ Advance care planning is associated with less aggressive care towards the end of life for persons with advanced illness,² which ultimately improves the quality of end-of-life care for these individuals. Prior interventions to improve advance care planning have had variable effects, while video-based interventions to improve advance care planning have shown promise.³

This pragmatic randomized trial assessed the effect of an advance care planning video program on important clinical outcomes for nursing home residents, particularly those with advanced illness. The results, however, are disappointing, as the video intervention failed to improve hospital transfer rate and burdensome treatment in this population. The negative results could be attributed to the limited adoption of the video intervention in the study, as only 21.9% of residents in the intervention group were actually exposed to the intervention. What is not reported, and is difficult to assess, is whether the video intervention led to advance care planning, as would be demonstrated by advance directive documentation and acceptance of goals of care of comfort. A per-protocol analysis may be considered to demonstrate if there is an effect on residents who were exposed to the intervention. Nonetheless, the low adoption rate of the intervention may prompt further investigation of factors limiting adoption and perhaps lead to a redesigned trial aimed at enhancing adoption, with consideration of use of implementation trial designs.

As pointed out by the study investigators, other changes to nursing home practices, specifically on hospital transfer, likely occurred during the study period. A number of national initiatives to reduce unnecessary hospital transfer from nursing homes have been introduced, and a reduction in hospital transfers occurred between 2011 and 2017⁴; these initiatives could have impacted staff priorities and adoption of the study intervention relative to other co-occurring initiatives.

Applications for Clinical Practice

The authors of this study reported negative trial results, but their findings highlight important issues in conducting trials in the nursing home setting. Additional demonstration of actual effect on advance care planning discussions and documentation will further enhance our understanding of whether the intervention, as tested, yields changes in practice on advance care planning in nursing homes. The pragmatic clinical trial design used in this study accounts for real-world settings, but may have limited the study’s ability to account for and adjust for differences in staff, settings, and other conditions and factors that may impact adoption of and fidelity to the intervention. Quality improvement approaches, such as INTERACT, have targeted unnecessary hospital transfers and may yield positive results.⁵ Quality improvement approaches like INTERACT allow for a high degree of adaptation to local procedures and settings, which in clinical trials is difficult to do. However, in a real-world setting, such approaches may be necessary to improve care.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the effect of clinical geriatric assessments and collaborative medication review by geriatricians and family physicians on quality of life and other patient outcomes in home-dwelling older adults with polypharmacy.

Design. The study was a single-blind, cluster randomized clinical trial enrolling home-dwelling adults aged 70 years and older who were taking 7 or more medications. Family physicians in Norway were recruited to participate in the trial with their patients. Randomization was at the family physician level to avoid contamination between intervention and control groups.

Setting and participants. The study was conducted in Akershus and Oslo, Norway. Family physicians were recruited to participate in the trial with their patients. A total of 84 family physicians were recruited, of which 70 were included in the trial and randomized to intervention versus control; 14 were excluded because they had no eligible patients. The cluster size of each family physician was limited to 5 patients per physician to avoid large variation in cluster sizes. Patients were eligible for enrollment if they were home-dwelling, aged 70 years or older, and were taking 7 or more systemic medications regularly and had medications administered by the home nursing service. Patients were excluded if they were expected to die or be institutionalized within 6 months, or if they were discouraged from participation by their family physician. A total of 174 patients were recruited, with 87 patients in each group (34 family physicians were in the control group and 36 in the intervention group).

Intervention. The intervention included a geriatric assessment performed by a physician trained in geriatric medicine and supervised by a senior consultant. The geriatric assessment consisted of review of medical history; systematic screening for current problems; clinical examination; supplementary tests, if indicated; and review of each medication being used. The review of medication included the indication for each medication, dosage, adverse effects, and interactions. The geriatric assessment consultation took 1 hour to complete, on average. After the geriatric assessment, the family physician and the geriatrician met to discuss each medication and to establish a collaborative plan for adjustments and follow-up; this meeting was approximately 15 minutes in duration. Lastly, clinical follow-up with the older adult was conducted by the geriatrician or the family physician, as agreed upon in the plan, with most follow-up conducted by the family physician. Participants randomized to the control group received usual care without any intervention.

Main outcome measures. Outcomes were assessed at 16-week and 24-week follow-up. The main study outcome measure was health-related quality of life (HRQoL), as measured by the 15D instrument, at 16 weeks. The quality-of-life measure included the following aspects, each rated on an ordinal scale of 5 levels: mobility, vision, hearing, breathing, sleeping, eating, speech, elimination, usual activities, mental function, discomfort or symptoms, depression, distress, vitality, and sexual activity. The index scale including all aspects is in the range of 0 to 1, with a higher score indicating better quality of life. A predetermined change of 0.015 or more is considered clinically important, and a positive change of 0.035 indicates much better HRQoL. Other outcomes included: appropriateness of medications measured by the Medication Appropriateness Index and the Assessment of Underutilization; physical function (short Physical Performance battery); gait speed; grip strength; cognitive functioning; physical and cognitive disability (Functional Independence Measure); caregiver burden (Relative Stress Scale); physical measures, including orthostatic blood pressure, falls, and weight; hospital admissions; use of home nursing service; incidence of institutionalization; and mortality.

Main results. The study included 174 patients with an average age of 83.3 years (SD, 7.3); 67.8% were women. Of those who were randomized to the intervention and control groups, 158 (90.8%) completed the trial. The average number of regularly used medications was 10.1 (SD, 2.7) in the intervention group and 9.5 (SD, 2.6) in the control group. At week 16 of follow-up, patients in the intervention group had an improved HRQoL score measured by the 15D instrument; the difference between the intervention group and control groups was 0.045 (95% confidence interval [CI], 0.004 -0.086; P = 0.03). Medication appropriateness was better in the intervention group, as compared with the control group at both 16 weeks and 24 weeks. Nearly all (99%) patients in the intervention group experienced medication changes, which included withdrawal of medications, dosage adjustment, or new drug regimens. There was a trend towards a higher rate of hospitalization during follow-up in the intervention group (adjusted risk ratio, 2.03; 95% CI, 0.98-4.24; P = 0.06). Other secondary outcomes were not substantially different between the intervention and control groups.

Conclusion. The study demonstrated that a clinical geriatric assessment and collaborative medication review by geriatrician and family physician led to improved HRQoL and improved medication use.

The use of multiple medications in older adults is common, with almost 20% of older adults over age 65 taking 10 or more medications.¹ Polypharmacy in older adults is associated with lower adherence rates and increases the potential for interactions between medications.² Age-related changes, such as changes in absorption, metabolism, and excretion, affect pharmacokinetics of medications and potentiate adverse drug reactions, requiring adjustments in use and dosing to optimize safety and outcomes. Recognizing the potential effects of medications in older adults, evidence-based guidelines, such as the Beers criteria³ and START/STOPP criteria,⁴ have been developed to identify potentially inappropriate medications in older adults and to improve prescribing. Randomized trials using the START/STOPP criteria have demonstrated improved medication appropriateness, reduced polypharmacy, and reduced adverse drug reactions.⁵ Although this study did not use a criteria-based approach for improving medication use, it demonstrated that in a population of older adults with polypharmacy, medication review with geriatricians can lead to improved HRQoL while improving medication appropriateness. The collaborative approach between the family physician and geriatrician, rather than a consultative approach with recommendations from a geriatrician, may have contributed to increased uptake of medication changes. Such an approach may be a reasonable strategy to improve medication use in older adults.

A limitation of the study is that the improvement in HRQoL could have been the result of medication changes, but could also have been due to other changes in the plan of care that resulted from the geriatric assessment. As noted by the authors, the increase in hospital admissions, though not statistically significant, could have resulted from the medication modifications; however, it was also noted that the geriatric assessments could have identified severe illnesses that required hospitalization, as the timeline from geriatric assessment to hospitalization suggested was the case. Thus, the increase in hospitalization resulting from timely identification of severe illness was more likely a benefit than an adverse effect; however, further studies should be done to elucidate this.

Applications for Clinical Practice

Older adults with multiple chronic conditions and complex medication regimens are at risk for poor health outcomes, and a purposeful medication review to improve medication use, leading to the removal of unnecessary and potentially harmful medications, adjustment of dosages, and initiation of appropriate medications, may yield health benefits, such as improved HRQoL. The present study utilized an approach that could be scalable, which is important given the limited number of clinicians with geriatrics expertise. For health systems with geriatrics clinical expertise, it may be reasonable to consider adopting a similar collaborative approach in order to improve care for older adults most at risk. Further reports on how patients and family physicians perceive this intervention will enhance our understanding of whether it could be implemented widely.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the effect of clinical geriatric assessments and collaborative medication review by geriatricians and family physicians on quality of life and other patient outcomes in home-dwelling older adults with polypharmacy.

Design. The study was a single-blind, cluster randomized clinical trial enrolling home-dwelling adults aged 70 years and older who were taking 7 or more medications. Family physicians in Norway were recruited to participate in the trial with their patients. Randomization was at the family physician level to avoid contamination between intervention and control groups.

Setting and participants. The study was conducted in Akershus and Oslo, Norway. Family physicians were recruited to participate in the trial with their patients. A total of 84 family physicians were recruited, of which 70 were included in the trial and randomized to intervention versus control; 14 were excluded because they had no eligible patients. The cluster size of each family physician was limited to 5 patients per physician to avoid large variation in cluster sizes. Patients were eligible for enrollment if they were home-dwelling, aged 70 years or older, and were taking 7 or more systemic medications regularly and had medications administered by the home nursing service. Patients were excluded if they were expected to die or be institutionalized within 6 months, or if they were discouraged from participation by their family physician. A total of 174 patients were recruited, with 87 patients in each group (34 family physicians were in the control group and 36 in the intervention group).

Intervention. The intervention included a geriatric assessment performed by a physician trained in geriatric medicine and supervised by a senior consultant. The geriatric assessment consisted of review of medical history; systematic screening for current problems; clinical examination; supplementary tests, if indicated; and review of each medication being used. The review of medication included the indication for each medication, dosage, adverse effects, and interactions. The geriatric assessment consultation took 1 hour to complete, on average. After the geriatric assessment, the family physician and the geriatrician met to discuss each medication and to establish a collaborative plan for adjustments and follow-up; this meeting was approximately 15 minutes in duration. Lastly, clinical follow-up with the older adult was conducted by the geriatrician or the family physician, as agreed upon in the plan, with most follow-up conducted by the family physician. Participants randomized to the control group received usual care without any intervention.

Main outcome measures. Outcomes were assessed at 16-week and 24-week follow-up. The main study outcome measure was health-related quality of life (HRQoL), as measured by the 15D instrument, at 16 weeks. The quality-of-life measure included the following aspects, each rated on an ordinal scale of 5 levels: mobility, vision, hearing, breathing, sleeping, eating, speech, elimination, usual activities, mental function, discomfort or symptoms, depression, distress, vitality, and sexual activity. The index scale including all aspects is in the range of 0 to 1, with a higher score indicating better quality of life. A predetermined change of 0.015 or more is considered clinically important, and a positive change of 0.035 indicates much better HRQoL. Other outcomes included: appropriateness of medications measured by the Medication Appropriateness Index and the Assessment of Underutilization; physical function (short Physical Performance battery); gait speed; grip strength; cognitive functioning; physical and cognitive disability (Functional Independence Measure); caregiver burden (Relative Stress Scale); physical measures, including orthostatic blood pressure, falls, and weight; hospital admissions; use of home nursing service; incidence of institutionalization; and mortality.

Main results. The study included 174 patients with an average age of 83.3 years (SD, 7.3); 67.8% were women. Of those who were randomized to the intervention and control groups, 158 (90.8%) completed the trial. The average number of regularly used medications was 10.1 (SD, 2.7) in the intervention group and 9.5 (SD, 2.6) in the control group. At week 16 of follow-up, patients in the intervention group had an improved HRQoL score measured by the 15D instrument; the difference between the intervention group and control groups was 0.045 (95% confidence interval [CI], 0.004 -0.086; P = 0.03). Medication appropriateness was better in the intervention group, as compared with the control group at both 16 weeks and 24 weeks. Nearly all (99%) patients in the intervention group experienced medication changes, which included withdrawal of medications, dosage adjustment, or new drug regimens. There was a trend towards a higher rate of hospitalization during follow-up in the intervention group (adjusted risk ratio, 2.03; 95% CI, 0.98-4.24; P = 0.06). Other secondary outcomes were not substantially different between the intervention and control groups.

Conclusion. The study demonstrated that a clinical geriatric assessment and collaborative medication review by geriatrician and family physician led to improved HRQoL and improved medication use.

The use of multiple medications in older adults is common, with almost 20% of older adults over age 65 taking 10 or more medications.¹ Polypharmacy in older adults is associated with lower adherence rates and increases the potential for interactions between medications.² Age-related changes, such as changes in absorption, metabolism, and excretion, affect pharmacokinetics of medications and potentiate adverse drug reactions, requiring adjustments in use and dosing to optimize safety and outcomes. Recognizing the potential effects of medications in older adults, evidence-based guidelines, such as the Beers criteria³ and START/STOPP criteria,⁴ have been developed to identify potentially inappropriate medications in older adults and to improve prescribing. Randomized trials using the START/STOPP criteria have demonstrated improved medication appropriateness, reduced polypharmacy, and reduced adverse drug reactions.⁵ Although this study did not use a criteria-based approach for improving medication use, it demonstrated that in a population of older adults with polypharmacy, medication review with geriatricians can lead to improved HRQoL while improving medication appropriateness. The collaborative approach between the family physician and geriatrician, rather than a consultative approach with recommendations from a geriatrician, may have contributed to increased uptake of medication changes. Such an approach may be a reasonable strategy to improve medication use in older adults.

A limitation of the study is that the improvement in HRQoL could have been the result of medication changes, but could also have been due to other changes in the plan of care that resulted from the geriatric assessment. As noted by the authors, the increase in hospital admissions, though not statistically significant, could have resulted from the medication modifications; however, it was also noted that the geriatric assessments could have identified severe illnesses that required hospitalization, as the timeline from geriatric assessment to hospitalization suggested was the case. Thus, the increase in hospitalization resulting from timely identification of severe illness was more likely a benefit than an adverse effect; however, further studies should be done to elucidate this.

Applications for Clinical Practice

Older adults with multiple chronic conditions and complex medication regimens are at risk for poor health outcomes, and a purposeful medication review to improve medication use, leading to the removal of unnecessary and potentially harmful medications, adjustment of dosages, and initiation of appropriate medications, may yield health benefits, such as improved HRQoL. The present study utilized an approach that could be scalable, which is important given the limited number of clinicians with geriatrics expertise. For health systems with geriatrics clinical expertise, it may be reasonable to consider adopting a similar collaborative approach in order to improve care for older adults most at risk. Further reports on how patients and family physicians perceive this intervention will enhance our understanding of whether it could be implemented widely.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the effect of clinical geriatric assessments and collaborative medication review by geriatricians and family physicians on quality of life and other patient outcomes in home-dwelling older adults with polypharmacy.

Design. The study was a single-blind, cluster randomized clinical trial enrolling home-dwelling adults aged 70 years and older who were taking 7 or more medications. Family physicians in Norway were recruited to participate in the trial with their patients. Randomization was at the family physician level to avoid contamination between intervention and control groups.

Setting and participants. The study was conducted in Akershus and Oslo, Norway. Family physicians were recruited to participate in the trial with their patients. A total of 84 family physicians were recruited, of which 70 were included in the trial and randomized to intervention versus control; 14 were excluded because they had no eligible patients. The cluster size of each family physician was limited to 5 patients per physician to avoid large variation in cluster sizes. Patients were eligible for enrollment if they were home-dwelling, aged 70 years or older, and were taking 7 or more systemic medications regularly and had medications administered by the home nursing service. Patients were excluded if they were expected to die or be institutionalized within 6 months, or if they were discouraged from participation by their family physician. A total of 174 patients were recruited, with 87 patients in each group (34 family physicians were in the control group and 36 in the intervention group).

Intervention. The intervention included a geriatric assessment performed by a physician trained in geriatric medicine and supervised by a senior consultant. The geriatric assessment consisted of review of medical history; systematic screening for current problems; clinical examination; supplementary tests, if indicated; and review of each medication being used. The review of medication included the indication for each medication, dosage, adverse effects, and interactions. The geriatric assessment consultation took 1 hour to complete, on average. After the geriatric assessment, the family physician and the geriatrician met to discuss each medication and to establish a collaborative plan for adjustments and follow-up; this meeting was approximately 15 minutes in duration. Lastly, clinical follow-up with the older adult was conducted by the geriatrician or the family physician, as agreed upon in the plan, with most follow-up conducted by the family physician. Participants randomized to the control group received usual care without any intervention.

Main outcome measures. Outcomes were assessed at 16-week and 24-week follow-up. The main study outcome measure was health-related quality of life (HRQoL), as measured by the 15D instrument, at 16 weeks. The quality-of-life measure included the following aspects, each rated on an ordinal scale of 5 levels: mobility, vision, hearing, breathing, sleeping, eating, speech, elimination, usual activities, mental function, discomfort or symptoms, depression, distress, vitality, and sexual activity. The index scale including all aspects is in the range of 0 to 1, with a higher score indicating better quality of life. A predetermined change of 0.015 or more is considered clinically important, and a positive change of 0.035 indicates much better HRQoL. Other outcomes included: appropriateness of medications measured by the Medication Appropriateness Index and the Assessment of Underutilization; physical function (short Physical Performance battery); gait speed; grip strength; cognitive functioning; physical and cognitive disability (Functional Independence Measure); caregiver burden (Relative Stress Scale); physical measures, including orthostatic blood pressure, falls, and weight; hospital admissions; use of home nursing service; incidence of institutionalization; and mortality.

Main results. The study included 174 patients with an average age of 83.3 years (SD, 7.3); 67.8% were women. Of those who were randomized to the intervention and control groups, 158 (90.8%) completed the trial. The average number of regularly used medications was 10.1 (SD, 2.7) in the intervention group and 9.5 (SD, 2.6) in the control group. At week 16 of follow-up, patients in the intervention group had an improved HRQoL score measured by the 15D instrument; the difference between the intervention group and control groups was 0.045 (95% confidence interval [CI], 0.004 -0.086; P = 0.03). Medication appropriateness was better in the intervention group, as compared with the control group at both 16 weeks and 24 weeks. Nearly all (99%) patients in the intervention group experienced medication changes, which included withdrawal of medications, dosage adjustment, or new drug regimens. There was a trend towards a higher rate of hospitalization during follow-up in the intervention group (adjusted risk ratio, 2.03; 95% CI, 0.98-4.24; P = 0.06). Other secondary outcomes were not substantially different between the intervention and control groups.

Conclusion. The study demonstrated that a clinical geriatric assessment and collaborative medication review by geriatrician and family physician led to improved HRQoL and improved medication use.

The use of multiple medications in older adults is common, with almost 20% of older adults over age 65 taking 10 or more medications.¹ Polypharmacy in older adults is associated with lower adherence rates and increases the potential for interactions between medications.² Age-related changes, such as changes in absorption, metabolism, and excretion, affect pharmacokinetics of medications and potentiate adverse drug reactions, requiring adjustments in use and dosing to optimize safety and outcomes. Recognizing the potential effects of medications in older adults, evidence-based guidelines, such as the Beers criteria³ and START/STOPP criteria,⁴ have been developed to identify potentially inappropriate medications in older adults and to improve prescribing. Randomized trials using the START/STOPP criteria have demonstrated improved medication appropriateness, reduced polypharmacy, and reduced adverse drug reactions.⁵ Although this study did not use a criteria-based approach for improving medication use, it demonstrated that in a population of older adults with polypharmacy, medication review with geriatricians can lead to improved HRQoL while improving medication appropriateness. The collaborative approach between the family physician and geriatrician, rather than a consultative approach with recommendations from a geriatrician, may have contributed to increased uptake of medication changes. Such an approach may be a reasonable strategy to improve medication use in older adults.

A limitation of the study is that the improvement in HRQoL could have been the result of medication changes, but could also have been due to other changes in the plan of care that resulted from the geriatric assessment. As noted by the authors, the increase in hospital admissions, though not statistically significant, could have resulted from the medication modifications; however, it was also noted that the geriatric assessments could have identified severe illnesses that required hospitalization, as the timeline from geriatric assessment to hospitalization suggested was the case. Thus, the increase in hospitalization resulting from timely identification of severe illness was more likely a benefit than an adverse effect; however, further studies should be done to elucidate this.

Applications for Clinical Practice

Older adults with multiple chronic conditions and complex medication regimens are at risk for poor health outcomes, and a purposeful medication review to improve medication use, leading to the removal of unnecessary and potentially harmful medications, adjustment of dosages, and initiation of appropriate medications, may yield health benefits, such as improved HRQoL. The present study utilized an approach that could be scalable, which is important given the limited number of clinicians with geriatrics expertise. For health systems with geriatrics clinical expertise, it may be reasonable to consider adopting a similar collaborative approach in order to improve care for older adults most at risk. Further reports on how patients and family physicians perceive this intervention will enhance our understanding of whether it could be implemented widely.

–William W. Hung, MD, MPH