Robert S. Dittus, MD, MPH

Delirium is an acute neurocognitive disorder¹ that affects up to 25% of older emergency department (ED) and hospitalized patients.^2-4 The relationship between delirium and adverse outcomes is well documented.^5-7 Delirium is a strong predictor of increased length of mechanical ventilation, longer intensive care unit and hospital stays, increased risk of falls, long-term cognitive impairment, and mortality.^8-13 Delirium is frequently missed by healthcare professionals^2,14-16 and goes undetected in up to 3 out of 4 patients by bedside nurses and medical practitioners in many hospital settings.^14,17-22 A significant barrier to recognizing delirium is the absence of brief delirium assessments.

In an effort to improve delirium recognition in the acute care setting, there has been a concerted effort to develop and validate brief delirium assessments. To address this unmet need, 4 ‘A’s Test (4AT), the Brief Confusion Assessment Method (bCAM), and the 3-minute diagnostic assessment for CAM-defined delirium (3D-CAM) are 1- to 3-minute delirium assessments that were validated in acutely ill older patients.²³ However, 1 to 3 minutes may still be too long in busy clinical environments, and briefer (<30 seconds) delirium assessments may be needed.

One potential more-rapid method to screen for delirium is to specifically test for the presence of inattention, which is a cardinal feature of delirium.^24,25 Inattention can be ascertained by having the patient recite the months backwards, recite the days of the week backwards, or spell a word backwards.²⁶ Recent studies have evaluated the diagnostic accuracy of reciting the months of the year backwards for delirium. O’Regan et al.²⁷ evaluated the diagnostic accuracy of the month of the year backwards from December to July (MOTYB-6) and observed that this task was 84% sensitive and 90% specific for delirium in older patients. However, they performed the reference standard delirium assessments in patients who had a positive MOTYB-6, which can overestimate sensitivity and underestimate specificity (verification bias).²⁸ Fick et al.²⁹ examined the diagnostic accuracy of 20 individual elements of the 3D-CAM and observed that reciting the months of the year backwards from December to January (MOTYB-12) was 83% sensitive and 69% specific for delirium. However, this was an exploratory study that was designed to identify an element of the 3D-CAM that had the best diagnostic accuracy.

To address these limitations, we sought to evaluate the diagnostic performance of the MOTYB-6 and MOTYB-12 for delirium as diagnosed by a reference standard. We also explored other brief tests of inattention such as spelling a word (“LUNCH”) backwards, reciting the days of the week backwards, 10-letter vigilance “A” task, and 5 picture recognition task.

METHODS

Study Design and Setting

This was a preplanned secondary analysis of a prospective observational study that validated 3 delirium assessments.^30,31 This study was conducted at a tertiary care, academic ED. The local institutional review board (IRB) reviewed and approved this study. Informed consent from the patient or an authorized surrogate was obtained whenever possible. Because this was an observational study and posed minimal risk to the patient, the IRB granted a waiver of consent for patients who were both unable to provide consent and were without an authorized surrogate available in the ED or by phone.

Selection of Participants

We enrolled a convenience sample of patients between June 2010 and February 2012 Monday through Friday from 8 am to 4 pm. This enrollment window was based upon the psychiatrist’s availability. Because of the extensiveness of the psychiatric evaluations, we limited enrollment to 1 patient per day. Patients who were 65 years or older, not in a hallway bed, and in the ED for less than 12 hours at the time of enrollment were included. We used a 12-hour cutoff so that patients who presented in the evening and early morning hours could be included. Patients were excluded if they were previously enrolled, non-English speaking, deaf or blind, comatose, suffered from end-stage dementia, or were unable to complete all the study assessments. The rationale for excluding patients with end-stage dementia was that diagnosing delirium in this patient population is challenging.

 

Research assistants approached patients who met inclusion criteria and determined if any exclusion criteria were present. If none of the exclusion criteria were present, then the research assistant reviewed the informed consent document with the patient or authorized surrogate if the patient was not capable of providing consent. If a patient was not capable of providing consent and no authorized surrogate was available, then the patient was enrolled (under the waiver of consent) as long as the patient assented to be a part of the study. Once the patient was enrolled, the research assistant contacted the physician rater and reference standard psychiatrists to approach the patient.

Measures of Inattention

An emergency physician (JHH) who had no formal training in the mental status assessment of elders administered a cognitive battery to the patient, including tests of inattention. The following inattention tasks were administered:

• Spell the word “LUNCH” backwards.³⁰ Patients were initially allowed to spell the word “LUNCH” forwards. Patients who were unable to perform the task were assigned 5 errors.
• Recite the months of the year backwards from December to July.^{23,26,27,30,32} Patients who were unable to perform the task were assigned 6 errors.
• Recite the days of the week backwards.^23,26,33 Patients who were unable to perform the task were assigned 7 errors.
• Ten-letter vigilance “A” task.³⁴ The patient was given a series of 10 letters (“S-A-V-E-A-H-A-A-R-T”) every 3 seconds and was asked to squeeze the rater’s hand every time the patient heard the letter “A.” Patients who were unable to perform the task were assigned 10 errors.
• Five picture recognition task.³⁴ Patients were shown 5 objects on picture cards. Afterwards, patients were shown 10 pictures with the previously shown objects intermingled. The patient had to identify which objects were seen previously in the first 5 pictures. Patients who were unable to perform the task were assigned 10 errors.
• Recite the months of the year backwards from December to January.²⁹ Patients who were unable to perform the task were assigned 12 errors.

Reference Standard for Delirium

A comprehensive consultation-liaison psychiatrist assessment was the reference standard for delirium; the diagnosis of delirium was based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria.³⁵ Three psychiatrists who each had an average of 11 years of clinical experience and regularly diagnosed delirium as part of their daily clinical practice were available to perform these assessments. To arrive at the diagnosis of delirium, they interviewed those who best understood the patient’s mental status (eg, the patient’s family members or caregivers, physician, and nurses). They also reviewed the patient’s medical record and radiology and laboratory test results. They performed bedside cognitive testing that included, but was not limited to, the Mini-Mental State Examination, Clock Drawing Test, Luria hand sequencing task, and tests for verbal fluency. A focused neurological examination was also performed (ie, screening for paraphasic errors, tremors, tone, asterixis, frontal release signs, etc.), and they also evaluated the patient for affective lability, hallucinations, and level of alertness. If the presence of delirium was still questionable, then confrontational naming, proverb interpretation or similarities, and assessments for apraxias were performed at the discretion of the psychiatrist. The psychiatrists were blinded to the physician’s assessments, and the assessments were conducted within 3 hours of each other.

Additional Variables Collected

Using medical record review, comorbidity burden, severity of illness, and premorbid cognition were ascertained. The Charlson Comorbidity Index, a weighted index that takes into account the number and seriousness of 19 preexisting comorbid conditions, was used to quantify comorbidity burden; higher scores indicate higher comorbid burden.^36,37 The Acute Physiology Score of the Acute Physiology and Chronic Health Evaluation II was used to quantify severity of illness.³⁸ This score is based upon the initial values of 12 routine physiologic measurements such as vital sign and laboratory abnormalities; higher scores represent higher severities of illness.³⁸ The medical record was reviewed to ascertain the presence of premorbid cognitive impairment; any documentation of dementia in the patient’s clinical problem list or physician history and physical examination from the outpatient or inpatient settings was considered positive. The medical record review was performed by a research assistant and was double-checked for accuracy by one of the investigators (JHH).

Data Analyses

Measures of central tendency and dispersion for continuous variables were reported as medians and interquartile ranges. Categorical variables were reported as proportions. Receiver operating characteristic curves were constructed for each inattention task. Area under the receiver operating characteristic curves (AUC) was reported to provide a global measure of diagnostic accuracy. Sensitivities, specificities, positive likelihood ratios (PLRs), and negative likelihood ratios (NLRs) with their 95% CIs were calculated using the psychiatrist’s assessment as the reference standard.³⁹ Cut-points with PLRs greater than 10 (strongly increased the likelihood of delirium) or NLRs less than 0.1 (strongly decreased the likelihood of delirium) were preferentially reported whenever possible.

 

All statistical analyses were performed with open source R statistical software version 3.0.1 (http://www.r-project.org/), SAS 9.4 (SAS Institute, Cary, NC), and Microsoft Excel 2010 (Microsoft Inc., Redmond, WA).

RESULTS

A total of 542 patients were screened; 214 patients refused to participate, and 93 were excluded, leaving 235 patients. The patient characteristics can be seen in Table 1. Compared with all patients (N = 15,359) who presented to the ED during the study period, enrolled patients were similar in age but more likely to be male, have cardiovascular chief complaints, and be admitted to the hospital. Of those enrolled, 25 (10.6%) were delirious. Delirious patients were older, more likely to be nonwhite, have a past history of dementia, have a graduate school degree, and have a chief complaint of altered mental status.

Making any error on the MOTYB-6 task had a sensitivity of 80.0% (95% CI, 60.9%-91.1%), specificity of 57.1% (95% CI, 50.4%-63.7%), PLR of 1.87 (95% CI, 1.45-2.40) and NLR of 0.35 (95% CI, 0.16-0.77) for delirium as diagnosed by a psychiatrist. Making any error on the MOTYB-12 task had a sensitivity of 84.0% (95% CI, 65.4%-93.6%), specificity of 51.9% (95% CI, 45.2%-58.5%), PLR of 1.75 (95% CI, 1.40-2.18), and NLR of 0.31 (95% CI, 0.12-0.76) for delirium. The AUCs for the MOTYB-6 and MOTYB-12 tasks were 0.77 and 0.78, respectively, indicating very good diagnostic performance.

The diagnostic performances of the other inattention tasks and additional cutoff values for the MOTYB-6 and MOTYB-12 tasks can be seen in Table 2. Increasing the MOTYB-6 cut-off to 2 or more errors and MOTYB-12 cut-off to 3 or more errors increased the specificity to 70.0% and 70.5%, respectively, without decreasing their sensitivity. The best combination of sensitivity and specificity was reciting the days of the week backwards task; if the patient made any error, this was 84.0% (95% CI, 65.4%-93.6%) sensitive and 81.9% (95% CI, 76.1%-86.5%) specific for delirium. The inattention tasks that strongly increased the likelihood of delirium (PLR > 10) were the vigilance “A” and picture recognition tasks. If the patient made 2 or more errors on the vigilance task or 3 or more errors on the picture recognition task, then the likelihood of delirium strongly increased, as evidenced by a PLR of 16.80 (95% CI, 8.01-35.23) and 23.10 (95% CI, 7.95-67.12), respectively. No other inattention tasks were able to achieve a PLR of greater than 10, regardless of what cutoff was used. No inattention task was able to achieve a NLR of less than 0.10, which would have strongly decreased the likelihood of delirium. The best NLRs were if the patient made no errors spelling the word “LUNCH” backwards (NLR, 0.16; 95% CI, 0.04-0.60), no errors on the vigilance “A” task (NLR, 0.18; 95% CI, 0.07-0.43), and no errors on the days of the week backwards task (NLR, 0.20; 95% CI, 0.08-0.48).

DISCUSSION

Delirium is frequently missed by healthcare providers because it is not routinely screened for in the acute care setting. To help address this deficiency of care, we evaluated several brief measures of inattention that take less than 30 seconds to complete. We observed that any errors made on the MOTYB-6 and MOTYB-12 tasks had very good sensitivities (80% and 84%) but were limited by their modest specificities (approximately 50%) for delirium. As a result, these assessments have limited clinical utility as standalone delirium screens. We also explored other commonly used brief measures of inattention and at a variety of error cutoffs. Reciting the days of the week backwards appeared to best balance sensitivity and specificity. None of the inattention measures could convincingly rule out delirium (NLR < 0.10), but the vigilance “A” and picture recognition tasks may have clinical utility in ruling in delirium (PLR > 10). Overall, all the inattention tasks, including MOTYB-6 and MOTYB-12, had very good diagnostic performances based upon their AUC. However, achieving a high sensitivity often had to be sacrificed for specificity or, alternatively, achieving a high specificity had to be sacrificed for sensitivity.

Inattention has been shown to be the cardinal feature for delirium,⁴⁰ and its assessment using cognitive testing has been recommended to help identify the presence of delirium according to an expert consensus panel.²⁶ The diagnostic performance of the MOTYB-12 observed in our study is similar to a study by Fick et al., who reported that MOTYB-12 had very good sensitivity (83%) but had modest specificity (69%) with a cutoff of 1 or more errors. Hendry et al. observed that the MOTYB-12 was 91% sensitive and 50% specific using a cutoff of 4 or more errors. With regard to the MOTYB-6, our reported specificity was different from what was observed by O’Regan et al.²⁷ Using 1 or more errors as a cutoff, they observed a much higher specificity for delirium than we did (90% vs 57%). Discordant observations regarding the diagnostic accuracy for other inattention tasks also exist. We observed that making any error on the days of the week backwards task was 84% sensitive and 82% specific for delirium, whereas Fick et al. observed a sensitivity and specificity of 50% and 94%, respectively. For the vigilance “A” task, we observed that making 2 or more errors over a series of 10 letters was 64.0% sensitive and 91.4% specific for delirium, whereas Pompei et al.⁴¹ observed that making 2 or more errors over a series of 60 letters was 51% sensitive and 77% specific for delirium.

The abovementioned discordant findings may be driven by spectrum bias, wherein the sensitivities and specificities for each inattention task may differ in different subgroups. As a result, differences in the age distribution, proportion of college graduates, history of dementia, and susceptibility to delirium can influence overall sensitivity and specificity. Objective measures of delirium, including the inattention screens studied, are particularly prone to spectrum bias.^31,34 However, the strength of this approach is that the assessment of inattention becomes less reliant upon clinical judgment and allows it to be used by raters from a wide range of clinical backgrounds. On the other hand, a subjective interpretation of these inattention tasks may allow the rater to capture the subtleties of inattention (ie, decreased speed of performance in a highly intelligent and well-educated patient without dementia). The disadvantage of this approach, however, is that it is more dependent on clinical judgment and may have decreased diagnostic accuracy in those with less clinical experience or with limited training.^14,42,43 These factors must be carefully considered when determining which delirium assessment to use.

Additional research is required to determine the clinical utility of these brief inattention assessments. These findings need to be further validated in larger studies, and the optimal cutoff of each task for different subgroup of patients (eg, demented vs nondemented) needs to be further clarified. It is not completely clear whether these inattention tests can serve as standalone assessments. Depending on the cutoff used, some of these assessments may have unacceptable false negative or false positive rates that may lead to increased adverse patient outcomes or increased resource utilization, respectively. Additional components or assessments may be needed to improve the diagnostic accuracy of these assessments. In addition to understanding these inattention assessments’ diagnostic accuracies, their ability to predict adverse outcomes also needs to be investigated. While a previous study observed that making any error on the MOTYB-12 task was associated with increased physical restraint use and prolonged hospital length of stay,⁴⁴ these assessments’ ability to prognosticate long-term outcomes such as mortality or long-term cognition or function need to be studied. Lastly, studies should also evaluate how easily implementable these assessments are and whether improved delirium recognition leads to improved patient outcomes.

This study has several notable limitations. Though planned a priori, this was a secondary analysis of a larger investigation designed to validate 3 delirium assessments. Our sample size was also relatively small, causing our 95% CIs to overlap in most cases and limiting the statistical power to truly determine whether one measure is better than the other. We also asked the patient to recite the months backwards from December to July as well as recite the months backwards from December to January. It is possible that the patient may have performed better at going from December to January because of learning effect. Our reference standard for delirium was based upon DSM-IV-TR criteria. The new DSM-V criteria may be more restrictive and may slightly change the sensitivities and specificities of the inattention tasks. We enrolled a convenience sample and enrolled patients who were more likely to be male, have cardiovascular chief complaints, and be admitted to the hospital; as a result, selection bias may have been introduced. Lastly, this study was conducted in a single center and enrolled patients who were 65 years and older. Our findings may not be generalizable to other settings and in those who are less than 65 years of age.

 

CONCLUSIONS

The MOTYB-6 and MOTYB-12 tasks had very good sensitivities but modest specificities (approximately 50%) using any error made as a cutoff; increasing cutoff to 2 errors and 3 errors, respectively, improved their specificities (approximately 70%) with minimal impact to their sensitivities. Reciting the days of the week backwards, spelling the word “LUNCH” backwards, and the 10-letter vigilance “A” task appeared to perform the best in ruling out delirium but only moderately decreased the likelihood of delirium. The 10-letter Vigilance “A” and picture recognition task appeared to perform the best in ruling in delirium. Days of the week backwards appeared to have the best combination of sensitivity and specificity.

Disclosure

This study was funded by the Emergency Medicine Foundation Career Development Award, National Institutes of Health K23AG032355, and National Center for Research Resources, Grant UL1 RR024975-01. The authors report no financial conflicts of interest.

Delirium is an acute neurocognitive disorder¹ that affects up to 25% of older emergency department (ED) and hospitalized patients.^2-4 The relationship between delirium and adverse outcomes is well documented.^5-7 Delirium is a strong predictor of increased length of mechanical ventilation, longer intensive care unit and hospital stays, increased risk of falls, long-term cognitive impairment, and mortality.^8-13 Delirium is frequently missed by healthcare professionals^2,14-16 and goes undetected in up to 3 out of 4 patients by bedside nurses and medical practitioners in many hospital settings.^14,17-22 A significant barrier to recognizing delirium is the absence of brief delirium assessments.

In an effort to improve delirium recognition in the acute care setting, there has been a concerted effort to develop and validate brief delirium assessments. To address this unmet need, 4 ‘A’s Test (4AT), the Brief Confusion Assessment Method (bCAM), and the 3-minute diagnostic assessment for CAM-defined delirium (3D-CAM) are 1- to 3-minute delirium assessments that were validated in acutely ill older patients.²³ However, 1 to 3 minutes may still be too long in busy clinical environments, and briefer (<30 seconds) delirium assessments may be needed.

One potential more-rapid method to screen for delirium is to specifically test for the presence of inattention, which is a cardinal feature of delirium.^24,25 Inattention can be ascertained by having the patient recite the months backwards, recite the days of the week backwards, or spell a word backwards.²⁶ Recent studies have evaluated the diagnostic accuracy of reciting the months of the year backwards for delirium. O’Regan et al.²⁷ evaluated the diagnostic accuracy of the month of the year backwards from December to July (MOTYB-6) and observed that this task was 84% sensitive and 90% specific for delirium in older patients. However, they performed the reference standard delirium assessments in patients who had a positive MOTYB-6, which can overestimate sensitivity and underestimate specificity (verification bias).²⁸ Fick et al.²⁹ examined the diagnostic accuracy of 20 individual elements of the 3D-CAM and observed that reciting the months of the year backwards from December to January (MOTYB-12) was 83% sensitive and 69% specific for delirium. However, this was an exploratory study that was designed to identify an element of the 3D-CAM that had the best diagnostic accuracy.

To address these limitations, we sought to evaluate the diagnostic performance of the MOTYB-6 and MOTYB-12 for delirium as diagnosed by a reference standard. We also explored other brief tests of inattention such as spelling a word (“LUNCH”) backwards, reciting the days of the week backwards, 10-letter vigilance “A” task, and 5 picture recognition task.

METHODS

Study Design and Setting

This was a preplanned secondary analysis of a prospective observational study that validated 3 delirium assessments.^30,31 This study was conducted at a tertiary care, academic ED. The local institutional review board (IRB) reviewed and approved this study. Informed consent from the patient or an authorized surrogate was obtained whenever possible. Because this was an observational study and posed minimal risk to the patient, the IRB granted a waiver of consent for patients who were both unable to provide consent and were without an authorized surrogate available in the ED or by phone.

Selection of Participants

We enrolled a convenience sample of patients between June 2010 and February 2012 Monday through Friday from 8 am to 4 pm. This enrollment window was based upon the psychiatrist’s availability. Because of the extensiveness of the psychiatric evaluations, we limited enrollment to 1 patient per day. Patients who were 65 years or older, not in a hallway bed, and in the ED for less than 12 hours at the time of enrollment were included. We used a 12-hour cutoff so that patients who presented in the evening and early morning hours could be included. Patients were excluded if they were previously enrolled, non-English speaking, deaf or blind, comatose, suffered from end-stage dementia, or were unable to complete all the study assessments. The rationale for excluding patients with end-stage dementia was that diagnosing delirium in this patient population is challenging.

 

Research assistants approached patients who met inclusion criteria and determined if any exclusion criteria were present. If none of the exclusion criteria were present, then the research assistant reviewed the informed consent document with the patient or authorized surrogate if the patient was not capable of providing consent. If a patient was not capable of providing consent and no authorized surrogate was available, then the patient was enrolled (under the waiver of consent) as long as the patient assented to be a part of the study. Once the patient was enrolled, the research assistant contacted the physician rater and reference standard psychiatrists to approach the patient.

Measures of Inattention

An emergency physician (JHH) who had no formal training in the mental status assessment of elders administered a cognitive battery to the patient, including tests of inattention. The following inattention tasks were administered:

• Spell the word “LUNCH” backwards.³⁰ Patients were initially allowed to spell the word “LUNCH” forwards. Patients who were unable to perform the task were assigned 5 errors.
• Recite the months of the year backwards from December to July.^{23,26,27,30,32} Patients who were unable to perform the task were assigned 6 errors.
• Recite the days of the week backwards.^23,26,33 Patients who were unable to perform the task were assigned 7 errors.
• Ten-letter vigilance “A” task.³⁴ The patient was given a series of 10 letters (“S-A-V-E-A-H-A-A-R-T”) every 3 seconds and was asked to squeeze the rater’s hand every time the patient heard the letter “A.” Patients who were unable to perform the task were assigned 10 errors.
• Five picture recognition task.³⁴ Patients were shown 5 objects on picture cards. Afterwards, patients were shown 10 pictures with the previously shown objects intermingled. The patient had to identify which objects were seen previously in the first 5 pictures. Patients who were unable to perform the task were assigned 10 errors.
• Recite the months of the year backwards from December to January.²⁹ Patients who were unable to perform the task were assigned 12 errors.

Reference Standard for Delirium

A comprehensive consultation-liaison psychiatrist assessment was the reference standard for delirium; the diagnosis of delirium was based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria.³⁵ Three psychiatrists who each had an average of 11 years of clinical experience and regularly diagnosed delirium as part of their daily clinical practice were available to perform these assessments. To arrive at the diagnosis of delirium, they interviewed those who best understood the patient’s mental status (eg, the patient’s family members or caregivers, physician, and nurses). They also reviewed the patient’s medical record and radiology and laboratory test results. They performed bedside cognitive testing that included, but was not limited to, the Mini-Mental State Examination, Clock Drawing Test, Luria hand sequencing task, and tests for verbal fluency. A focused neurological examination was also performed (ie, screening for paraphasic errors, tremors, tone, asterixis, frontal release signs, etc.), and they also evaluated the patient for affective lability, hallucinations, and level of alertness. If the presence of delirium was still questionable, then confrontational naming, proverb interpretation or similarities, and assessments for apraxias were performed at the discretion of the psychiatrist. The psychiatrists were blinded to the physician’s assessments, and the assessments were conducted within 3 hours of each other.

Additional Variables Collected

Using medical record review, comorbidity burden, severity of illness, and premorbid cognition were ascertained. The Charlson Comorbidity Index, a weighted index that takes into account the number and seriousness of 19 preexisting comorbid conditions, was used to quantify comorbidity burden; higher scores indicate higher comorbid burden.^36,37 The Acute Physiology Score of the Acute Physiology and Chronic Health Evaluation II was used to quantify severity of illness.³⁸ This score is based upon the initial values of 12 routine physiologic measurements such as vital sign and laboratory abnormalities; higher scores represent higher severities of illness.³⁸ The medical record was reviewed to ascertain the presence of premorbid cognitive impairment; any documentation of dementia in the patient’s clinical problem list or physician history and physical examination from the outpatient or inpatient settings was considered positive. The medical record review was performed by a research assistant and was double-checked for accuracy by one of the investigators (JHH).

Data Analyses

Measures of central tendency and dispersion for continuous variables were reported as medians and interquartile ranges. Categorical variables were reported as proportions. Receiver operating characteristic curves were constructed for each inattention task. Area under the receiver operating characteristic curves (AUC) was reported to provide a global measure of diagnostic accuracy. Sensitivities, specificities, positive likelihood ratios (PLRs), and negative likelihood ratios (NLRs) with their 95% CIs were calculated using the psychiatrist’s assessment as the reference standard.³⁹ Cut-points with PLRs greater than 10 (strongly increased the likelihood of delirium) or NLRs less than 0.1 (strongly decreased the likelihood of delirium) were preferentially reported whenever possible.

 

All statistical analyses were performed with open source R statistical software version 3.0.1 (http://www.r-project.org/), SAS 9.4 (SAS Institute, Cary, NC), and Microsoft Excel 2010 (Microsoft Inc., Redmond, WA).

RESULTS

A total of 542 patients were screened; 214 patients refused to participate, and 93 were excluded, leaving 235 patients. The patient characteristics can be seen in Table 1. Compared with all patients (N = 15,359) who presented to the ED during the study period, enrolled patients were similar in age but more likely to be male, have cardiovascular chief complaints, and be admitted to the hospital. Of those enrolled, 25 (10.6%) were delirious. Delirious patients were older, more likely to be nonwhite, have a past history of dementia, have a graduate school degree, and have a chief complaint of altered mental status.

Making any error on the MOTYB-6 task had a sensitivity of 80.0% (95% CI, 60.9%-91.1%), specificity of 57.1% (95% CI, 50.4%-63.7%), PLR of 1.87 (95% CI, 1.45-2.40) and NLR of 0.35 (95% CI, 0.16-0.77) for delirium as diagnosed by a psychiatrist. Making any error on the MOTYB-12 task had a sensitivity of 84.0% (95% CI, 65.4%-93.6%), specificity of 51.9% (95% CI, 45.2%-58.5%), PLR of 1.75 (95% CI, 1.40-2.18), and NLR of 0.31 (95% CI, 0.12-0.76) for delirium. The AUCs for the MOTYB-6 and MOTYB-12 tasks were 0.77 and 0.78, respectively, indicating very good diagnostic performance.

The diagnostic performances of the other inattention tasks and additional cutoff values for the MOTYB-6 and MOTYB-12 tasks can be seen in Table 2. Increasing the MOTYB-6 cut-off to 2 or more errors and MOTYB-12 cut-off to 3 or more errors increased the specificity to 70.0% and 70.5%, respectively, without decreasing their sensitivity. The best combination of sensitivity and specificity was reciting the days of the week backwards task; if the patient made any error, this was 84.0% (95% CI, 65.4%-93.6%) sensitive and 81.9% (95% CI, 76.1%-86.5%) specific for delirium. The inattention tasks that strongly increased the likelihood of delirium (PLR > 10) were the vigilance “A” and picture recognition tasks. If the patient made 2 or more errors on the vigilance task or 3 or more errors on the picture recognition task, then the likelihood of delirium strongly increased, as evidenced by a PLR of 16.80 (95% CI, 8.01-35.23) and 23.10 (95% CI, 7.95-67.12), respectively. No other inattention tasks were able to achieve a PLR of greater than 10, regardless of what cutoff was used. No inattention task was able to achieve a NLR of less than 0.10, which would have strongly decreased the likelihood of delirium. The best NLRs were if the patient made no errors spelling the word “LUNCH” backwards (NLR, 0.16; 95% CI, 0.04-0.60), no errors on the vigilance “A” task (NLR, 0.18; 95% CI, 0.07-0.43), and no errors on the days of the week backwards task (NLR, 0.20; 95% CI, 0.08-0.48).

DISCUSSION

Delirium is frequently missed by healthcare providers because it is not routinely screened for in the acute care setting. To help address this deficiency of care, we evaluated several brief measures of inattention that take less than 30 seconds to complete. We observed that any errors made on the MOTYB-6 and MOTYB-12 tasks had very good sensitivities (80% and 84%) but were limited by their modest specificities (approximately 50%) for delirium. As a result, these assessments have limited clinical utility as standalone delirium screens. We also explored other commonly used brief measures of inattention and at a variety of error cutoffs. Reciting the days of the week backwards appeared to best balance sensitivity and specificity. None of the inattention measures could convincingly rule out delirium (NLR < 0.10), but the vigilance “A” and picture recognition tasks may have clinical utility in ruling in delirium (PLR > 10). Overall, all the inattention tasks, including MOTYB-6 and MOTYB-12, had very good diagnostic performances based upon their AUC. However, achieving a high sensitivity often had to be sacrificed for specificity or, alternatively, achieving a high specificity had to be sacrificed for sensitivity.

Inattention has been shown to be the cardinal feature for delirium,⁴⁰ and its assessment using cognitive testing has been recommended to help identify the presence of delirium according to an expert consensus panel.²⁶ The diagnostic performance of the MOTYB-12 observed in our study is similar to a study by Fick et al., who reported that MOTYB-12 had very good sensitivity (83%) but had modest specificity (69%) with a cutoff of 1 or more errors. Hendry et al. observed that the MOTYB-12 was 91% sensitive and 50% specific using a cutoff of 4 or more errors. With regard to the MOTYB-6, our reported specificity was different from what was observed by O’Regan et al.²⁷ Using 1 or more errors as a cutoff, they observed a much higher specificity for delirium than we did (90% vs 57%). Discordant observations regarding the diagnostic accuracy for other inattention tasks also exist. We observed that making any error on the days of the week backwards task was 84% sensitive and 82% specific for delirium, whereas Fick et al. observed a sensitivity and specificity of 50% and 94%, respectively. For the vigilance “A” task, we observed that making 2 or more errors over a series of 10 letters was 64.0% sensitive and 91.4% specific for delirium, whereas Pompei et al.⁴¹ observed that making 2 or more errors over a series of 60 letters was 51% sensitive and 77% specific for delirium.

The abovementioned discordant findings may be driven by spectrum bias, wherein the sensitivities and specificities for each inattention task may differ in different subgroups. As a result, differences in the age distribution, proportion of college graduates, history of dementia, and susceptibility to delirium can influence overall sensitivity and specificity. Objective measures of delirium, including the inattention screens studied, are particularly prone to spectrum bias.^31,34 However, the strength of this approach is that the assessment of inattention becomes less reliant upon clinical judgment and allows it to be used by raters from a wide range of clinical backgrounds. On the other hand, a subjective interpretation of these inattention tasks may allow the rater to capture the subtleties of inattention (ie, decreased speed of performance in a highly intelligent and well-educated patient without dementia). The disadvantage of this approach, however, is that it is more dependent on clinical judgment and may have decreased diagnostic accuracy in those with less clinical experience or with limited training.^14,42,43 These factors must be carefully considered when determining which delirium assessment to use.

Additional research is required to determine the clinical utility of these brief inattention assessments. These findings need to be further validated in larger studies, and the optimal cutoff of each task for different subgroup of patients (eg, demented vs nondemented) needs to be further clarified. It is not completely clear whether these inattention tests can serve as standalone assessments. Depending on the cutoff used, some of these assessments may have unacceptable false negative or false positive rates that may lead to increased adverse patient outcomes or increased resource utilization, respectively. Additional components or assessments may be needed to improve the diagnostic accuracy of these assessments. In addition to understanding these inattention assessments’ diagnostic accuracies, their ability to predict adverse outcomes also needs to be investigated. While a previous study observed that making any error on the MOTYB-12 task was associated with increased physical restraint use and prolonged hospital length of stay,⁴⁴ these assessments’ ability to prognosticate long-term outcomes such as mortality or long-term cognition or function need to be studied. Lastly, studies should also evaluate how easily implementable these assessments are and whether improved delirium recognition leads to improved patient outcomes.

This study has several notable limitations. Though planned a priori, this was a secondary analysis of a larger investigation designed to validate 3 delirium assessments. Our sample size was also relatively small, causing our 95% CIs to overlap in most cases and limiting the statistical power to truly determine whether one measure is better than the other. We also asked the patient to recite the months backwards from December to July as well as recite the months backwards from December to January. It is possible that the patient may have performed better at going from December to January because of learning effect. Our reference standard for delirium was based upon DSM-IV-TR criteria. The new DSM-V criteria may be more restrictive and may slightly change the sensitivities and specificities of the inattention tasks. We enrolled a convenience sample and enrolled patients who were more likely to be male, have cardiovascular chief complaints, and be admitted to the hospital; as a result, selection bias may have been introduced. Lastly, this study was conducted in a single center and enrolled patients who were 65 years and older. Our findings may not be generalizable to other settings and in those who are less than 65 years of age.

 

CONCLUSIONS

The MOTYB-6 and MOTYB-12 tasks had very good sensitivities but modest specificities (approximately 50%) using any error made as a cutoff; increasing cutoff to 2 errors and 3 errors, respectively, improved their specificities (approximately 70%) with minimal impact to their sensitivities. Reciting the days of the week backwards, spelling the word “LUNCH” backwards, and the 10-letter vigilance “A” task appeared to perform the best in ruling out delirium but only moderately decreased the likelihood of delirium. The 10-letter Vigilance “A” and picture recognition task appeared to perform the best in ruling in delirium. Days of the week backwards appeared to have the best combination of sensitivity and specificity.

Disclosure

This study was funded by the Emergency Medicine Foundation Career Development Award, National Institutes of Health K23AG032355, and National Center for Research Resources, Grant UL1 RR024975-01. The authors report no financial conflicts of interest.

Delirium is an acute neurocognitive disorder¹ that affects up to 25% of older emergency department (ED) and hospitalized patients.^2-4 The relationship between delirium and adverse outcomes is well documented.^5-7 Delirium is a strong predictor of increased length of mechanical ventilation, longer intensive care unit and hospital stays, increased risk of falls, long-term cognitive impairment, and mortality.^8-13 Delirium is frequently missed by healthcare professionals^2,14-16 and goes undetected in up to 3 out of 4 patients by bedside nurses and medical practitioners in many hospital settings.^14,17-22 A significant barrier to recognizing delirium is the absence of brief delirium assessments.

In an effort to improve delirium recognition in the acute care setting, there has been a concerted effort to develop and validate brief delirium assessments. To address this unmet need, 4 ‘A’s Test (4AT), the Brief Confusion Assessment Method (bCAM), and the 3-minute diagnostic assessment for CAM-defined delirium (3D-CAM) are 1- to 3-minute delirium assessments that were validated in acutely ill older patients.²³ However, 1 to 3 minutes may still be too long in busy clinical environments, and briefer (<30 seconds) delirium assessments may be needed.

One potential more-rapid method to screen for delirium is to specifically test for the presence of inattention, which is a cardinal feature of delirium.^24,25 Inattention can be ascertained by having the patient recite the months backwards, recite the days of the week backwards, or spell a word backwards.²⁶ Recent studies have evaluated the diagnostic accuracy of reciting the months of the year backwards for delirium. O’Regan et al.²⁷ evaluated the diagnostic accuracy of the month of the year backwards from December to July (MOTYB-6) and observed that this task was 84% sensitive and 90% specific for delirium in older patients. However, they performed the reference standard delirium assessments in patients who had a positive MOTYB-6, which can overestimate sensitivity and underestimate specificity (verification bias).²⁸ Fick et al.²⁹ examined the diagnostic accuracy of 20 individual elements of the 3D-CAM and observed that reciting the months of the year backwards from December to January (MOTYB-12) was 83% sensitive and 69% specific for delirium. However, this was an exploratory study that was designed to identify an element of the 3D-CAM that had the best diagnostic accuracy.

To address these limitations, we sought to evaluate the diagnostic performance of the MOTYB-6 and MOTYB-12 for delirium as diagnosed by a reference standard. We also explored other brief tests of inattention such as spelling a word (“LUNCH”) backwards, reciting the days of the week backwards, 10-letter vigilance “A” task, and 5 picture recognition task.

METHODS

Study Design and Setting

This was a preplanned secondary analysis of a prospective observational study that validated 3 delirium assessments.^30,31 This study was conducted at a tertiary care, academic ED. The local institutional review board (IRB) reviewed and approved this study. Informed consent from the patient or an authorized surrogate was obtained whenever possible. Because this was an observational study and posed minimal risk to the patient, the IRB granted a waiver of consent for patients who were both unable to provide consent and were without an authorized surrogate available in the ED or by phone.

Selection of Participants

We enrolled a convenience sample of patients between June 2010 and February 2012 Monday through Friday from 8 am to 4 pm. This enrollment window was based upon the psychiatrist’s availability. Because of the extensiveness of the psychiatric evaluations, we limited enrollment to 1 patient per day. Patients who were 65 years or older, not in a hallway bed, and in the ED for less than 12 hours at the time of enrollment were included. We used a 12-hour cutoff so that patients who presented in the evening and early morning hours could be included. Patients were excluded if they were previously enrolled, non-English speaking, deaf or blind, comatose, suffered from end-stage dementia, or were unable to complete all the study assessments. The rationale for excluding patients with end-stage dementia was that diagnosing delirium in this patient population is challenging.

 

Research assistants approached patients who met inclusion criteria and determined if any exclusion criteria were present. If none of the exclusion criteria were present, then the research assistant reviewed the informed consent document with the patient or authorized surrogate if the patient was not capable of providing consent. If a patient was not capable of providing consent and no authorized surrogate was available, then the patient was enrolled (under the waiver of consent) as long as the patient assented to be a part of the study. Once the patient was enrolled, the research assistant contacted the physician rater and reference standard psychiatrists to approach the patient.

Measures of Inattention

An emergency physician (JHH) who had no formal training in the mental status assessment of elders administered a cognitive battery to the patient, including tests of inattention. The following inattention tasks were administered:

• Spell the word “LUNCH” backwards.³⁰ Patients were initially allowed to spell the word “LUNCH” forwards. Patients who were unable to perform the task were assigned 5 errors.
• Recite the months of the year backwards from December to July.^{23,26,27,30,32} Patients who were unable to perform the task were assigned 6 errors.
• Recite the days of the week backwards.^23,26,33 Patients who were unable to perform the task were assigned 7 errors.
• Ten-letter vigilance “A” task.³⁴ The patient was given a series of 10 letters (“S-A-V-E-A-H-A-A-R-T”) every 3 seconds and was asked to squeeze the rater’s hand every time the patient heard the letter “A.” Patients who were unable to perform the task were assigned 10 errors.
• Five picture recognition task.³⁴ Patients were shown 5 objects on picture cards. Afterwards, patients were shown 10 pictures with the previously shown objects intermingled. The patient had to identify which objects were seen previously in the first 5 pictures. Patients who were unable to perform the task were assigned 10 errors.
• Recite the months of the year backwards from December to January.²⁹ Patients who were unable to perform the task were assigned 12 errors.

Reference Standard for Delirium

A comprehensive consultation-liaison psychiatrist assessment was the reference standard for delirium; the diagnosis of delirium was based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria.³⁵ Three psychiatrists who each had an average of 11 years of clinical experience and regularly diagnosed delirium as part of their daily clinical practice were available to perform these assessments. To arrive at the diagnosis of delirium, they interviewed those who best understood the patient’s mental status (eg, the patient’s family members or caregivers, physician, and nurses). They also reviewed the patient’s medical record and radiology and laboratory test results. They performed bedside cognitive testing that included, but was not limited to, the Mini-Mental State Examination, Clock Drawing Test, Luria hand sequencing task, and tests for verbal fluency. A focused neurological examination was also performed (ie, screening for paraphasic errors, tremors, tone, asterixis, frontal release signs, etc.), and they also evaluated the patient for affective lability, hallucinations, and level of alertness. If the presence of delirium was still questionable, then confrontational naming, proverb interpretation or similarities, and assessments for apraxias were performed at the discretion of the psychiatrist. The psychiatrists were blinded to the physician’s assessments, and the assessments were conducted within 3 hours of each other.

Additional Variables Collected

Using medical record review, comorbidity burden, severity of illness, and premorbid cognition were ascertained. The Charlson Comorbidity Index, a weighted index that takes into account the number and seriousness of 19 preexisting comorbid conditions, was used to quantify comorbidity burden; higher scores indicate higher comorbid burden.^36,37 The Acute Physiology Score of the Acute Physiology and Chronic Health Evaluation II was used to quantify severity of illness.³⁸ This score is based upon the initial values of 12 routine physiologic measurements such as vital sign and laboratory abnormalities; higher scores represent higher severities of illness.³⁸ The medical record was reviewed to ascertain the presence of premorbid cognitive impairment; any documentation of dementia in the patient’s clinical problem list or physician history and physical examination from the outpatient or inpatient settings was considered positive. The medical record review was performed by a research assistant and was double-checked for accuracy by one of the investigators (JHH).

Data Analyses

Measures of central tendency and dispersion for continuous variables were reported as medians and interquartile ranges. Categorical variables were reported as proportions. Receiver operating characteristic curves were constructed for each inattention task. Area under the receiver operating characteristic curves (AUC) was reported to provide a global measure of diagnostic accuracy. Sensitivities, specificities, positive likelihood ratios (PLRs), and negative likelihood ratios (NLRs) with their 95% CIs were calculated using the psychiatrist’s assessment as the reference standard.³⁹ Cut-points with PLRs greater than 10 (strongly increased the likelihood of delirium) or NLRs less than 0.1 (strongly decreased the likelihood of delirium) were preferentially reported whenever possible.

 

All statistical analyses were performed with open source R statistical software version 3.0.1 (http://www.r-project.org/), SAS 9.4 (SAS Institute, Cary, NC), and Microsoft Excel 2010 (Microsoft Inc., Redmond, WA).

RESULTS

A total of 542 patients were screened; 214 patients refused to participate, and 93 were excluded, leaving 235 patients. The patient characteristics can be seen in Table 1. Compared with all patients (N = 15,359) who presented to the ED during the study period, enrolled patients were similar in age but more likely to be male, have cardiovascular chief complaints, and be admitted to the hospital. Of those enrolled, 25 (10.6%) were delirious. Delirious patients were older, more likely to be nonwhite, have a past history of dementia, have a graduate school degree, and have a chief complaint of altered mental status.

Making any error on the MOTYB-6 task had a sensitivity of 80.0% (95% CI, 60.9%-91.1%), specificity of 57.1% (95% CI, 50.4%-63.7%), PLR of 1.87 (95% CI, 1.45-2.40) and NLR of 0.35 (95% CI, 0.16-0.77) for delirium as diagnosed by a psychiatrist. Making any error on the MOTYB-12 task had a sensitivity of 84.0% (95% CI, 65.4%-93.6%), specificity of 51.9% (95% CI, 45.2%-58.5%), PLR of 1.75 (95% CI, 1.40-2.18), and NLR of 0.31 (95% CI, 0.12-0.76) for delirium. The AUCs for the MOTYB-6 and MOTYB-12 tasks were 0.77 and 0.78, respectively, indicating very good diagnostic performance.

The diagnostic performances of the other inattention tasks and additional cutoff values for the MOTYB-6 and MOTYB-12 tasks can be seen in Table 2. Increasing the MOTYB-6 cut-off to 2 or more errors and MOTYB-12 cut-off to 3 or more errors increased the specificity to 70.0% and 70.5%, respectively, without decreasing their sensitivity. The best combination of sensitivity and specificity was reciting the days of the week backwards task; if the patient made any error, this was 84.0% (95% CI, 65.4%-93.6%) sensitive and 81.9% (95% CI, 76.1%-86.5%) specific for delirium. The inattention tasks that strongly increased the likelihood of delirium (PLR > 10) were the vigilance “A” and picture recognition tasks. If the patient made 2 or more errors on the vigilance task or 3 or more errors on the picture recognition task, then the likelihood of delirium strongly increased, as evidenced by a PLR of 16.80 (95% CI, 8.01-35.23) and 23.10 (95% CI, 7.95-67.12), respectively. No other inattention tasks were able to achieve a PLR of greater than 10, regardless of what cutoff was used. No inattention task was able to achieve a NLR of less than 0.10, which would have strongly decreased the likelihood of delirium. The best NLRs were if the patient made no errors spelling the word “LUNCH” backwards (NLR, 0.16; 95% CI, 0.04-0.60), no errors on the vigilance “A” task (NLR, 0.18; 95% CI, 0.07-0.43), and no errors on the days of the week backwards task (NLR, 0.20; 95% CI, 0.08-0.48).

DISCUSSION

Delirium is frequently missed by healthcare providers because it is not routinely screened for in the acute care setting. To help address this deficiency of care, we evaluated several brief measures of inattention that take less than 30 seconds to complete. We observed that any errors made on the MOTYB-6 and MOTYB-12 tasks had very good sensitivities (80% and 84%) but were limited by their modest specificities (approximately 50%) for delirium. As a result, these assessments have limited clinical utility as standalone delirium screens. We also explored other commonly used brief measures of inattention and at a variety of error cutoffs. Reciting the days of the week backwards appeared to best balance sensitivity and specificity. None of the inattention measures could convincingly rule out delirium (NLR < 0.10), but the vigilance “A” and picture recognition tasks may have clinical utility in ruling in delirium (PLR > 10). Overall, all the inattention tasks, including MOTYB-6 and MOTYB-12, had very good diagnostic performances based upon their AUC. However, achieving a high sensitivity often had to be sacrificed for specificity or, alternatively, achieving a high specificity had to be sacrificed for sensitivity.

Inattention has been shown to be the cardinal feature for delirium,⁴⁰ and its assessment using cognitive testing has been recommended to help identify the presence of delirium according to an expert consensus panel.²⁶ The diagnostic performance of the MOTYB-12 observed in our study is similar to a study by Fick et al., who reported that MOTYB-12 had very good sensitivity (83%) but had modest specificity (69%) with a cutoff of 1 or more errors. Hendry et al. observed that the MOTYB-12 was 91% sensitive and 50% specific using a cutoff of 4 or more errors. With regard to the MOTYB-6, our reported specificity was different from what was observed by O’Regan et al.²⁷ Using 1 or more errors as a cutoff, they observed a much higher specificity for delirium than we did (90% vs 57%). Discordant observations regarding the diagnostic accuracy for other inattention tasks also exist. We observed that making any error on the days of the week backwards task was 84% sensitive and 82% specific for delirium, whereas Fick et al. observed a sensitivity and specificity of 50% and 94%, respectively. For the vigilance “A” task, we observed that making 2 or more errors over a series of 10 letters was 64.0% sensitive and 91.4% specific for delirium, whereas Pompei et al.⁴¹ observed that making 2 or more errors over a series of 60 letters was 51% sensitive and 77% specific for delirium.

The abovementioned discordant findings may be driven by spectrum bias, wherein the sensitivities and specificities for each inattention task may differ in different subgroups. As a result, differences in the age distribution, proportion of college graduates, history of dementia, and susceptibility to delirium can influence overall sensitivity and specificity. Objective measures of delirium, including the inattention screens studied, are particularly prone to spectrum bias.^31,34 However, the strength of this approach is that the assessment of inattention becomes less reliant upon clinical judgment and allows it to be used by raters from a wide range of clinical backgrounds. On the other hand, a subjective interpretation of these inattention tasks may allow the rater to capture the subtleties of inattention (ie, decreased speed of performance in a highly intelligent and well-educated patient without dementia). The disadvantage of this approach, however, is that it is more dependent on clinical judgment and may have decreased diagnostic accuracy in those with less clinical experience or with limited training.^14,42,43 These factors must be carefully considered when determining which delirium assessment to use.

Additional research is required to determine the clinical utility of these brief inattention assessments. These findings need to be further validated in larger studies, and the optimal cutoff of each task for different subgroup of patients (eg, demented vs nondemented) needs to be further clarified. It is not completely clear whether these inattention tests can serve as standalone assessments. Depending on the cutoff used, some of these assessments may have unacceptable false negative or false positive rates that may lead to increased adverse patient outcomes or increased resource utilization, respectively. Additional components or assessments may be needed to improve the diagnostic accuracy of these assessments. In addition to understanding these inattention assessments’ diagnostic accuracies, their ability to predict adverse outcomes also needs to be investigated. While a previous study observed that making any error on the MOTYB-12 task was associated with increased physical restraint use and prolonged hospital length of stay,⁴⁴ these assessments’ ability to prognosticate long-term outcomes such as mortality or long-term cognition or function need to be studied. Lastly, studies should also evaluate how easily implementable these assessments are and whether improved delirium recognition leads to improved patient outcomes.

This study has several notable limitations. Though planned a priori, this was a secondary analysis of a larger investigation designed to validate 3 delirium assessments. Our sample size was also relatively small, causing our 95% CIs to overlap in most cases and limiting the statistical power to truly determine whether one measure is better than the other. We also asked the patient to recite the months backwards from December to July as well as recite the months backwards from December to January. It is possible that the patient may have performed better at going from December to January because of learning effect. Our reference standard for delirium was based upon DSM-IV-TR criteria. The new DSM-V criteria may be more restrictive and may slightly change the sensitivities and specificities of the inattention tasks. We enrolled a convenience sample and enrolled patients who were more likely to be male, have cardiovascular chief complaints, and be admitted to the hospital; as a result, selection bias may have been introduced. Lastly, this study was conducted in a single center and enrolled patients who were 65 years and older. Our findings may not be generalizable to other settings and in those who are less than 65 years of age.

 

CONCLUSIONS

The MOTYB-6 and MOTYB-12 tasks had very good sensitivities but modest specificities (approximately 50%) using any error made as a cutoff; increasing cutoff to 2 errors and 3 errors, respectively, improved their specificities (approximately 70%) with minimal impact to their sensitivities. Reciting the days of the week backwards, spelling the word “LUNCH” backwards, and the 10-letter vigilance “A” task appeared to perform the best in ruling out delirium but only moderately decreased the likelihood of delirium. The 10-letter Vigilance “A” and picture recognition task appeared to perform the best in ruling in delirium. Days of the week backwards appeared to have the best combination of sensitivity and specificity.

Disclosure

This study was funded by the Emergency Medicine Foundation Career Development Award, National Institutes of Health K23AG032355, and National Center for Research Resources, Grant UL1 RR024975-01. The authors report no financial conflicts of interest.

A special concern since the institution of hospital readmission penalties¹ is the transitions in care of a patient from one care setting to another, often at hospital discharge. Burke et al.² proposed a framework for an ideal transition in care (ITC) to study and improve transitions from the hospital to home. The features in the ITC were identified based upon their inclusion in the interventions that improved discharge outcomes.^3-5 Inspired by the ITC and other patient risk tools,⁶ we identified 10 domains of transitional care needs ([TCN] specified below), which we define as patient-centered risk factors that should be addressed to foster a safe and effective transition in care.⁷

One particularly important risk factor in patient self-management at transition points is health literacy, a patient’s ability to obtain, understand, and use basic health information and services. Low health literacy affects approximately 26% to 36% of adults in the United States.^8,9 Health literacy is associated with many factors that may affect successful navigation of care transitions, including doctor-patient communication,^10,11 understanding of the medication regimen,¹² and self-management.^13-15 Research has also demonstrated an association between low health literacy and poor outcomes after hospital discharge, including medication errors,¹⁶ 30-day hospital readmission,¹⁷ and mortality.¹⁸ Transitional care initiatives have begun to incorporate health literacy into patient risk assessments⁶ and provide specific attention to low health literacy in interventions to reduce adverse drug events and readmission.^4,19 Training programs for medical students and nurses advise teaching skills in health literacy as part of fostering effective transitions in care.^20,21

Although low health literacy is generally recognized as a barrier to patient education and self-management, little is known about whether patients with low health literacy are more likely to have other risk factors that could further increase their risk for poor transitions in care. A better understanding of associated risks would inform and improve patient care. We hypothesized that TCNs are more common among patients with low health literacy, as compared with those with adequate health literacy. We also aimed to describe the relationship between low health literacy and specific TCNs in order to guide clinical care and future interventions.

METHODS

Setting

The present study is a cross-sectional analysis of data from a quality improvement (QI) intervention that was performed at Vanderbilt University Medical Center, a tertiary care facility in Nashville, Tennessee. The QI intervention, My Health Team (MHT), was funded by the Centers for Medicare and Medicaid Services Innovation Award program. The overall MHT program included outpatient care coordination for chronic disease management as well as a transitional care program that was designed to reduce hospital readmission. The latter included an inpatient needs assessment (which provided data for the present analysis), inpatient intervention, and postdischarge phone follow-up. The MHT initiative was reviewed by the institutional review board (IRB), which deemed it a QI program and granted a waiver of informed consent. The present secondary data analysis was reviewed and approved by the IRB.

Sample

Patients were identified for inclusion in the MHT transitions of care program if the presenting problem for hospital admission was pneumonia, chronic obstructive pulmonary disease (COPD) exacerbation, or decompensated heart failure, as determined by the review of clinical documentation by nurse transition care coordinators (TCCs). Adults over the age of 18 years were eligible, though priority was given to patients aged 65 years or older. This study includes the first inpatient encounter between June 2013 and December 31, 2014, for patients having a completed needs assessment and documentation of health literacy data in the medical record.

 

Data Collection

TCN assessment was developed from published patient risk tools and the ITC framework.^2,6,22 The assessment has 10 domains composed of 49 individual items as follows: (1) caregiver support (caregiver support not sufficient for patient needs), (2) transportation (relies on public or others for transportation and misses medical care because of transportation), (3) health care utilization (no primary care physician, unplanned hospitalization in the last year, emergency department [ED] visit in the last 6 months, or home health services in the last 60 days), (4) high-risk medical comorbidities (malnutrition or body mass index <18.5, renal failure, chronic pain, diabetes, heart failure, COPD, or stroke), (5) medication management provider or caregiver concern (cannot provide medication list, >10 preadmission medications, high-risk medications [eg, insulin, warfarin], poor medication understanding, or adherence issue identified), (6) medical devices (vascular access, urinary catheter, wounds, or home supplemental oxygen), (7) functional status (weakness of extremities, limited extremity range of motion, difficulty with mobility, falls at home, or activities of daily living challenges), (8) mental health comorbidities (over the past month has felt down, depressed, or hopeless or over the past month has felt little interest or pleasure in doing things, high-risk alcohol use, or high-risk substance use), (9) communication (limited English proficiency or at risk for limited health literacy), and (10) financial resources (no health insurance, skips or rations medicines because of cost, misses medical care because of cost, or misses medical care because of job).

The 49 items of the TCN assessment were documented as being present or absent by nurse TCCs at the time patients were enrolled in the transitional care program, based on patient and family interview and chart review, and the items were later extracted for analysis. Patients were determined to have a domain-level need if they reported a need on any individual item within that domain, resulting in a binary score (any need present, absent) for each of the 10 TCN domains.

Health literacy was assessed by using the Brief Health Literacy Screen (BHLS), which is administered routinely by nurses at hospital intake and documented in the medical record, with completion rates of approximately 90%.²³ The BHLS is a 3-question subjective health literacy assessment (scoring range 3-15) that has been validated against longer objective measures²⁴ and shown to predict disease control and mortality.^18,25 To improve the stability of scores (for patients who completed the BHLS more than once because of repeat hospitalizations) and to reduce missing values, we calculated the patient’s mean BHLS score for assessments obtained between January 1, 2013, and December 31, 2014. Patients were then categorized as having inadequate health literacy (BHLS ≤ 9) or adequate health literacy (BHLS > 9).^18,25 Demographic information was extracted from patient records and included age, sex (male/female), marital status (married/without a partner), race (white/nonwhite), and years of education. Income level and primary language were not available for analysis.

Statistical Analysis

Patient characteristics and TCNs were summarized by using the frequency and percentages for categorical variables and the mean and standard deviation (SD) for continuous variables. We compared patient characteristics (age, sex, marital status, race, and education) between health literacy groups (inadequate vs adequate) by using χ² or analysis of variance as appropriate. We assessed Pearson correlations among the 10 TCN domains, and we examined differences in reported needs for each of 10 TCN domains by the level of health literacy by using the χ² test. Because the TCN domain of communication included low health literacy as one of its items, we excluded this domain from subsequent analyses. We then compared differences in the number of TCNs documented (scoring range 0-9) by using an independent samples Student t test.

Multivariate logistic regression models were then constructed to examine the independent association of inadequate health literacy with 8 TCN domains while controlling for age, sex, marital status, race, and education. Patients with incomplete demographic data were excluded from these models. Additionally, these analyses excluded 2 TCN domains: the communication domain for reasons noted above and the high-risk medical comorbidity domain because it ended up being positive in 98.4% of patients. Statistical significance was set at an alpha of 0.05. All analyses were performed by using SPSS Statistics for Mac, version 23.0 (IBM Corp., Armonk, New York)

RESULTS

A total of 403 unique patients received the needs assessment, and 384 (95.3%) patients had health literacy data available (Table 1). The number of patients with missing or unknown values were 3 for marital status, 8 for race, and 6 for education. The patients had an average age of 66.9 years (SD = 13.0 years). Among the sample, 209 (54%) were female, 172 (45%) were married, and 291 (75.8%) were white. The average years of education was 12.6 (SD = 2.9 years), and 113 (29%) had inadequate health literacy. Patients with inadequate health literacy completed fewer years of schooling (11.2 vs 13.2; P < 0.001) and were less likely to be married (37% vs 49%; P = 0.031). There was no significant difference in age, sex, or race by level of health literacy.

 

Patients overall had a mean of 4.6 (SD = 1.8) TCN domains with any need reported. The most common domains were high-risk comorbidity (98%), medication management (76%), and healthcare utilization (76%; Table 2). For most domains, the presence of needs was significantly correlated with the presence of needs in multiple other domains (Table 3). Patients with inadequate health literacy had needs in a greater number of TCN domains (mean = 5.29 vs 4.36; P < 0.001).

In unadjusted analysis, patients with inadequate health literacy were significantly more likely to have TCNs in 7 out of the 10 domains (Table 2). These concerns related to caregiver support, transportation, healthcare utilization, presence of a medical device, functional status, mental health comorbidities, and communication. The inadequate and adequate health literacy groups were similar in needs with respect to high-risk comorbidity and finance and borderline nonsignificant for medication management.

In multivariate analyses, 371 patients had complete demographic data and were thus included. After adjustment for age, sex, marital status, race, and education, inadequate health literacy remained significantly associated with reported needs in 2 transitional care domains: inadequate caregiver support (odds ratio [OR], 2.61; 95% confidence interval [CI], 1.37-5.00) and transportation barriers (OR, 1.69; 95% CI, 1.04-2.76; Figure). Other domains approached statistical significance: medical devices (OR, 1.56; 95% CI, 0.96-2.54), functional status (OR, 1.67; 95% CI, 1.00-2.74), and mental health comorbidities (OR, 1.60; 95% CI, 0.98-2.62).

Older age was independently associated with more needs related to medical devices (OR, 1.02; 95% CI, 1.00-1.04), functional status (OR, 1.03; 95% CI, 1.02-1.05), and fewer financial needs (OR, 0.93; 95% CI, 0.91-0.96). Being married or living with a partner was associated with fewer needs related to caregiver support (OR, 0.37; 95% CI, 0.19-0.75) and more device-related needs (OR, 1.60; 95% CI, 1.03-2.49). A higher level of education was associated with fewer transportation needs (OR, 0.89; 95% CI, 0.82-0.97).

DISCUSSION

A structured patient risk factor assessment derived from literature was used to record TCNs in preparation for hospital discharge. On average, patients had needs in about half of the TCN domains (4.6 of 9). The most common areas identified were related to the presence of high-risk comorbidities (98.4%), frequent or prior healthcare utilization (76.6%), medication management (76.3%), functional status (54.9%), and transportation (48.7%). Many of the TCNs were significantly correlated with one another. The prevalence of these needs highlights the importance of using a structured assessment to identify patient concerns so that they may be addressed through discharge planning and follow-up. In addition, using a standardized TCN instrument based on a framework for ITC promotes further research in understanding patient needs and in developing personalized interventions to address them.

As hypothesized, we found that TCNs were more common in patients with inadequate health literacy. After adjustment for demographic factors, inadequate health literacy was significantly associated with transportation barriers and inadequate caregiver support. Analyses also suggested a relationship with needs related to medical devices, functional status, and mental health comorbidities. A review of the literature substantiates a link between inadequate health literacy and these needs and also suggests solutions to address these barriers.

The association with inadequate caregiver support is concerning because there is often a high degree of reliance on caregivers at transitions in care.^3-5 Caregivers are routinely called upon to provide assistance with activities that may be difficult for patients with low health literacy, including medication adherence, provider communication, and self-care activities.^26,27 Our finding that patients with inadequate health literacy are more likely to have inadequate caregiver support indicates additional vulnerability. This may be because of the absence of a caregiver, or in many cases, the presence of a caregiver who is underprepared to assist with care. Prior research has shown that when caregivers are present, up to 33% have low health literacy, even when they are paid nonfamilial caregivers.^26,28 Other studies have noted the inadequacy of information and patient training for caregivers.^29,30 Transitional care programs to improve caregiver understanding have been developed³¹ and have been demonstrated to lower rehospitalization and ED visits.³²

Patients with inadequate health literacy were also more likely to have transportation barriers. Lack of transportation has been recorded as a factor in early hospital readmission in patients with chronic disease,³³ and it has been shown to have a negative effect on a variety of health outcomes.³⁴ A likely link between readmission and lack of transportation is poor follow-up care. Wheeler et al.³⁵ found that 59% of patients expected difficulty keeping postdischarge appointments because of transportation needs. Instead of expecting patients to navigate their own transportation, the Agency for Healthcare Research and Quality recommends identifying community resources for patients with low health literacy.³⁶

In this sample, inadequate health literacy also had near significant associations with TCNs in the use of medical devices, lower functional status, and mental health comorbidities. The use of a medical device, such as home oxygen, is a risk factor for readmission,³⁷ and early reports suggest that interventions, including education related to home oxygen use, can dramatically reduce these readmissions.³⁸ Lower functional capacity and faster functional decline are associated with inadequate health literacy,³⁹ which may have to do with the inability to appropriately utilize health resources.⁴⁰ If so, structured discharge planning could alleviate the known connection between functional impairment and hospital readmissions.⁴¹ A relationship between low health literacy and depression has been demonstrated repeatedly,⁴² with worsened symptoms in those with addiction.⁴³ As has been shown in other domains where health literacy is a factor, literacy-focused interventions provide greater benefits to these depressed patients.⁴⁴

The TCN assessment worked well overall, but certain domains proved less valuable and could be removed in the future. First, it was not useful to separately identify communication barriers, because doing so did not add to information beyond the measurement of health literacy. Second, high-risk comorbidities were ubiquitous within the sample and therefore unhelpful for group comparisons. In hindsight, this is unsurprising because the sample was comprised primarily of elderly patients admitted to medical services. Still, in a younger population or a surgical setting, identifying patients with high-risk medical comorbidities may be more useful.

We acknowledge several limitations of this study. First, the study was performed at a single center, and the TCN assessments were conducted by a small number of registered nurses who received training. Therefore, the results may not generalize to the profile of patient needs at other settings, and the instrument may perform differently when scaled across an organization. Second, the needs assessment was developed for this QI initiative and did not undergo formal validation, although it was developed from published frameworks and similar assessments. Third, for the measure of health literacy, we relied on data collected by nurses as part of their normal workflow. As is often the case with data collected during routine care, the scores are imperfect,⁴⁵ but they have proven to be a valuable and valid indicator of health literacy in our previous research.^18,24,25,46 Fourth, we chose to declare a domain as positive if any item in that domain was positive and to perform a domain-level analysis (for greater clarity). We did not take into account the variable number of items within each domain or attempt to grade their severity, as this would be a subjective exercise and impractical in the discharge planning process. Finally, we were unable to address associations among socioeconomic status,⁴⁷ primary language,⁴⁸ and health literacy, because relevant data were not available for this analysis.

 

CONCLUSION

In this sample of hospitalized patients who were administered a structured needs assessment, patients commonly had needs that placed them at a higher risk of adverse outcomes, such as hospital readmission. Patients with low health literacy had more TCNs that extended beyond the areas that we normally associate with low health literacy, namely patient education and self-management. Healthcare professionals should be aware of the greater likelihood of transportation barriers and inadequate caregiver support among patients with low health literacy. Screening for health literacy and TCN at admission or as part of the discharge planning process will elevate such risks, better positioning clinicians and hospitals to address them as a part of the efforts to ensure a quality transition of care.

Disclosure 

This work was funded by the Centers for Medicare and Medicaid Services (1C1CMS330979) and in part by the National Center for Advancing Translational Sciences (2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies, which did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication.

Dr. Dittus reports personal fees as a board member of the Robert Wood Johnson Foundation Medical Faculty Scholars Program National Advisory Committee; consultancy fees from the University of Virginia, Indiana University, University of Michigan, Northwestern University, Montana State University, and Purdue University; has grants/grants pending from NIH (research grants), PCORI (research grant), CME (innovation award), VA (training grant); payment for lectures including service on speakers bureaus from Corporate Parity (conference organizer) for the Global Hospital Management & Innovation Summit; and other from Medical Decision Making, Inc. (passive owner); all outside the submitted work. Dr. Kripalani has grants from NIH (research grant), PCORI (research grant), and CMS (QI grant); outside the submitted work. All other authors have nothing to disclose.

A special concern since the institution of hospital readmission penalties¹ is the transitions in care of a patient from one care setting to another, often at hospital discharge. Burke et al.² proposed a framework for an ideal transition in care (ITC) to study and improve transitions from the hospital to home. The features in the ITC were identified based upon their inclusion in the interventions that improved discharge outcomes.^3-5 Inspired by the ITC and other patient risk tools,⁶ we identified 10 domains of transitional care needs ([TCN] specified below), which we define as patient-centered risk factors that should be addressed to foster a safe and effective transition in care.⁷

One particularly important risk factor in patient self-management at transition points is health literacy, a patient’s ability to obtain, understand, and use basic health information and services. Low health literacy affects approximately 26% to 36% of adults in the United States.^8,9 Health literacy is associated with many factors that may affect successful navigation of care transitions, including doctor-patient communication,^10,11 understanding of the medication regimen,¹² and self-management.^13-15 Research has also demonstrated an association between low health literacy and poor outcomes after hospital discharge, including medication errors,¹⁶ 30-day hospital readmission,¹⁷ and mortality.¹⁸ Transitional care initiatives have begun to incorporate health literacy into patient risk assessments⁶ and provide specific attention to low health literacy in interventions to reduce adverse drug events and readmission.^4,19 Training programs for medical students and nurses advise teaching skills in health literacy as part of fostering effective transitions in care.^20,21

Although low health literacy is generally recognized as a barrier to patient education and self-management, little is known about whether patients with low health literacy are more likely to have other risk factors that could further increase their risk for poor transitions in care. A better understanding of associated risks would inform and improve patient care. We hypothesized that TCNs are more common among patients with low health literacy, as compared with those with adequate health literacy. We also aimed to describe the relationship between low health literacy and specific TCNs in order to guide clinical care and future interventions.

METHODS

Setting

The present study is a cross-sectional analysis of data from a quality improvement (QI) intervention that was performed at Vanderbilt University Medical Center, a tertiary care facility in Nashville, Tennessee. The QI intervention, My Health Team (MHT), was funded by the Centers for Medicare and Medicaid Services Innovation Award program. The overall MHT program included outpatient care coordination for chronic disease management as well as a transitional care program that was designed to reduce hospital readmission. The latter included an inpatient needs assessment (which provided data for the present analysis), inpatient intervention, and postdischarge phone follow-up. The MHT initiative was reviewed by the institutional review board (IRB), which deemed it a QI program and granted a waiver of informed consent. The present secondary data analysis was reviewed and approved by the IRB.

Sample

Patients were identified for inclusion in the MHT transitions of care program if the presenting problem for hospital admission was pneumonia, chronic obstructive pulmonary disease (COPD) exacerbation, or decompensated heart failure, as determined by the review of clinical documentation by nurse transition care coordinators (TCCs). Adults over the age of 18 years were eligible, though priority was given to patients aged 65 years or older. This study includes the first inpatient encounter between June 2013 and December 31, 2014, for patients having a completed needs assessment and documentation of health literacy data in the medical record.

 

Data Collection

TCN assessment was developed from published patient risk tools and the ITC framework.^2,6,22 The assessment has 10 domains composed of 49 individual items as follows: (1) caregiver support (caregiver support not sufficient for patient needs), (2) transportation (relies on public or others for transportation and misses medical care because of transportation), (3) health care utilization (no primary care physician, unplanned hospitalization in the last year, emergency department [ED] visit in the last 6 months, or home health services in the last 60 days), (4) high-risk medical comorbidities (malnutrition or body mass index <18.5, renal failure, chronic pain, diabetes, heart failure, COPD, or stroke), (5) medication management provider or caregiver concern (cannot provide medication list, >10 preadmission medications, high-risk medications [eg, insulin, warfarin], poor medication understanding, or adherence issue identified), (6) medical devices (vascular access, urinary catheter, wounds, or home supplemental oxygen), (7) functional status (weakness of extremities, limited extremity range of motion, difficulty with mobility, falls at home, or activities of daily living challenges), (8) mental health comorbidities (over the past month has felt down, depressed, or hopeless or over the past month has felt little interest or pleasure in doing things, high-risk alcohol use, or high-risk substance use), (9) communication (limited English proficiency or at risk for limited health literacy), and (10) financial resources (no health insurance, skips or rations medicines because of cost, misses medical care because of cost, or misses medical care because of job).

The 49 items of the TCN assessment were documented as being present or absent by nurse TCCs at the time patients were enrolled in the transitional care program, based on patient and family interview and chart review, and the items were later extracted for analysis. Patients were determined to have a domain-level need if they reported a need on any individual item within that domain, resulting in a binary score (any need present, absent) for each of the 10 TCN domains.

Health literacy was assessed by using the Brief Health Literacy Screen (BHLS), which is administered routinely by nurses at hospital intake and documented in the medical record, with completion rates of approximately 90%.²³ The BHLS is a 3-question subjective health literacy assessment (scoring range 3-15) that has been validated against longer objective measures²⁴ and shown to predict disease control and mortality.^18,25 To improve the stability of scores (for patients who completed the BHLS more than once because of repeat hospitalizations) and to reduce missing values, we calculated the patient’s mean BHLS score for assessments obtained between January 1, 2013, and December 31, 2014. Patients were then categorized as having inadequate health literacy (BHLS ≤ 9) or adequate health literacy (BHLS > 9).^18,25 Demographic information was extracted from patient records and included age, sex (male/female), marital status (married/without a partner), race (white/nonwhite), and years of education. Income level and primary language were not available for analysis.

Statistical Analysis

Patient characteristics and TCNs were summarized by using the frequency and percentages for categorical variables and the mean and standard deviation (SD) for continuous variables. We compared patient characteristics (age, sex, marital status, race, and education) between health literacy groups (inadequate vs adequate) by using χ² or analysis of variance as appropriate. We assessed Pearson correlations among the 10 TCN domains, and we examined differences in reported needs for each of 10 TCN domains by the level of health literacy by using the χ² test. Because the TCN domain of communication included low health literacy as one of its items, we excluded this domain from subsequent analyses. We then compared differences in the number of TCNs documented (scoring range 0-9) by using an independent samples Student t test.

Multivariate logistic regression models were then constructed to examine the independent association of inadequate health literacy with 8 TCN domains while controlling for age, sex, marital status, race, and education. Patients with incomplete demographic data were excluded from these models. Additionally, these analyses excluded 2 TCN domains: the communication domain for reasons noted above and the high-risk medical comorbidity domain because it ended up being positive in 98.4% of patients. Statistical significance was set at an alpha of 0.05. All analyses were performed by using SPSS Statistics for Mac, version 23.0 (IBM Corp., Armonk, New York)

RESULTS

A total of 403 unique patients received the needs assessment, and 384 (95.3%) patients had health literacy data available (Table 1). The number of patients with missing or unknown values were 3 for marital status, 8 for race, and 6 for education. The patients had an average age of 66.9 years (SD = 13.0 years). Among the sample, 209 (54%) were female, 172 (45%) were married, and 291 (75.8%) were white. The average years of education was 12.6 (SD = 2.9 years), and 113 (29%) had inadequate health literacy. Patients with inadequate health literacy completed fewer years of schooling (11.2 vs 13.2; P < 0.001) and were less likely to be married (37% vs 49%; P = 0.031). There was no significant difference in age, sex, or race by level of health literacy.

 

Patients overall had a mean of 4.6 (SD = 1.8) TCN domains with any need reported. The most common domains were high-risk comorbidity (98%), medication management (76%), and healthcare utilization (76%; Table 2). For most domains, the presence of needs was significantly correlated with the presence of needs in multiple other domains (Table 3). Patients with inadequate health literacy had needs in a greater number of TCN domains (mean = 5.29 vs 4.36; P < 0.001).

In unadjusted analysis, patients with inadequate health literacy were significantly more likely to have TCNs in 7 out of the 10 domains (Table 2). These concerns related to caregiver support, transportation, healthcare utilization, presence of a medical device, functional status, mental health comorbidities, and communication. The inadequate and adequate health literacy groups were similar in needs with respect to high-risk comorbidity and finance and borderline nonsignificant for medication management.

In multivariate analyses, 371 patients had complete demographic data and were thus included. After adjustment for age, sex, marital status, race, and education, inadequate health literacy remained significantly associated with reported needs in 2 transitional care domains: inadequate caregiver support (odds ratio [OR], 2.61; 95% confidence interval [CI], 1.37-5.00) and transportation barriers (OR, 1.69; 95% CI, 1.04-2.76; Figure). Other domains approached statistical significance: medical devices (OR, 1.56; 95% CI, 0.96-2.54), functional status (OR, 1.67; 95% CI, 1.00-2.74), and mental health comorbidities (OR, 1.60; 95% CI, 0.98-2.62).

Older age was independently associated with more needs related to medical devices (OR, 1.02; 95% CI, 1.00-1.04), functional status (OR, 1.03; 95% CI, 1.02-1.05), and fewer financial needs (OR, 0.93; 95% CI, 0.91-0.96). Being married or living with a partner was associated with fewer needs related to caregiver support (OR, 0.37; 95% CI, 0.19-0.75) and more device-related needs (OR, 1.60; 95% CI, 1.03-2.49). A higher level of education was associated with fewer transportation needs (OR, 0.89; 95% CI, 0.82-0.97).

DISCUSSION

A structured patient risk factor assessment derived from literature was used to record TCNs in preparation for hospital discharge. On average, patients had needs in about half of the TCN domains (4.6 of 9). The most common areas identified were related to the presence of high-risk comorbidities (98.4%), frequent or prior healthcare utilization (76.6%), medication management (76.3%), functional status (54.9%), and transportation (48.7%). Many of the TCNs were significantly correlated with one another. The prevalence of these needs highlights the importance of using a structured assessment to identify patient concerns so that they may be addressed through discharge planning and follow-up. In addition, using a standardized TCN instrument based on a framework for ITC promotes further research in understanding patient needs and in developing personalized interventions to address them.

As hypothesized, we found that TCNs were more common in patients with inadequate health literacy. After adjustment for demographic factors, inadequate health literacy was significantly associated with transportation barriers and inadequate caregiver support. Analyses also suggested a relationship with needs related to medical devices, functional status, and mental health comorbidities. A review of the literature substantiates a link between inadequate health literacy and these needs and also suggests solutions to address these barriers.

The association with inadequate caregiver support is concerning because there is often a high degree of reliance on caregivers at transitions in care.^3-5 Caregivers are routinely called upon to provide assistance with activities that may be difficult for patients with low health literacy, including medication adherence, provider communication, and self-care activities.^26,27 Our finding that patients with inadequate health literacy are more likely to have inadequate caregiver support indicates additional vulnerability. This may be because of the absence of a caregiver, or in many cases, the presence of a caregiver who is underprepared to assist with care. Prior research has shown that when caregivers are present, up to 33% have low health literacy, even when they are paid nonfamilial caregivers.^26,28 Other studies have noted the inadequacy of information and patient training for caregivers.^29,30 Transitional care programs to improve caregiver understanding have been developed³¹ and have been demonstrated to lower rehospitalization and ED visits.³²

Patients with inadequate health literacy were also more likely to have transportation barriers. Lack of transportation has been recorded as a factor in early hospital readmission in patients with chronic disease,³³ and it has been shown to have a negative effect on a variety of health outcomes.³⁴ A likely link between readmission and lack of transportation is poor follow-up care. Wheeler et al.³⁵ found that 59% of patients expected difficulty keeping postdischarge appointments because of transportation needs. Instead of expecting patients to navigate their own transportation, the Agency for Healthcare Research and Quality recommends identifying community resources for patients with low health literacy.³⁶

In this sample, inadequate health literacy also had near significant associations with TCNs in the use of medical devices, lower functional status, and mental health comorbidities. The use of a medical device, such as home oxygen, is a risk factor for readmission,³⁷ and early reports suggest that interventions, including education related to home oxygen use, can dramatically reduce these readmissions.³⁸ Lower functional capacity and faster functional decline are associated with inadequate health literacy,³⁹ which may have to do with the inability to appropriately utilize health resources.⁴⁰ If so, structured discharge planning could alleviate the known connection between functional impairment and hospital readmissions.⁴¹ A relationship between low health literacy and depression has been demonstrated repeatedly,⁴² with worsened symptoms in those with addiction.⁴³ As has been shown in other domains where health literacy is a factor, literacy-focused interventions provide greater benefits to these depressed patients.⁴⁴

The TCN assessment worked well overall, but certain domains proved less valuable and could be removed in the future. First, it was not useful to separately identify communication barriers, because doing so did not add to information beyond the measurement of health literacy. Second, high-risk comorbidities were ubiquitous within the sample and therefore unhelpful for group comparisons. In hindsight, this is unsurprising because the sample was comprised primarily of elderly patients admitted to medical services. Still, in a younger population or a surgical setting, identifying patients with high-risk medical comorbidities may be more useful.

We acknowledge several limitations of this study. First, the study was performed at a single center, and the TCN assessments were conducted by a small number of registered nurses who received training. Therefore, the results may not generalize to the profile of patient needs at other settings, and the instrument may perform differently when scaled across an organization. Second, the needs assessment was developed for this QI initiative and did not undergo formal validation, although it was developed from published frameworks and similar assessments. Third, for the measure of health literacy, we relied on data collected by nurses as part of their normal workflow. As is often the case with data collected during routine care, the scores are imperfect,⁴⁵ but they have proven to be a valuable and valid indicator of health literacy in our previous research.^18,24,25,46 Fourth, we chose to declare a domain as positive if any item in that domain was positive and to perform a domain-level analysis (for greater clarity). We did not take into account the variable number of items within each domain or attempt to grade their severity, as this would be a subjective exercise and impractical in the discharge planning process. Finally, we were unable to address associations among socioeconomic status,⁴⁷ primary language,⁴⁸ and health literacy, because relevant data were not available for this analysis.

 

CONCLUSION

In this sample of hospitalized patients who were administered a structured needs assessment, patients commonly had needs that placed them at a higher risk of adverse outcomes, such as hospital readmission. Patients with low health literacy had more TCNs that extended beyond the areas that we normally associate with low health literacy, namely patient education and self-management. Healthcare professionals should be aware of the greater likelihood of transportation barriers and inadequate caregiver support among patients with low health literacy. Screening for health literacy and TCN at admission or as part of the discharge planning process will elevate such risks, better positioning clinicians and hospitals to address them as a part of the efforts to ensure a quality transition of care.

Disclosure 

This work was funded by the Centers for Medicare and Medicaid Services (1C1CMS330979) and in part by the National Center for Advancing Translational Sciences (2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies, which did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication.

Dr. Dittus reports personal fees as a board member of the Robert Wood Johnson Foundation Medical Faculty Scholars Program National Advisory Committee; consultancy fees from the University of Virginia, Indiana University, University of Michigan, Northwestern University, Montana State University, and Purdue University; has grants/grants pending from NIH (research grants), PCORI (research grant), CME (innovation award), VA (training grant); payment for lectures including service on speakers bureaus from Corporate Parity (conference organizer) for the Global Hospital Management & Innovation Summit; and other from Medical Decision Making, Inc. (passive owner); all outside the submitted work. Dr. Kripalani has grants from NIH (research grant), PCORI (research grant), and CMS (QI grant); outside the submitted work. All other authors have nothing to disclose.

A special concern since the institution of hospital readmission penalties¹ is the transitions in care of a patient from one care setting to another, often at hospital discharge. Burke et al.² proposed a framework for an ideal transition in care (ITC) to study and improve transitions from the hospital to home. The features in the ITC were identified based upon their inclusion in the interventions that improved discharge outcomes.^3-5 Inspired by the ITC and other patient risk tools,⁶ we identified 10 domains of transitional care needs ([TCN] specified below), which we define as patient-centered risk factors that should be addressed to foster a safe and effective transition in care.⁷

One particularly important risk factor in patient self-management at transition points is health literacy, a patient’s ability to obtain, understand, and use basic health information and services. Low health literacy affects approximately 26% to 36% of adults in the United States.^8,9 Health literacy is associated with many factors that may affect successful navigation of care transitions, including doctor-patient communication,^10,11 understanding of the medication regimen,¹² and self-management.^13-15 Research has also demonstrated an association between low health literacy and poor outcomes after hospital discharge, including medication errors,¹⁶ 30-day hospital readmission,¹⁷ and mortality.¹⁸ Transitional care initiatives have begun to incorporate health literacy into patient risk assessments⁶ and provide specific attention to low health literacy in interventions to reduce adverse drug events and readmission.^4,19 Training programs for medical students and nurses advise teaching skills in health literacy as part of fostering effective transitions in care.^20,21

Although low health literacy is generally recognized as a barrier to patient education and self-management, little is known about whether patients with low health literacy are more likely to have other risk factors that could further increase their risk for poor transitions in care. A better understanding of associated risks would inform and improve patient care. We hypothesized that TCNs are more common among patients with low health literacy, as compared with those with adequate health literacy. We also aimed to describe the relationship between low health literacy and specific TCNs in order to guide clinical care and future interventions.

METHODS

Setting

The present study is a cross-sectional analysis of data from a quality improvement (QI) intervention that was performed at Vanderbilt University Medical Center, a tertiary care facility in Nashville, Tennessee. The QI intervention, My Health Team (MHT), was funded by the Centers for Medicare and Medicaid Services Innovation Award program. The overall MHT program included outpatient care coordination for chronic disease management as well as a transitional care program that was designed to reduce hospital readmission. The latter included an inpatient needs assessment (which provided data for the present analysis), inpatient intervention, and postdischarge phone follow-up. The MHT initiative was reviewed by the institutional review board (IRB), which deemed it a QI program and granted a waiver of informed consent. The present secondary data analysis was reviewed and approved by the IRB.

Sample

Patients were identified for inclusion in the MHT transitions of care program if the presenting problem for hospital admission was pneumonia, chronic obstructive pulmonary disease (COPD) exacerbation, or decompensated heart failure, as determined by the review of clinical documentation by nurse transition care coordinators (TCCs). Adults over the age of 18 years were eligible, though priority was given to patients aged 65 years or older. This study includes the first inpatient encounter between June 2013 and December 31, 2014, for patients having a completed needs assessment and documentation of health literacy data in the medical record.

 

Data Collection

TCN assessment was developed from published patient risk tools and the ITC framework.^2,6,22 The assessment has 10 domains composed of 49 individual items as follows: (1) caregiver support (caregiver support not sufficient for patient needs), (2) transportation (relies on public or others for transportation and misses medical care because of transportation), (3) health care utilization (no primary care physician, unplanned hospitalization in the last year, emergency department [ED] visit in the last 6 months, or home health services in the last 60 days), (4) high-risk medical comorbidities (malnutrition or body mass index <18.5, renal failure, chronic pain, diabetes, heart failure, COPD, or stroke), (5) medication management provider or caregiver concern (cannot provide medication list, >10 preadmission medications, high-risk medications [eg, insulin, warfarin], poor medication understanding, or adherence issue identified), (6) medical devices (vascular access, urinary catheter, wounds, or home supplemental oxygen), (7) functional status (weakness of extremities, limited extremity range of motion, difficulty with mobility, falls at home, or activities of daily living challenges), (8) mental health comorbidities (over the past month has felt down, depressed, or hopeless or over the past month has felt little interest or pleasure in doing things, high-risk alcohol use, or high-risk substance use), (9) communication (limited English proficiency or at risk for limited health literacy), and (10) financial resources (no health insurance, skips or rations medicines because of cost, misses medical care because of cost, or misses medical care because of job).

The 49 items of the TCN assessment were documented as being present or absent by nurse TCCs at the time patients were enrolled in the transitional care program, based on patient and family interview and chart review, and the items were later extracted for analysis. Patients were determined to have a domain-level need if they reported a need on any individual item within that domain, resulting in a binary score (any need present, absent) for each of the 10 TCN domains.

Health literacy was assessed by using the Brief Health Literacy Screen (BHLS), which is administered routinely by nurses at hospital intake and documented in the medical record, with completion rates of approximately 90%.²³ The BHLS is a 3-question subjective health literacy assessment (scoring range 3-15) that has been validated against longer objective measures²⁴ and shown to predict disease control and mortality.^18,25 To improve the stability of scores (for patients who completed the BHLS more than once because of repeat hospitalizations) and to reduce missing values, we calculated the patient’s mean BHLS score for assessments obtained between January 1, 2013, and December 31, 2014. Patients were then categorized as having inadequate health literacy (BHLS ≤ 9) or adequate health literacy (BHLS > 9).^18,25 Demographic information was extracted from patient records and included age, sex (male/female), marital status (married/without a partner), race (white/nonwhite), and years of education. Income level and primary language were not available for analysis.

Statistical Analysis

Patient characteristics and TCNs were summarized by using the frequency and percentages for categorical variables and the mean and standard deviation (SD) for continuous variables. We compared patient characteristics (age, sex, marital status, race, and education) between health literacy groups (inadequate vs adequate) by using χ² or analysis of variance as appropriate. We assessed Pearson correlations among the 10 TCN domains, and we examined differences in reported needs for each of 10 TCN domains by the level of health literacy by using the χ² test. Because the TCN domain of communication included low health literacy as one of its items, we excluded this domain from subsequent analyses. We then compared differences in the number of TCNs documented (scoring range 0-9) by using an independent samples Student t test.

Multivariate logistic regression models were then constructed to examine the independent association of inadequate health literacy with 8 TCN domains while controlling for age, sex, marital status, race, and education. Patients with incomplete demographic data were excluded from these models. Additionally, these analyses excluded 2 TCN domains: the communication domain for reasons noted above and the high-risk medical comorbidity domain because it ended up being positive in 98.4% of patients. Statistical significance was set at an alpha of 0.05. All analyses were performed by using SPSS Statistics for Mac, version 23.0 (IBM Corp., Armonk, New York)

RESULTS

A total of 403 unique patients received the needs assessment, and 384 (95.3%) patients had health literacy data available (Table 1). The number of patients with missing or unknown values were 3 for marital status, 8 for race, and 6 for education. The patients had an average age of 66.9 years (SD = 13.0 years). Among the sample, 209 (54%) were female, 172 (45%) were married, and 291 (75.8%) were white. The average years of education was 12.6 (SD = 2.9 years), and 113 (29%) had inadequate health literacy. Patients with inadequate health literacy completed fewer years of schooling (11.2 vs 13.2; P < 0.001) and were less likely to be married (37% vs 49%; P = 0.031). There was no significant difference in age, sex, or race by level of health literacy.

 

Patients overall had a mean of 4.6 (SD = 1.8) TCN domains with any need reported. The most common domains were high-risk comorbidity (98%), medication management (76%), and healthcare utilization (76%; Table 2). For most domains, the presence of needs was significantly correlated with the presence of needs in multiple other domains (Table 3). Patients with inadequate health literacy had needs in a greater number of TCN domains (mean = 5.29 vs 4.36; P < 0.001).

In unadjusted analysis, patients with inadequate health literacy were significantly more likely to have TCNs in 7 out of the 10 domains (Table 2). These concerns related to caregiver support, transportation, healthcare utilization, presence of a medical device, functional status, mental health comorbidities, and communication. The inadequate and adequate health literacy groups were similar in needs with respect to high-risk comorbidity and finance and borderline nonsignificant for medication management.

In multivariate analyses, 371 patients had complete demographic data and were thus included. After adjustment for age, sex, marital status, race, and education, inadequate health literacy remained significantly associated with reported needs in 2 transitional care domains: inadequate caregiver support (odds ratio [OR], 2.61; 95% confidence interval [CI], 1.37-5.00) and transportation barriers (OR, 1.69; 95% CI, 1.04-2.76; Figure). Other domains approached statistical significance: medical devices (OR, 1.56; 95% CI, 0.96-2.54), functional status (OR, 1.67; 95% CI, 1.00-2.74), and mental health comorbidities (OR, 1.60; 95% CI, 0.98-2.62).

Older age was independently associated with more needs related to medical devices (OR, 1.02; 95% CI, 1.00-1.04), functional status (OR, 1.03; 95% CI, 1.02-1.05), and fewer financial needs (OR, 0.93; 95% CI, 0.91-0.96). Being married or living with a partner was associated with fewer needs related to caregiver support (OR, 0.37; 95% CI, 0.19-0.75) and more device-related needs (OR, 1.60; 95% CI, 1.03-2.49). A higher level of education was associated with fewer transportation needs (OR, 0.89; 95% CI, 0.82-0.97).

DISCUSSION

A structured patient risk factor assessment derived from literature was used to record TCNs in preparation for hospital discharge. On average, patients had needs in about half of the TCN domains (4.6 of 9). The most common areas identified were related to the presence of high-risk comorbidities (98.4%), frequent or prior healthcare utilization (76.6%), medication management (76.3%), functional status (54.9%), and transportation (48.7%). Many of the TCNs were significantly correlated with one another. The prevalence of these needs highlights the importance of using a structured assessment to identify patient concerns so that they may be addressed through discharge planning and follow-up. In addition, using a standardized TCN instrument based on a framework for ITC promotes further research in understanding patient needs and in developing personalized interventions to address them.

As hypothesized, we found that TCNs were more common in patients with inadequate health literacy. After adjustment for demographic factors, inadequate health literacy was significantly associated with transportation barriers and inadequate caregiver support. Analyses also suggested a relationship with needs related to medical devices, functional status, and mental health comorbidities. A review of the literature substantiates a link between inadequate health literacy and these needs and also suggests solutions to address these barriers.

The association with inadequate caregiver support is concerning because there is often a high degree of reliance on caregivers at transitions in care.^3-5 Caregivers are routinely called upon to provide assistance with activities that may be difficult for patients with low health literacy, including medication adherence, provider communication, and self-care activities.^26,27 Our finding that patients with inadequate health literacy are more likely to have inadequate caregiver support indicates additional vulnerability. This may be because of the absence of a caregiver, or in many cases, the presence of a caregiver who is underprepared to assist with care. Prior research has shown that when caregivers are present, up to 33% have low health literacy, even when they are paid nonfamilial caregivers.^26,28 Other studies have noted the inadequacy of information and patient training for caregivers.^29,30 Transitional care programs to improve caregiver understanding have been developed³¹ and have been demonstrated to lower rehospitalization and ED visits.³²

Patients with inadequate health literacy were also more likely to have transportation barriers. Lack of transportation has been recorded as a factor in early hospital readmission in patients with chronic disease,³³ and it has been shown to have a negative effect on a variety of health outcomes.³⁴ A likely link between readmission and lack of transportation is poor follow-up care. Wheeler et al.³⁵ found that 59% of patients expected difficulty keeping postdischarge appointments because of transportation needs. Instead of expecting patients to navigate their own transportation, the Agency for Healthcare Research and Quality recommends identifying community resources for patients with low health literacy.³⁶

In this sample, inadequate health literacy also had near significant associations with TCNs in the use of medical devices, lower functional status, and mental health comorbidities. The use of a medical device, such as home oxygen, is a risk factor for readmission,³⁷ and early reports suggest that interventions, including education related to home oxygen use, can dramatically reduce these readmissions.³⁸ Lower functional capacity and faster functional decline are associated with inadequate health literacy,³⁹ which may have to do with the inability to appropriately utilize health resources.⁴⁰ If so, structured discharge planning could alleviate the known connection between functional impairment and hospital readmissions.⁴¹ A relationship between low health literacy and depression has been demonstrated repeatedly,⁴² with worsened symptoms in those with addiction.⁴³ As has been shown in other domains where health literacy is a factor, literacy-focused interventions provide greater benefits to these depressed patients.⁴⁴

The TCN assessment worked well overall, but certain domains proved less valuable and could be removed in the future. First, it was not useful to separately identify communication barriers, because doing so did not add to information beyond the measurement of health literacy. Second, high-risk comorbidities were ubiquitous within the sample and therefore unhelpful for group comparisons. In hindsight, this is unsurprising because the sample was comprised primarily of elderly patients admitted to medical services. Still, in a younger population or a surgical setting, identifying patients with high-risk medical comorbidities may be more useful.

We acknowledge several limitations of this study. First, the study was performed at a single center, and the TCN assessments were conducted by a small number of registered nurses who received training. Therefore, the results may not generalize to the profile of patient needs at other settings, and the instrument may perform differently when scaled across an organization. Second, the needs assessment was developed for this QI initiative and did not undergo formal validation, although it was developed from published frameworks and similar assessments. Third, for the measure of health literacy, we relied on data collected by nurses as part of their normal workflow. As is often the case with data collected during routine care, the scores are imperfect,⁴⁵ but they have proven to be a valuable and valid indicator of health literacy in our previous research.^18,24,25,46 Fourth, we chose to declare a domain as positive if any item in that domain was positive and to perform a domain-level analysis (for greater clarity). We did not take into account the variable number of items within each domain or attempt to grade their severity, as this would be a subjective exercise and impractical in the discharge planning process. Finally, we were unable to address associations among socioeconomic status,⁴⁷ primary language,⁴⁸ and health literacy, because relevant data were not available for this analysis.

 

CONCLUSION

In this sample of hospitalized patients who were administered a structured needs assessment, patients commonly had needs that placed them at a higher risk of adverse outcomes, such as hospital readmission. Patients with low health literacy had more TCNs that extended beyond the areas that we normally associate with low health literacy, namely patient education and self-management. Healthcare professionals should be aware of the greater likelihood of transportation barriers and inadequate caregiver support among patients with low health literacy. Screening for health literacy and TCN at admission or as part of the discharge planning process will elevate such risks, better positioning clinicians and hospitals to address them as a part of the efforts to ensure a quality transition of care.

Disclosure 

This work was funded by the Centers for Medicare and Medicaid Services (1C1CMS330979) and in part by the National Center for Advancing Translational Sciences (2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies, which did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication.

Dr. Dittus reports personal fees as a board member of the Robert Wood Johnson Foundation Medical Faculty Scholars Program National Advisory Committee; consultancy fees from the University of Virginia, Indiana University, University of Michigan, Northwestern University, Montana State University, and Purdue University; has grants/grants pending from NIH (research grants), PCORI (research grant), CME (innovation award), VA (training grant); payment for lectures including service on speakers bureaus from Corporate Parity (conference organizer) for the Global Hospital Management & Innovation Summit; and other from Medical Decision Making, Inc. (passive owner); all outside the submitted work. Dr. Kripalani has grants from NIH (research grant), PCORI (research grant), and CMS (QI grant); outside the submitted work. All other authors have nothing to disclose.