Hospitalization is a critical time for older patients with cognitive impairment. Past research has found that hospitalized older adults with cognitive dysfunction have more rapid cognitive decline, increased morbidity and mortality, and higher costs of healthcare utilization.[1, 2, 3] Those with preexisting cognitive dysfunction, such as dementia, are most susceptible to the negative impacts of hospitalization.[4, 5, 6, 7, 8] Identification of cognitive deficits upon admission is important for risk stratification of patients and prevention of negative hospital health events.

Frontline healthcare providers are underequipped to detect acute cognitive dysfunction.[9, 10] Current practice and research for the detection of cognitive dysfunction in the acute care setting utilizes instruments that require training[11] and are relatively lengthy (>5 minutes).[12] Although these cognitive screening tests are accurate and reliable, the time requirement is not feasible in a fast‐paced clinical setting. A possible alternative is the use of ultra‐brief cognitive screening instruments (<1 minute) that have the potential to identify those individuals requiring additional evaluation and follow‐up. These brief instruments are composed of screening tools that emphasize core features of acute cognitive dysfunction such as level of arousal or attention.[13, 14, 15, 16] Arousal, the ability to respond to or interact with the environment,[15] is an important component of cognition because it is generally preserved in chronic cognitive disorders (eg, dementia). Thus, an alteration in arousal may be a harbinger of more acute impairment[17] in need of evaluation, and in these lowered states of arousal it may be difficult to test for attention.[18] Attention is a broadly defined cognitive domain indicating focus.[19] Older adults, regardless of preexisting cognitive dysfunction, warrant additional cognitive testing if levels of arousal or attention are altered[20, 21] due to the significant relationship to delirium, which is associated with adverse events in this population. Recent research has demonstrated that these brief cognitive screening instruments provide information about the risk for delirium and are a strong test for clinical characteristics of delirium.[16, 21]

The purpose of this analysis was to demonstrate the clinical outcomes of poor performance on ultrabrief assessments arousal and attention by frontline staff using a quality improvement database. Specific objectives include determining (1) the association of poor performance on brief cognitive assessments and hospital outcomes and (2) the inter‐relationship between alterations in the levels of arousal and attention on in‐hospital and discharge outcomes.

METHODS

RESULTS

DISCUSSION

Impaired performance on a one‐time assessment of arousal or attention during hospitalization demonstrated a relationship with in‐hospital and discharge outcomes. Relative to normal levels of arousal and attention, alterations in attention, level of arousal, or both were associated with progressively negative consequences. Combined with the prognostic value, the administration of ultra‐brief cognitive screening measures may have value in the identification of patients who would benefit from additional screening, supporting prior work in this area.[23] The brevity of the assessments enhances clinical utility and implementation potential.

Cognitive function during hospitalization has been associated with many negative outcomes including delirium, falls, pressure ulcers, and functional decline.[3, 33, 34, 35, 36, 37] The findings of this analysis are consistent with previous studies and provide important clinical implications. First, prior work in cognitive screening has focused on more time‐consuming instruments.[12] By focusing on brief instruments, particularly those under 1 minute that do not require paper or props, a user‐friendly tool that is associated with health outcomes is provided.

In addition, this analysis demonstrates that each assessment, when used individually, has some prognostic significance associated with the identification of delirium or other types of cognitive impairment. When used alone, abnormal scores on the mRASS or MOYB may be indicative of individuals requiring further cognitive assessment, supporting previous research.[16, 23] Individuals with abnormal scores on both the mRASS and MOYB identify a high‐risk group in need of further clinical assessment for delirium (Figure 1). Neither of these assessments are meant to be used as the only means to diagnosis delirium, but together they identify key clinical characteristics of delirium (arousal and attention).[16, 18, 21] Considering the significant negative consequences associated with delirium, it is not surprising that tools identifying core features of delirium, such as those presented here, would also be associated with in‐hospital and discharge outcomes.

Figure 1

The quality improvement design of this project allowed the recording of outcomes in those who were unable or refused to complete the screening. This may be a potentially high‐risk group who would otherwise go unnoticed. A recent editorial from the American and European Delirium Societies highlights that individuals who are unable or refuse to complete testing due to impaired arousal are neglected in the most recent American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition definition of delirium.[18] Further work to identify and intervene on behalf of individuals who are unable to complete testing will aid in understanding arousal and its relationship to delirium and other disorders.

This analysis provides additional insight in the selection of measures of arousal and attention. Level of arousal is a complex concept that involves components of awareness and alertness, including external stimuli and self‐awareness.[38, 39, 40] As an ultra‐brief measure of arousal level, the mRASS incorporates both external stimuli (asking an open‐ended question) and self‐awareness (describing current state) to determine basic cognitive function. Attention can be defined as the selection of stimuli for further cognitive processing.[40] Attention is an umbrella term referring to many cognitive processes, ranging from sustained attention and working memory to executive function such as set shifting and multitasking. Ultra‐brief measures of attention, such as MOYB, are basic tasks of sustained attention with components of working memory.[19] An alteration in attention may be indicative of a more significant global change in cognition[41] beyond basic cognitive function assessed by administration of the mRASS, such as delirium.[42] The relationship between level of arousal and attention is complex, and arguments have been made that one has to have a certain level of arousal to attend to a stimuli, whereas others have found that one has to have a certain level of attention.[18, 39, 40] Administration of both the mRASS and MOYB is a useful bedside tool for clinicians to examine both basic cognitive function and more complex tasks of attention.

The quality improvement nature of this work has limitations and strengths that deserve mention. The significant strength of this work is the robust sample size. Also, trained staff not involved in the direct clinical care of patients administered the cognitive screens, suggesting that nonclinically trained personnel could be utilized for risk assessment. The major limitation is the restricted amount of covariate data that were collected. Data for this project were collected to operationalize and demonstrate the impact and business case of a delirium risk modification program,[17] limiting the ability to perform adjustment for other covariates such as comorbidity and reason for admission. Also, due to the nature of this project, a diagnosis of delirium was not determined. A limitation of excluding in‐hospital deaths from the cost analysis was that some individuals at high risk died early, thus costing less overall. Generalizability is limited by an over‐representation of males within a single setting. Further use and understanding of mRASS and MOYB in other population is warranted and welcomed. Use of MOYB is also a limitation considering that scores are not standardized across patients or settings.[26] Data regarding administration time of either of these tools were not collected; therefore, determining that these are ultra‐brief assessments (<1 minute) is based on estimates. As such, these measures should not be the sole source of information for clinical evaluation and diagnosis.

CONCLUSION

This work found that impaired performance on brief cognitive assessments of arousal and attention in hospitalized patients were associated with restraint use, in‐hospital mortality, longer LOS, less discharge home, and hospital costs. Routine screening of older patients with brief, user‐friendly cognitive assessments upon admission can identify those who would benefit from additional assessment and intervention to alleviate individual and economic burdens.

Hospitalization is a critical time for older patients with cognitive impairment. Past research has found that hospitalized older adults with cognitive dysfunction have more rapid cognitive decline, increased morbidity and mortality, and higher costs of healthcare utilization.[1, 2, 3] Those with preexisting cognitive dysfunction, such as dementia, are most susceptible to the negative impacts of hospitalization.[4, 5, 6, 7, 8] Identification of cognitive deficits upon admission is important for risk stratification of patients and prevention of negative hospital health events.

Frontline healthcare providers are underequipped to detect acute cognitive dysfunction.[9, 10] Current practice and research for the detection of cognitive dysfunction in the acute care setting utilizes instruments that require training[11] and are relatively lengthy (>5 minutes).[12] Although these cognitive screening tests are accurate and reliable, the time requirement is not feasible in a fast‐paced clinical setting. A possible alternative is the use of ultra‐brief cognitive screening instruments (<1 minute) that have the potential to identify those individuals requiring additional evaluation and follow‐up. These brief instruments are composed of screening tools that emphasize core features of acute cognitive dysfunction such as level of arousal or attention.[13, 14, 15, 16] Arousal, the ability to respond to or interact with the environment,[15] is an important component of cognition because it is generally preserved in chronic cognitive disorders (eg, dementia). Thus, an alteration in arousal may be a harbinger of more acute impairment[17] in need of evaluation, and in these lowered states of arousal it may be difficult to test for attention.[18] Attention is a broadly defined cognitive domain indicating focus.[19] Older adults, regardless of preexisting cognitive dysfunction, warrant additional cognitive testing if levels of arousal or attention are altered[20, 21] due to the significant relationship to delirium, which is associated with adverse events in this population. Recent research has demonstrated that these brief cognitive screening instruments provide information about the risk for delirium and are a strong test for clinical characteristics of delirium.[16, 21]

The purpose of this analysis was to demonstrate the clinical outcomes of poor performance on ultrabrief assessments arousal and attention by frontline staff using a quality improvement database. Specific objectives include determining (1) the association of poor performance on brief cognitive assessments and hospital outcomes and (2) the inter‐relationship between alterations in the levels of arousal and attention on in‐hospital and discharge outcomes.

METHODS

RESULTS

DISCUSSION

Impaired performance on a one‐time assessment of arousal or attention during hospitalization demonstrated a relationship with in‐hospital and discharge outcomes. Relative to normal levels of arousal and attention, alterations in attention, level of arousal, or both were associated with progressively negative consequences. Combined with the prognostic value, the administration of ultra‐brief cognitive screening measures may have value in the identification of patients who would benefit from additional screening, supporting prior work in this area.[23] The brevity of the assessments enhances clinical utility and implementation potential.

Cognitive function during hospitalization has been associated with many negative outcomes including delirium, falls, pressure ulcers, and functional decline.[3, 33, 34, 35, 36, 37] The findings of this analysis are consistent with previous studies and provide important clinical implications. First, prior work in cognitive screening has focused on more time‐consuming instruments.[12] By focusing on brief instruments, particularly those under 1 minute that do not require paper or props, a user‐friendly tool that is associated with health outcomes is provided.

In addition, this analysis demonstrates that each assessment, when used individually, has some prognostic significance associated with the identification of delirium or other types of cognitive impairment. When used alone, abnormal scores on the mRASS or MOYB may be indicative of individuals requiring further cognitive assessment, supporting previous research.[16, 23] Individuals with abnormal scores on both the mRASS and MOYB identify a high‐risk group in need of further clinical assessment for delirium (Figure 1). Neither of these assessments are meant to be used as the only means to diagnosis delirium, but together they identify key clinical characteristics of delirium (arousal and attention).[16, 18, 21] Considering the significant negative consequences associated with delirium, it is not surprising that tools identifying core features of delirium, such as those presented here, would also be associated with in‐hospital and discharge outcomes.

Figure 1

The quality improvement design of this project allowed the recording of outcomes in those who were unable or refused to complete the screening. This may be a potentially high‐risk group who would otherwise go unnoticed. A recent editorial from the American and European Delirium Societies highlights that individuals who are unable or refuse to complete testing due to impaired arousal are neglected in the most recent American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition definition of delirium.[18] Further work to identify and intervene on behalf of individuals who are unable to complete testing will aid in understanding arousal and its relationship to delirium and other disorders.

This analysis provides additional insight in the selection of measures of arousal and attention. Level of arousal is a complex concept that involves components of awareness and alertness, including external stimuli and self‐awareness.[38, 39, 40] As an ultra‐brief measure of arousal level, the mRASS incorporates both external stimuli (asking an open‐ended question) and self‐awareness (describing current state) to determine basic cognitive function. Attention can be defined as the selection of stimuli for further cognitive processing.[40] Attention is an umbrella term referring to many cognitive processes, ranging from sustained attention and working memory to executive function such as set shifting and multitasking. Ultra‐brief measures of attention, such as MOYB, are basic tasks of sustained attention with components of working memory.[19] An alteration in attention may be indicative of a more significant global change in cognition[41] beyond basic cognitive function assessed by administration of the mRASS, such as delirium.[42] The relationship between level of arousal and attention is complex, and arguments have been made that one has to have a certain level of arousal to attend to a stimuli, whereas others have found that one has to have a certain level of attention.[18, 39, 40] Administration of both the mRASS and MOYB is a useful bedside tool for clinicians to examine both basic cognitive function and more complex tasks of attention.

The quality improvement nature of this work has limitations and strengths that deserve mention. The significant strength of this work is the robust sample size. Also, trained staff not involved in the direct clinical care of patients administered the cognitive screens, suggesting that nonclinically trained personnel could be utilized for risk assessment. The major limitation is the restricted amount of covariate data that were collected. Data for this project were collected to operationalize and demonstrate the impact and business case of a delirium risk modification program,[17] limiting the ability to perform adjustment for other covariates such as comorbidity and reason for admission. Also, due to the nature of this project, a diagnosis of delirium was not determined. A limitation of excluding in‐hospital deaths from the cost analysis was that some individuals at high risk died early, thus costing less overall. Generalizability is limited by an over‐representation of males within a single setting. Further use and understanding of mRASS and MOYB in other population is warranted and welcomed. Use of MOYB is also a limitation considering that scores are not standardized across patients or settings.[26] Data regarding administration time of either of these tools were not collected; therefore, determining that these are ultra‐brief assessments (<1 minute) is based on estimates. As such, these measures should not be the sole source of information for clinical evaluation and diagnosis.

CONCLUSION

This work found that impaired performance on brief cognitive assessments of arousal and attention in hospitalized patients were associated with restraint use, in‐hospital mortality, longer LOS, less discharge home, and hospital costs. Routine screening of older patients with brief, user‐friendly cognitive assessments upon admission can identify those who would benefit from additional assessment and intervention to alleviate individual and economic burdens.

Hospitalization is a critical time for older patients with cognitive impairment. Past research has found that hospitalized older adults with cognitive dysfunction have more rapid cognitive decline, increased morbidity and mortality, and higher costs of healthcare utilization.[1, 2, 3] Those with preexisting cognitive dysfunction, such as dementia, are most susceptible to the negative impacts of hospitalization.[4, 5, 6, 7, 8] Identification of cognitive deficits upon admission is important for risk stratification of patients and prevention of negative hospital health events.

Frontline healthcare providers are underequipped to detect acute cognitive dysfunction.[9, 10] Current practice and research for the detection of cognitive dysfunction in the acute care setting utilizes instruments that require training[11] and are relatively lengthy (>5 minutes).[12] Although these cognitive screening tests are accurate and reliable, the time requirement is not feasible in a fast‐paced clinical setting. A possible alternative is the use of ultra‐brief cognitive screening instruments (<1 minute) that have the potential to identify those individuals requiring additional evaluation and follow‐up. These brief instruments are composed of screening tools that emphasize core features of acute cognitive dysfunction such as level of arousal or attention.[13, 14, 15, 16] Arousal, the ability to respond to or interact with the environment,[15] is an important component of cognition because it is generally preserved in chronic cognitive disorders (eg, dementia). Thus, an alteration in arousal may be a harbinger of more acute impairment[17] in need of evaluation, and in these lowered states of arousal it may be difficult to test for attention.[18] Attention is a broadly defined cognitive domain indicating focus.[19] Older adults, regardless of preexisting cognitive dysfunction, warrant additional cognitive testing if levels of arousal or attention are altered[20, 21] due to the significant relationship to delirium, which is associated with adverse events in this population. Recent research has demonstrated that these brief cognitive screening instruments provide information about the risk for delirium and are a strong test for clinical characteristics of delirium.[16, 21]

The purpose of this analysis was to demonstrate the clinical outcomes of poor performance on ultrabrief assessments arousal and attention by frontline staff using a quality improvement database. Specific objectives include determining (1) the association of poor performance on brief cognitive assessments and hospital outcomes and (2) the inter‐relationship between alterations in the levels of arousal and attention on in‐hospital and discharge outcomes.

METHODS

RESULTS

DISCUSSION

Impaired performance on a one‐time assessment of arousal or attention during hospitalization demonstrated a relationship with in‐hospital and discharge outcomes. Relative to normal levels of arousal and attention, alterations in attention, level of arousal, or both were associated with progressively negative consequences. Combined with the prognostic value, the administration of ultra‐brief cognitive screening measures may have value in the identification of patients who would benefit from additional screening, supporting prior work in this area.[23] The brevity of the assessments enhances clinical utility and implementation potential.

Cognitive function during hospitalization has been associated with many negative outcomes including delirium, falls, pressure ulcers, and functional decline.[3, 33, 34, 35, 36, 37] The findings of this analysis are consistent with previous studies and provide important clinical implications. First, prior work in cognitive screening has focused on more time‐consuming instruments.[12] By focusing on brief instruments, particularly those under 1 minute that do not require paper or props, a user‐friendly tool that is associated with health outcomes is provided.

In addition, this analysis demonstrates that each assessment, when used individually, has some prognostic significance associated with the identification of delirium or other types of cognitive impairment. When used alone, abnormal scores on the mRASS or MOYB may be indicative of individuals requiring further cognitive assessment, supporting previous research.[16, 23] Individuals with abnormal scores on both the mRASS and MOYB identify a high‐risk group in need of further clinical assessment for delirium (Figure 1). Neither of these assessments are meant to be used as the only means to diagnosis delirium, but together they identify key clinical characteristics of delirium (arousal and attention).[16, 18, 21] Considering the significant negative consequences associated with delirium, it is not surprising that tools identifying core features of delirium, such as those presented here, would also be associated with in‐hospital and discharge outcomes.

Figure 1

The quality improvement design of this project allowed the recording of outcomes in those who were unable or refused to complete the screening. This may be a potentially high‐risk group who would otherwise go unnoticed. A recent editorial from the American and European Delirium Societies highlights that individuals who are unable or refuse to complete testing due to impaired arousal are neglected in the most recent American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition definition of delirium.[18] Further work to identify and intervene on behalf of individuals who are unable to complete testing will aid in understanding arousal and its relationship to delirium and other disorders.

This analysis provides additional insight in the selection of measures of arousal and attention. Level of arousal is a complex concept that involves components of awareness and alertness, including external stimuli and self‐awareness.[38, 39, 40] As an ultra‐brief measure of arousal level, the mRASS incorporates both external stimuli (asking an open‐ended question) and self‐awareness (describing current state) to determine basic cognitive function. Attention can be defined as the selection of stimuli for further cognitive processing.[40] Attention is an umbrella term referring to many cognitive processes, ranging from sustained attention and working memory to executive function such as set shifting and multitasking. Ultra‐brief measures of attention, such as MOYB, are basic tasks of sustained attention with components of working memory.[19] An alteration in attention may be indicative of a more significant global change in cognition[41] beyond basic cognitive function assessed by administration of the mRASS, such as delirium.[42] The relationship between level of arousal and attention is complex, and arguments have been made that one has to have a certain level of arousal to attend to a stimuli, whereas others have found that one has to have a certain level of attention.[18, 39, 40] Administration of both the mRASS and MOYB is a useful bedside tool for clinicians to examine both basic cognitive function and more complex tasks of attention.

The quality improvement nature of this work has limitations and strengths that deserve mention. The significant strength of this work is the robust sample size. Also, trained staff not involved in the direct clinical care of patients administered the cognitive screens, suggesting that nonclinically trained personnel could be utilized for risk assessment. The major limitation is the restricted amount of covariate data that were collected. Data for this project were collected to operationalize and demonstrate the impact and business case of a delirium risk modification program,[17] limiting the ability to perform adjustment for other covariates such as comorbidity and reason for admission. Also, due to the nature of this project, a diagnosis of delirium was not determined. A limitation of excluding in‐hospital deaths from the cost analysis was that some individuals at high risk died early, thus costing less overall. Generalizability is limited by an over‐representation of males within a single setting. Further use and understanding of mRASS and MOYB in other population is warranted and welcomed. Use of MOYB is also a limitation considering that scores are not standardized across patients or settings.[26] Data regarding administration time of either of these tools were not collected; therefore, determining that these are ultra‐brief assessments (<1 minute) is based on estimates. As such, these measures should not be the sole source of information for clinical evaluation and diagnosis.

CONCLUSION

This work found that impaired performance on brief cognitive assessments of arousal and attention in hospitalized patients were associated with restraint use, in‐hospital mortality, longer LOS, less discharge home, and hospital costs. Routine screening of older patients with brief, user‐friendly cognitive assessments upon admission can identify those who would benefit from additional assessment and intervention to alleviate individual and economic burdens.

Altered mental status (AMS), characterized by abnormal changes in a patient's arousal and/or cognition, is a significant predictor of hospital mortality.[1, 2, 3] Yet despite its prevalence[3, 4, 5] and importance, up to three‐quarters of AMS events go unrecognized by caregivers.[6, 7, 8] Acute changes in mental status, often caused by delirium in the hospitalized patient,[3] can present nonspecifically, making it difficult to detect and distinguish from other diagnoses such as depression or dementia.[7, 9] Further complicating the recognition of AMS, numerous and imprecise qualitative descriptors such as confused and alert and oriented are used in clinical practice to describe the mental status of patients.[10] Thus, more objective measures may result in improved detection of altered mental status and in earlier diagnostic and therapeutic interventions.

In critically ill patients, several scales have been widely adopted for quantifying mental status. The Richmond Agitation and Sedation Scale (RASS) was created to optimize sedation.[11] The Glasgow Coma Scale (GCS) was developed for head‐trauma patients[12] and is now a standardized assessment tool in intensive care units,[13] the emergency department,[14] and the prehospital setting.[15] In addition, a simplified scale, AVPU (Alert, responsive to Verbal stimuli, responsive to Painful stimuli, and Unresponsive) was initially used in the primary survey of trauma patients[16] but is now a common component of early‐warning scores and rapid response activation criteria, such as the Modified Early Warning Score (MEWS).[17, 18] In fact, in a systematic review of 72 distinct early‐warning scores, 89% of the scores used AVPU as the measure of mentation.[17] However, the utility of these 3 scales is not well established in the general‐ward setting. Our aim was therefore to compare the accuracies of AVPU, GCS, and RASS for predicting mortality in hospitalized general‐ward patients to provide insight into the accuracy of these different scores for clinical deterioration.

METHODS

RESULTS

During the study period, 313,577 complete GCS and 305,177 RASS scores were recorded in the electronic health record by nursing staff. A total of 26,806 (17,603 GCS and 9203 RASS) observations were excluded due to nonsimultaneous measurement of the other score, resulting in 295,974 paired mental‐status observations. These observations were obtained from 26,873 admissions in 17,660 unique patients, with a median MEWS at ward admission of 1 (IQR 11). The mean patient age was 57 years (SD 17), and 23% were surgical patients (Table 1). Patients spent a median 63.9 hours (IQR 26.7118.6) on the wards per admission and contributed a median of 3 paired observations (IQR 24) per day, with 91% of patients having at least 2 observations per day. A total of 417 (1.6%) general‐ward admissions resulted in death during the hospitalization, with 354 mental‐status observations occurring within 24 hours of a death. In addition, 26,618 (99.9%) admissions had at least 1 paired mental‐status observation within the last 24 hours of their ward stay.

AVPU was moderately correlated with GCS (Spearman's =0.56) (Figure 1a) and weakly correlated with RASS (Spearman's =0.28) (Figure 1b). GCS scores were also weakly correlated to RASS (Spearman's =0.13, P<0.001). Notably, AVPU mapped to distinct levels of GCS, with Alert associated with a median GCS total score of 15, Voice a score of 12, Pain a score of 8, and Unresponsive a score of 5. Abnormal mental‐status scores on any scale were associated with significantly higher odds of death within 24 hours than normal mental‐status scores (Table 2). This association was consistent within the 3 subscales of GCS and for scores in both the sedation (<0) and agitation (>0) ranges of RASS.

Figure 1

AVPU was the least accurate predictor of mortality (AUC 0.73 [95% confidence interval {CI}: 0.710.76]), whereas simultaneous use of GCS and RASS was the most accurate predictor (AUC 0.85 [95% CI: 0.820.87] (Figure 2). The accuracies of GCS and RASS were not significantly different from one another in the total study population (AUC 0.80 [95% CI: 0.770.83] and 0.82 [0.790.84], respectively, P=0.13). Ten‐fold cross‐validation to estimate the internal validity of the RASS model resulted in a lower AUC (0.78 [95% CI: 0.750.81]) for RASS as a predictor of 24‐hour mortality. Subgroup analysis indicated that RASS was more accurate than GCS in younger patients (<57 years old) and in surgical patients (Figure 3).

Figure 2

Figure 3

Removal of the 255 admissions missing a paired mental‐status observation within the last 24 hours of their ward stay resulted in no change in the AUC values. A sensitivity analysis for prediction of a combined secondary outcome of 24‐hour intensive care unit ICU transfer or cardiac arrest yielded lower AUCs for each mental‐status scale, with no change in the association among scales.

DISCUSSION

To our knowledge, this study is the first to compare the accuracies of AVPU, GCS, and RASS for predicting mortality in the general‐ward setting. Similar to McNarry and Goldhill, we demonstrated that AVPU scores mapped to distinct levels of GCS. Although our study reports the same median GCS scores of 15 and 8 for AVPU levels of Alert and Pain, respectively, we indicate slightly lower corresponding median GCS scores for AVPU scores of Voice (12 vs 13) and Unresponsive (5 vs 6) than their previous work.[21] We found that AVPU was the least accurate predictor of mortality within 24 hours of an observation, and the combination of GCS and RASS was the most accurate. RASS was at least as accurate a predictor for 24‐hour mortality in comparison to GCS total in the overall study population. However, the RASS score was the most accurate individual score in surgical and younger patients. These findings suggest that changing from the commonly used AVPU scale to the RASS and/or GCS would improve the prognostic ability of mental‐status assessments on the general wards.

Buist and colleagues have previously demonstrated altered mental status to be one of the strongest predictors of death on the wards. In that study, a GCS score of 3 and a decrease in GCS score by more than 2 points were independently associated with mortality (odds ratio 6.1 [95% CI: 3.111.8] and 5.5 [95% CI: 2.611.9], respectively).[22] We have also previously shown that after adjusting for vital signs, being unresponsive to pain was associated with a 4.5‐fold increase in the odds of death within 24 hours,[23]whereas Subbe and colleagues showed a relative risk ratio of 5.2 (95% CI: 1.518.1) for the combined endpoint of cardiac arrest, death at 60 days, or admission to the intensive care/high dependency unit.[19] In the current study, the magnitude of these associations was even stronger, with a GCS score <13 correlating with a 55‐fold increase in the odds of death, compared to a normal GCS, and not being alert being associated with a 33.8‐fold increase in the odds of death. This difference in magnitude is likely a product of the univariate nature of the current analysis, compared to both the Buist et al. and Churpek et al. studies, which adjusted for vital signs, thereby lessening the impact of any single predictor. Because this study was designed to compare mental‐status variables to one another for future model inclusion, and all the analyses were paired, confounding by additional predictors of death was not a concern.

One of the potential strengths of RASS over GCS and AVPU is its ability to measure agitation levels, in addition to depressed mentation, a feature that has been shown to be present in up to 60% of delirium episodes.[24] This may also explain why RASS was the most accurate predictor of mortality in our subset of younger patients and surgical patients, because hyperactive delirium is more common in younger and healthier patients, which surgical patients tend to be as compared to medical patients.[25, 26] In this study, we found negative RASS scores portending a worse prognosis than positive ones, which supports previous findings that hypoactive delirium had a higher association with mortality than hyperactive delirium at 6 months (hazard ratio 1.90 vs 1.37) and at 1 year (hazard ratio 1.60 vs 1.30) in elderly patients at postacute‐care facilities in 2 separate studies.[27, 28] However, a study of patients undergoing surgery for hip fracture found that patients with hyperactive delirium were more likely to die or be placed in a nursing home at 1 month follow‐up when compared to patients with purely hypoactive delirium (79% vs 32%, P=0.003).[29]

We found the assessment of RASS and GCS by ward nurses to be highly feasible. During the study period, nurses assessed mental status with the GCS and RASS scales at least once per 12‐hour shift in 91% of patients. GCS has been shown to be reliably and accurately recorded by experienced nurses (reliability coefficient=0.944 with 96.4% agreement with expert ratings).[30] RASS can take <30 seconds to administer, and in previous studies of the ICU setting has been shown to have over 94% nurse compliance for administration,[31] and good inter‐rater reliability (weighted kappa 0.66 and 0.89, respectively).[31, 32] Further, in a prior survey of 55 critical care nurses, 82% agreed that RASS was easy to score and clinically relevant.[31]

This study has several limitations. First, it was conducted in a single academic institution, which may limit generalizability to other hospitals. Second, baseline cognition and comorbidities were not available in the dataset, so we were unable to conduct additional subgroup analyses by these categories. However, we used age and hospital admission type as proxies. Third, the AVPU scores in this study were extracted from the Eye subset of the GCS scale, as AVPU was not directly assessed on our wards during the study period. Clinical assessment of mental status on the AVPU scale notes the presence of any active patient response (eg, eye opening, grunting, moaning, movement) to increasingly noxious stimuli. As such, our adaptation of AVPU using only eye‐opening criteria may underestimate the true number of patients correctly classified as alert, or responding to vocal/painful stimuli. However, a sensitivity analysis comparing directly assessed AVPU during a 3‐year period prior to the study implementation at our institution, and AVPU derived from the GCS Eye subscale for the study period, indicated no difference in predictive value for 24‐hour mortality. Fourth, we did not perform trend analyses for change from baseline mental status or evolution of AMS, which may more accurately predict 24‐hour mortality than discrete mental‐status observations. Finally, the 3 scales we compared differ in length, which may bias the AUC against AVPU, a 4‐point scale with a trapezoidal ROC curve compared to the smoother curve generated by the 15‐point GCS scale, for example. However, the lack of discrimination of the AVPU is the likely source of its lesser accuracy.

CONCLUSION

In the general‐ward setting, routine collection of GCS and RASS is feasible, and both are significantly more accurate for predicting mortality than the more commonly used AVPU scale. In addition, the combination of GCS and RASS has greater accuracy than any of the 3 individual scales. RASS may be particularly beneficial in the assessment of younger and/or surgical patients. Routine documentation and tracking of GCS and/or RASS by nurses may improve the detection of clinical deterioration in general‐ward patients. In addition, future early‐warning scores may benefit from the inclusion of GCS and/or RASS in lieu of AVPU.

Altered mental status (AMS), characterized by abnormal changes in a patient's arousal and/or cognition, is a significant predictor of hospital mortality.[1, 2, 3] Yet despite its prevalence[3, 4, 5] and importance, up to three‐quarters of AMS events go unrecognized by caregivers.[6, 7, 8] Acute changes in mental status, often caused by delirium in the hospitalized patient,[3] can present nonspecifically, making it difficult to detect and distinguish from other diagnoses such as depression or dementia.[7, 9] Further complicating the recognition of AMS, numerous and imprecise qualitative descriptors such as confused and alert and oriented are used in clinical practice to describe the mental status of patients.[10] Thus, more objective measures may result in improved detection of altered mental status and in earlier diagnostic and therapeutic interventions.

In critically ill patients, several scales have been widely adopted for quantifying mental status. The Richmond Agitation and Sedation Scale (RASS) was created to optimize sedation.[11] The Glasgow Coma Scale (GCS) was developed for head‐trauma patients[12] and is now a standardized assessment tool in intensive care units,[13] the emergency department,[14] and the prehospital setting.[15] In addition, a simplified scale, AVPU (Alert, responsive to Verbal stimuli, responsive to Painful stimuli, and Unresponsive) was initially used in the primary survey of trauma patients[16] but is now a common component of early‐warning scores and rapid response activation criteria, such as the Modified Early Warning Score (MEWS).[17, 18] In fact, in a systematic review of 72 distinct early‐warning scores, 89% of the scores used AVPU as the measure of mentation.[17] However, the utility of these 3 scales is not well established in the general‐ward setting. Our aim was therefore to compare the accuracies of AVPU, GCS, and RASS for predicting mortality in hospitalized general‐ward patients to provide insight into the accuracy of these different scores for clinical deterioration.

METHODS

RESULTS

During the study period, 313,577 complete GCS and 305,177 RASS scores were recorded in the electronic health record by nursing staff. A total of 26,806 (17,603 GCS and 9203 RASS) observations were excluded due to nonsimultaneous measurement of the other score, resulting in 295,974 paired mental‐status observations. These observations were obtained from 26,873 admissions in 17,660 unique patients, with a median MEWS at ward admission of 1 (IQR 11). The mean patient age was 57 years (SD 17), and 23% were surgical patients (Table 1). Patients spent a median 63.9 hours (IQR 26.7118.6) on the wards per admission and contributed a median of 3 paired observations (IQR 24) per day, with 91% of patients having at least 2 observations per day. A total of 417 (1.6%) general‐ward admissions resulted in death during the hospitalization, with 354 mental‐status observations occurring within 24 hours of a death. In addition, 26,618 (99.9%) admissions had at least 1 paired mental‐status observation within the last 24 hours of their ward stay.

AVPU was moderately correlated with GCS (Spearman's =0.56) (Figure 1a) and weakly correlated with RASS (Spearman's =0.28) (Figure 1b). GCS scores were also weakly correlated to RASS (Spearman's =0.13, P<0.001). Notably, AVPU mapped to distinct levels of GCS, with Alert associated with a median GCS total score of 15, Voice a score of 12, Pain a score of 8, and Unresponsive a score of 5. Abnormal mental‐status scores on any scale were associated with significantly higher odds of death within 24 hours than normal mental‐status scores (Table 2). This association was consistent within the 3 subscales of GCS and for scores in both the sedation (<0) and agitation (>0) ranges of RASS.

Figure 1

AVPU was the least accurate predictor of mortality (AUC 0.73 [95% confidence interval {CI}: 0.710.76]), whereas simultaneous use of GCS and RASS was the most accurate predictor (AUC 0.85 [95% CI: 0.820.87] (Figure 2). The accuracies of GCS and RASS were not significantly different from one another in the total study population (AUC 0.80 [95% CI: 0.770.83] and 0.82 [0.790.84], respectively, P=0.13). Ten‐fold cross‐validation to estimate the internal validity of the RASS model resulted in a lower AUC (0.78 [95% CI: 0.750.81]) for RASS as a predictor of 24‐hour mortality. Subgroup analysis indicated that RASS was more accurate than GCS in younger patients (<57 years old) and in surgical patients (Figure 3).

Figure 2

Figure 3

Removal of the 255 admissions missing a paired mental‐status observation within the last 24 hours of their ward stay resulted in no change in the AUC values. A sensitivity analysis for prediction of a combined secondary outcome of 24‐hour intensive care unit ICU transfer or cardiac arrest yielded lower AUCs for each mental‐status scale, with no change in the association among scales.

DISCUSSION

To our knowledge, this study is the first to compare the accuracies of AVPU, GCS, and RASS for predicting mortality in the general‐ward setting. Similar to McNarry and Goldhill, we demonstrated that AVPU scores mapped to distinct levels of GCS. Although our study reports the same median GCS scores of 15 and 8 for AVPU levels of Alert and Pain, respectively, we indicate slightly lower corresponding median GCS scores for AVPU scores of Voice (12 vs 13) and Unresponsive (5 vs 6) than their previous work.[21] We found that AVPU was the least accurate predictor of mortality within 24 hours of an observation, and the combination of GCS and RASS was the most accurate. RASS was at least as accurate a predictor for 24‐hour mortality in comparison to GCS total in the overall study population. However, the RASS score was the most accurate individual score in surgical and younger patients. These findings suggest that changing from the commonly used AVPU scale to the RASS and/or GCS would improve the prognostic ability of mental‐status assessments on the general wards.

Buist and colleagues have previously demonstrated altered mental status to be one of the strongest predictors of death on the wards. In that study, a GCS score of 3 and a decrease in GCS score by more than 2 points were independently associated with mortality (odds ratio 6.1 [95% CI: 3.111.8] and 5.5 [95% CI: 2.611.9], respectively).[22] We have also previously shown that after adjusting for vital signs, being unresponsive to pain was associated with a 4.5‐fold increase in the odds of death within 24 hours,[23]whereas Subbe and colleagues showed a relative risk ratio of 5.2 (95% CI: 1.518.1) for the combined endpoint of cardiac arrest, death at 60 days, or admission to the intensive care/high dependency unit.[19] In the current study, the magnitude of these associations was even stronger, with a GCS score <13 correlating with a 55‐fold increase in the odds of death, compared to a normal GCS, and not being alert being associated with a 33.8‐fold increase in the odds of death. This difference in magnitude is likely a product of the univariate nature of the current analysis, compared to both the Buist et al. and Churpek et al. studies, which adjusted for vital signs, thereby lessening the impact of any single predictor. Because this study was designed to compare mental‐status variables to one another for future model inclusion, and all the analyses were paired, confounding by additional predictors of death was not a concern.

One of the potential strengths of RASS over GCS and AVPU is its ability to measure agitation levels, in addition to depressed mentation, a feature that has been shown to be present in up to 60% of delirium episodes.[24] This may also explain why RASS was the most accurate predictor of mortality in our subset of younger patients and surgical patients, because hyperactive delirium is more common in younger and healthier patients, which surgical patients tend to be as compared to medical patients.[25, 26] In this study, we found negative RASS scores portending a worse prognosis than positive ones, which supports previous findings that hypoactive delirium had a higher association with mortality than hyperactive delirium at 6 months (hazard ratio 1.90 vs 1.37) and at 1 year (hazard ratio 1.60 vs 1.30) in elderly patients at postacute‐care facilities in 2 separate studies.[27, 28] However, a study of patients undergoing surgery for hip fracture found that patients with hyperactive delirium were more likely to die or be placed in a nursing home at 1 month follow‐up when compared to patients with purely hypoactive delirium (79% vs 32%, P=0.003).[29]

We found the assessment of RASS and GCS by ward nurses to be highly feasible. During the study period, nurses assessed mental status with the GCS and RASS scales at least once per 12‐hour shift in 91% of patients. GCS has been shown to be reliably and accurately recorded by experienced nurses (reliability coefficient=0.944 with 96.4% agreement with expert ratings).[30] RASS can take <30 seconds to administer, and in previous studies of the ICU setting has been shown to have over 94% nurse compliance for administration,[31] and good inter‐rater reliability (weighted kappa 0.66 and 0.89, respectively).[31, 32] Further, in a prior survey of 55 critical care nurses, 82% agreed that RASS was easy to score and clinically relevant.[31]

This study has several limitations. First, it was conducted in a single academic institution, which may limit generalizability to other hospitals. Second, baseline cognition and comorbidities were not available in the dataset, so we were unable to conduct additional subgroup analyses by these categories. However, we used age and hospital admission type as proxies. Third, the AVPU scores in this study were extracted from the Eye subset of the GCS scale, as AVPU was not directly assessed on our wards during the study period. Clinical assessment of mental status on the AVPU scale notes the presence of any active patient response (eg, eye opening, grunting, moaning, movement) to increasingly noxious stimuli. As such, our adaptation of AVPU using only eye‐opening criteria may underestimate the true number of patients correctly classified as alert, or responding to vocal/painful stimuli. However, a sensitivity analysis comparing directly assessed AVPU during a 3‐year period prior to the study implementation at our institution, and AVPU derived from the GCS Eye subscale for the study period, indicated no difference in predictive value for 24‐hour mortality. Fourth, we did not perform trend analyses for change from baseline mental status or evolution of AMS, which may more accurately predict 24‐hour mortality than discrete mental‐status observations. Finally, the 3 scales we compared differ in length, which may bias the AUC against AVPU, a 4‐point scale with a trapezoidal ROC curve compared to the smoother curve generated by the 15‐point GCS scale, for example. However, the lack of discrimination of the AVPU is the likely source of its lesser accuracy.

CONCLUSION

In the general‐ward setting, routine collection of GCS and RASS is feasible, and both are significantly more accurate for predicting mortality than the more commonly used AVPU scale. In addition, the combination of GCS and RASS has greater accuracy than any of the 3 individual scales. RASS may be particularly beneficial in the assessment of younger and/or surgical patients. Routine documentation and tracking of GCS and/or RASS by nurses may improve the detection of clinical deterioration in general‐ward patients. In addition, future early‐warning scores may benefit from the inclusion of GCS and/or RASS in lieu of AVPU.

Altered mental status (AMS), characterized by abnormal changes in a patient's arousal and/or cognition, is a significant predictor of hospital mortality.[1, 2, 3] Yet despite its prevalence[3, 4, 5] and importance, up to three‐quarters of AMS events go unrecognized by caregivers.[6, 7, 8] Acute changes in mental status, often caused by delirium in the hospitalized patient,[3] can present nonspecifically, making it difficult to detect and distinguish from other diagnoses such as depression or dementia.[7, 9] Further complicating the recognition of AMS, numerous and imprecise qualitative descriptors such as confused and alert and oriented are used in clinical practice to describe the mental status of patients.[10] Thus, more objective measures may result in improved detection of altered mental status and in earlier diagnostic and therapeutic interventions.

In critically ill patients, several scales have been widely adopted for quantifying mental status. The Richmond Agitation and Sedation Scale (RASS) was created to optimize sedation.[11] The Glasgow Coma Scale (GCS) was developed for head‐trauma patients[12] and is now a standardized assessment tool in intensive care units,[13] the emergency department,[14] and the prehospital setting.[15] In addition, a simplified scale, AVPU (Alert, responsive to Verbal stimuli, responsive to Painful stimuli, and Unresponsive) was initially used in the primary survey of trauma patients[16] but is now a common component of early‐warning scores and rapid response activation criteria, such as the Modified Early Warning Score (MEWS).[17, 18] In fact, in a systematic review of 72 distinct early‐warning scores, 89% of the scores used AVPU as the measure of mentation.[17] However, the utility of these 3 scales is not well established in the general‐ward setting. Our aim was therefore to compare the accuracies of AVPU, GCS, and RASS for predicting mortality in hospitalized general‐ward patients to provide insight into the accuracy of these different scores for clinical deterioration.

METHODS

RESULTS

During the study period, 313,577 complete GCS and 305,177 RASS scores were recorded in the electronic health record by nursing staff. A total of 26,806 (17,603 GCS and 9203 RASS) observations were excluded due to nonsimultaneous measurement of the other score, resulting in 295,974 paired mental‐status observations. These observations were obtained from 26,873 admissions in 17,660 unique patients, with a median MEWS at ward admission of 1 (IQR 11). The mean patient age was 57 years (SD 17), and 23% were surgical patients (Table 1). Patients spent a median 63.9 hours (IQR 26.7118.6) on the wards per admission and contributed a median of 3 paired observations (IQR 24) per day, with 91% of patients having at least 2 observations per day. A total of 417 (1.6%) general‐ward admissions resulted in death during the hospitalization, with 354 mental‐status observations occurring within 24 hours of a death. In addition, 26,618 (99.9%) admissions had at least 1 paired mental‐status observation within the last 24 hours of their ward stay.

AVPU was moderately correlated with GCS (Spearman's =0.56) (Figure 1a) and weakly correlated with RASS (Spearman's =0.28) (Figure 1b). GCS scores were also weakly correlated to RASS (Spearman's =0.13, P<0.001). Notably, AVPU mapped to distinct levels of GCS, with Alert associated with a median GCS total score of 15, Voice a score of 12, Pain a score of 8, and Unresponsive a score of 5. Abnormal mental‐status scores on any scale were associated with significantly higher odds of death within 24 hours than normal mental‐status scores (Table 2). This association was consistent within the 3 subscales of GCS and for scores in both the sedation (<0) and agitation (>0) ranges of RASS.

Figure 1

AVPU was the least accurate predictor of mortality (AUC 0.73 [95% confidence interval {CI}: 0.710.76]), whereas simultaneous use of GCS and RASS was the most accurate predictor (AUC 0.85 [95% CI: 0.820.87] (Figure 2). The accuracies of GCS and RASS were not significantly different from one another in the total study population (AUC 0.80 [95% CI: 0.770.83] and 0.82 [0.790.84], respectively, P=0.13). Ten‐fold cross‐validation to estimate the internal validity of the RASS model resulted in a lower AUC (0.78 [95% CI: 0.750.81]) for RASS as a predictor of 24‐hour mortality. Subgroup analysis indicated that RASS was more accurate than GCS in younger patients (<57 years old) and in surgical patients (Figure 3).

Figure 2

Figure 3

Removal of the 255 admissions missing a paired mental‐status observation within the last 24 hours of their ward stay resulted in no change in the AUC values. A sensitivity analysis for prediction of a combined secondary outcome of 24‐hour intensive care unit ICU transfer or cardiac arrest yielded lower AUCs for each mental‐status scale, with no change in the association among scales.

DISCUSSION

To our knowledge, this study is the first to compare the accuracies of AVPU, GCS, and RASS for predicting mortality in the general‐ward setting. Similar to McNarry and Goldhill, we demonstrated that AVPU scores mapped to distinct levels of GCS. Although our study reports the same median GCS scores of 15 and 8 for AVPU levels of Alert and Pain, respectively, we indicate slightly lower corresponding median GCS scores for AVPU scores of Voice (12 vs 13) and Unresponsive (5 vs 6) than their previous work.[21] We found that AVPU was the least accurate predictor of mortality within 24 hours of an observation, and the combination of GCS and RASS was the most accurate. RASS was at least as accurate a predictor for 24‐hour mortality in comparison to GCS total in the overall study population. However, the RASS score was the most accurate individual score in surgical and younger patients. These findings suggest that changing from the commonly used AVPU scale to the RASS and/or GCS would improve the prognostic ability of mental‐status assessments on the general wards.

Buist and colleagues have previously demonstrated altered mental status to be one of the strongest predictors of death on the wards. In that study, a GCS score of 3 and a decrease in GCS score by more than 2 points were independently associated with mortality (odds ratio 6.1 [95% CI: 3.111.8] and 5.5 [95% CI: 2.611.9], respectively).[22] We have also previously shown that after adjusting for vital signs, being unresponsive to pain was associated with a 4.5‐fold increase in the odds of death within 24 hours,[23]whereas Subbe and colleagues showed a relative risk ratio of 5.2 (95% CI: 1.518.1) for the combined endpoint of cardiac arrest, death at 60 days, or admission to the intensive care/high dependency unit.[19] In the current study, the magnitude of these associations was even stronger, with a GCS score <13 correlating with a 55‐fold increase in the odds of death, compared to a normal GCS, and not being alert being associated with a 33.8‐fold increase in the odds of death. This difference in magnitude is likely a product of the univariate nature of the current analysis, compared to both the Buist et al. and Churpek et al. studies, which adjusted for vital signs, thereby lessening the impact of any single predictor. Because this study was designed to compare mental‐status variables to one another for future model inclusion, and all the analyses were paired, confounding by additional predictors of death was not a concern.

One of the potential strengths of RASS over GCS and AVPU is its ability to measure agitation levels, in addition to depressed mentation, a feature that has been shown to be present in up to 60% of delirium episodes.[24] This may also explain why RASS was the most accurate predictor of mortality in our subset of younger patients and surgical patients, because hyperactive delirium is more common in younger and healthier patients, which surgical patients tend to be as compared to medical patients.[25, 26] In this study, we found negative RASS scores portending a worse prognosis than positive ones, which supports previous findings that hypoactive delirium had a higher association with mortality than hyperactive delirium at 6 months (hazard ratio 1.90 vs 1.37) and at 1 year (hazard ratio 1.60 vs 1.30) in elderly patients at postacute‐care facilities in 2 separate studies.[27, 28] However, a study of patients undergoing surgery for hip fracture found that patients with hyperactive delirium were more likely to die or be placed in a nursing home at 1 month follow‐up when compared to patients with purely hypoactive delirium (79% vs 32%, P=0.003).[29]

We found the assessment of RASS and GCS by ward nurses to be highly feasible. During the study period, nurses assessed mental status with the GCS and RASS scales at least once per 12‐hour shift in 91% of patients. GCS has been shown to be reliably and accurately recorded by experienced nurses (reliability coefficient=0.944 with 96.4% agreement with expert ratings).[30] RASS can take <30 seconds to administer, and in previous studies of the ICU setting has been shown to have over 94% nurse compliance for administration,[31] and good inter‐rater reliability (weighted kappa 0.66 and 0.89, respectively).[31, 32] Further, in a prior survey of 55 critical care nurses, 82% agreed that RASS was easy to score and clinically relevant.[31]

This study has several limitations. First, it was conducted in a single academic institution, which may limit generalizability to other hospitals. Second, baseline cognition and comorbidities were not available in the dataset, so we were unable to conduct additional subgroup analyses by these categories. However, we used age and hospital admission type as proxies. Third, the AVPU scores in this study were extracted from the Eye subset of the GCS scale, as AVPU was not directly assessed on our wards during the study period. Clinical assessment of mental status on the AVPU scale notes the presence of any active patient response (eg, eye opening, grunting, moaning, movement) to increasingly noxious stimuli. As such, our adaptation of AVPU using only eye‐opening criteria may underestimate the true number of patients correctly classified as alert, or responding to vocal/painful stimuli. However, a sensitivity analysis comparing directly assessed AVPU during a 3‐year period prior to the study implementation at our institution, and AVPU derived from the GCS Eye subscale for the study period, indicated no difference in predictive value for 24‐hour mortality. Fourth, we did not perform trend analyses for change from baseline mental status or evolution of AMS, which may more accurately predict 24‐hour mortality than discrete mental‐status observations. Finally, the 3 scales we compared differ in length, which may bias the AUC against AVPU, a 4‐point scale with a trapezoidal ROC curve compared to the smoother curve generated by the 15‐point GCS scale, for example. However, the lack of discrimination of the AVPU is the likely source of its lesser accuracy.

CONCLUSION

In the general‐ward setting, routine collection of GCS and RASS is feasible, and both are significantly more accurate for predicting mortality than the more commonly used AVPU scale. In addition, the combination of GCS and RASS has greater accuracy than any of the 3 individual scales. RASS may be particularly beneficial in the assessment of younger and/or surgical patients. Routine documentation and tracking of GCS and/or RASS by nurses may improve the detection of clinical deterioration in general‐ward patients. In addition, future early‐warning scores may benefit from the inclusion of GCS and/or RASS in lieu of AVPU.

Delirium is a form of acute brain failure that affects up to 64% of older hospitalized patients and is associated with a multitude of adverse outcomes.[1] Healthcare providers, regardless of clinical setting, do not identify delirium in approximately 75% of cases.[2, 3] The paucity of brief and simple delirium assessment tools has been a barrier to improving delirium recognition.

To address this unmet need, several ultrabrief (<30 seconds) delirium assessment tools have been recently studied. In this issue of the Journal of Hospital Medicine, Fick et al. evaluated 20 individual components of the 3‐minute diagnostic interview for delirium using the Confusion Assessment Method (3D‐CAM), which was recently validated in older hospitalized patients.[4, 5] They observed that the best‐performing single‐item delirium assessment was the months of the year backward (MOTYB) task from December to January. This task assesses for inattention, a cardinal feature of delirium. Using a cutoff of 1 or more errors, the MOTYB was 83% sensitive and 69% specific for delirium.[5] By adding name the day of the week, the sensitivity increased to 93% with similar specificity (64%). This supports research by O'Regan et al., who examined MOTYB, but defined a positive screen if they could not recite the months backward from December to July perfectly. They observed a sensitivity and specificity of 84% and 90%, respectively, in older hospitalized patients.[6]

The assessment of arousal, another feature of delirium, has also garnered significant interest as another ultrabrief delirium screening method. Arousal is the patient's responsiveness to the environment and can be assessed during routine clinical care. Fick et al. observed that impaired arousal using the 3D‐CAM was 19% sensitive for delirium. This is in contrast to others who have reported sensitivities of 64% to 84%.[7, 8, 9] The difference in sensitivity may in part be explained by the method used to detect arousal. The 3D‐CAM asks, Was the patient sleep/stuporous? or Was the patient hyperviglant? Previous studies used the Richmond Agitation Sedation Scale (RASS), an arousal scale based on eye contact and physical behaviors to assess patients from 5 (coma) to +4 (combative).[10] Therefore, it is important to consider the method of arousal assessment if using this feature for delirium screening.

These ultrabrief delirium assessments would be even more clinically useful if they identified patients at high risk for adverse outcomes. In this same journal issue, 2 studies evaluated the prognostic ability of several ultrabrief delirium assessments. Zadravecz et al. observed that an abnormal RASS was a moderately good predictor of 24‐hour mortality, with an area under the receiver operating characteristic curve of 0.82.[11] Yevchak et al. observed that an abnormal RASS or MOTYB was associated with longer hospital length of stays, increased in‐hospital mortality, and need for skilled nursing.[12]

Viewed as a whole, these studies represent a significant advancement in delirium measurement and have the potential to improve this quality‐of‐care issue. However, uncertainties still exist. (1) Can these ultrabrief delirium assessments be used as standalone assessments? Based upon current data, these assessments have a significant proportion of false negative and positive rates. The effect on such misclassification on patient outcomes and healthcare utilization needs to be clarified. Because of this concern, Fick et al. recommended performing a more specific delirium assessment in those who have a positive MOTYB screen.[5] (2) What is the optimal cutoff of the MOTYB task and does this cutoff vary in different patient populations? The optimal cutoff will depend on whether or not a more sensitive test (lower error threshold) or specific test (higher error threshold) is desired. The optimal cutoff may also depend on the patient population (eg, demented versus nondemented). (3) Most important to practicing hospitalist and patients, will introducing these ultrabrief delirium assessments improve delirium recognition and improve patient outcomes? The impetus for widespread implementation of these assessments would be strengthened if healthcare providers successfully applied these assessments in clinical practice and subsequently improved outcomes.

In conclusion, the MOTYB and the assessment of arousal may be reasonable alternatives to more conventional delirium screening, especially in clinical environments with significant time constraints. However, additional research is needed to better refine these instruments to the clinical environment they will be used and determine how they impact clinical care and patient outcomes.

Delirium is a form of acute brain failure that affects up to 64% of older hospitalized patients and is associated with a multitude of adverse outcomes.[1] Healthcare providers, regardless of clinical setting, do not identify delirium in approximately 75% of cases.[2, 3] The paucity of brief and simple delirium assessment tools has been a barrier to improving delirium recognition.

To address this unmet need, several ultrabrief (<30 seconds) delirium assessment tools have been recently studied. In this issue of the Journal of Hospital Medicine, Fick et al. evaluated 20 individual components of the 3‐minute diagnostic interview for delirium using the Confusion Assessment Method (3D‐CAM), which was recently validated in older hospitalized patients.[4, 5] They observed that the best‐performing single‐item delirium assessment was the months of the year backward (MOTYB) task from December to January. This task assesses for inattention, a cardinal feature of delirium. Using a cutoff of 1 or more errors, the MOTYB was 83% sensitive and 69% specific for delirium.[5] By adding name the day of the week, the sensitivity increased to 93% with similar specificity (64%). This supports research by O'Regan et al., who examined MOTYB, but defined a positive screen if they could not recite the months backward from December to July perfectly. They observed a sensitivity and specificity of 84% and 90%, respectively, in older hospitalized patients.[6]

The assessment of arousal, another feature of delirium, has also garnered significant interest as another ultrabrief delirium screening method. Arousal is the patient's responsiveness to the environment and can be assessed during routine clinical care. Fick et al. observed that impaired arousal using the 3D‐CAM was 19% sensitive for delirium. This is in contrast to others who have reported sensitivities of 64% to 84%.[7, 8, 9] The difference in sensitivity may in part be explained by the method used to detect arousal. The 3D‐CAM asks, Was the patient sleep/stuporous? or Was the patient hyperviglant? Previous studies used the Richmond Agitation Sedation Scale (RASS), an arousal scale based on eye contact and physical behaviors to assess patients from 5 (coma) to +4 (combative).[10] Therefore, it is important to consider the method of arousal assessment if using this feature for delirium screening.

These ultrabrief delirium assessments would be even more clinically useful if they identified patients at high risk for adverse outcomes. In this same journal issue, 2 studies evaluated the prognostic ability of several ultrabrief delirium assessments. Zadravecz et al. observed that an abnormal RASS was a moderately good predictor of 24‐hour mortality, with an area under the receiver operating characteristic curve of 0.82.[11] Yevchak et al. observed that an abnormal RASS or MOTYB was associated with longer hospital length of stays, increased in‐hospital mortality, and need for skilled nursing.[12]

Viewed as a whole, these studies represent a significant advancement in delirium measurement and have the potential to improve this quality‐of‐care issue. However, uncertainties still exist. (1) Can these ultrabrief delirium assessments be used as standalone assessments? Based upon current data, these assessments have a significant proportion of false negative and positive rates. The effect on such misclassification on patient outcomes and healthcare utilization needs to be clarified. Because of this concern, Fick et al. recommended performing a more specific delirium assessment in those who have a positive MOTYB screen.[5] (2) What is the optimal cutoff of the MOTYB task and does this cutoff vary in different patient populations? The optimal cutoff will depend on whether or not a more sensitive test (lower error threshold) or specific test (higher error threshold) is desired. The optimal cutoff may also depend on the patient population (eg, demented versus nondemented). (3) Most important to practicing hospitalist and patients, will introducing these ultrabrief delirium assessments improve delirium recognition and improve patient outcomes? The impetus for widespread implementation of these assessments would be strengthened if healthcare providers successfully applied these assessments in clinical practice and subsequently improved outcomes.

In conclusion, the MOTYB and the assessment of arousal may be reasonable alternatives to more conventional delirium screening, especially in clinical environments with significant time constraints. However, additional research is needed to better refine these instruments to the clinical environment they will be used and determine how they impact clinical care and patient outcomes.

Delirium is a form of acute brain failure that affects up to 64% of older hospitalized patients and is associated with a multitude of adverse outcomes.[1] Healthcare providers, regardless of clinical setting, do not identify delirium in approximately 75% of cases.[2, 3] The paucity of brief and simple delirium assessment tools has been a barrier to improving delirium recognition.

To address this unmet need, several ultrabrief (<30 seconds) delirium assessment tools have been recently studied. In this issue of the Journal of Hospital Medicine, Fick et al. evaluated 20 individual components of the 3‐minute diagnostic interview for delirium using the Confusion Assessment Method (3D‐CAM), which was recently validated in older hospitalized patients.[4, 5] They observed that the best‐performing single‐item delirium assessment was the months of the year backward (MOTYB) task from December to January. This task assesses for inattention, a cardinal feature of delirium. Using a cutoff of 1 or more errors, the MOTYB was 83% sensitive and 69% specific for delirium.[5] By adding name the day of the week, the sensitivity increased to 93% with similar specificity (64%). This supports research by O'Regan et al., who examined MOTYB, but defined a positive screen if they could not recite the months backward from December to July perfectly. They observed a sensitivity and specificity of 84% and 90%, respectively, in older hospitalized patients.[6]

The assessment of arousal, another feature of delirium, has also garnered significant interest as another ultrabrief delirium screening method. Arousal is the patient's responsiveness to the environment and can be assessed during routine clinical care. Fick et al. observed that impaired arousal using the 3D‐CAM was 19% sensitive for delirium. This is in contrast to others who have reported sensitivities of 64% to 84%.[7, 8, 9] The difference in sensitivity may in part be explained by the method used to detect arousal. The 3D‐CAM asks, Was the patient sleep/stuporous? or Was the patient hyperviglant? Previous studies used the Richmond Agitation Sedation Scale (RASS), an arousal scale based on eye contact and physical behaviors to assess patients from 5 (coma) to +4 (combative).[10] Therefore, it is important to consider the method of arousal assessment if using this feature for delirium screening.

These ultrabrief delirium assessments would be even more clinically useful if they identified patients at high risk for adverse outcomes. In this same journal issue, 2 studies evaluated the prognostic ability of several ultrabrief delirium assessments. Zadravecz et al. observed that an abnormal RASS was a moderately good predictor of 24‐hour mortality, with an area under the receiver operating characteristic curve of 0.82.[11] Yevchak et al. observed that an abnormal RASS or MOTYB was associated with longer hospital length of stays, increased in‐hospital mortality, and need for skilled nursing.[12]

Viewed as a whole, these studies represent a significant advancement in delirium measurement and have the potential to improve this quality‐of‐care issue. However, uncertainties still exist. (1) Can these ultrabrief delirium assessments be used as standalone assessments? Based upon current data, these assessments have a significant proportion of false negative and positive rates. The effect on such misclassification on patient outcomes and healthcare utilization needs to be clarified. Because of this concern, Fick et al. recommended performing a more specific delirium assessment in those who have a positive MOTYB screen.[5] (2) What is the optimal cutoff of the MOTYB task and does this cutoff vary in different patient populations? The optimal cutoff will depend on whether or not a more sensitive test (lower error threshold) or specific test (higher error threshold) is desired. The optimal cutoff may also depend on the patient population (eg, demented versus nondemented). (3) Most important to practicing hospitalist and patients, will introducing these ultrabrief delirium assessments improve delirium recognition and improve patient outcomes? The impetus for widespread implementation of these assessments would be strengthened if healthcare providers successfully applied these assessments in clinical practice and subsequently improved outcomes.

In conclusion, the MOTYB and the assessment of arousal may be reasonable alternatives to more conventional delirium screening, especially in clinical environments with significant time constraints. However, additional research is needed to better refine these instruments to the clinical environment they will be used and determine how they impact clinical care and patient outcomes.

In this issue of the Journal of Hospital Medicine, we introduce a new recurring feature, Choosing Wisely: Things We Do for No Reason. The series is based on a talk I have delivered for the past 4 years at the annual national meeting of the Society of Hospital Medicine, in which I highlight 4 diagnostic tests, therapies, or other clinical practices that are commonly performed even though they are of low value to our inpatients.

There are many reasons hospitalists order unnecessary tests or treatments, or employ unhelpful clinical practices. Unnecessary testing may occur when we are not familiar with the test itselfthe actual costs of the test, the operating characteristics of the test, or the evidence supporting its usefulness in specific situations. Some tests are ordered unnecessarily because we cannot retrieve usable results from a different hospital or even our own electronic medical records. We may order tests or treatments due to patient expectations, a perceived need to practice defensively, or economic incentives.

Finally, we may simply order tests because of our uncertainty in the absence of data or simply because they are traditional practices (the way we've always done it). Physicians often order tests and treatments and institute clinical practices learned in residency or fellowship training.[1, 2] Local norms and practices influence physician behavior.

We created Things We Do for No Reason (TWDFNR) as a platform for provocative discussions of practices that have become common parts of hospital care but have limited supporting evidence, or even have evidence refuting or justifiably challenging their value. Each article in TWDFNR will describe why the test, treatment, or other clinical practice is commonly employed, why it may not be of high value, in what circumstances it may actually be valuable, and what conclusions can be drawn from the evidence provided. TWDFNR pieces are not systematic reviews or meta‐analyses and do not represent black and white conclusions or clinical practice standards; they are meant as a starting place for research and active discussions among hospitalists and patients.

In many respects, the Choosing Wisely: Things We Do for No Reason series is an extension of the Choosing Wisely campaign created by the American Board of Internal Medicine Foundation. Like Choosing Wisely, we are focusing on individual tests, treatments, and other clinical practices that are not beneficial and are potentially harmful to patients. Practices discussed may not cause significant physical or financial harm at the time they are used, but they may have significant downstream effects.

The Choosing Wisely campaign has brilliantly identified 5 important hospital medicine low‐value practices, and we hope to identify many more. We hope this series will serve as a grassroots effort to uncover more Choosing Wisely‐type practices. As institutions create their own high‐value care committees, the Choosing Wisely: Things We Do for No Reason series can provide possible agenda items, or provide the opportunity for sites to carry out analyses of their own practices to see whether any of the TWDFNR topics provide local opportunities for implementing higher‐value practices.

Although we do not believe that reducing the low‐value practices that will appear in TWDFNR will, alone, solve our wasteful practices, we hope that highlighting them will remind individuals, institutions, and systems that targeting low‐value practices is a responsibility that we all must embrace. We accept that not everyone will agree that the practices we present are low value, but the conversation is important to have. We invite you to take part in the Choosing Wisely: Things We Do for No Reason conversation. Let us know whether you think the practices highlighted are low value or whether you disagree with the conclusions. We welcome unsolicited proposals for series topics submitted as a 500‐word prcis. Send us your prcis or ideas on low‐value adult or pediatric patient practices that we should highlight in this series by emailing us at [email protected].

Disclosure: Nothing to report.

In this issue of the Journal of Hospital Medicine, we introduce a new recurring feature, Choosing Wisely: Things We Do for No Reason. The series is based on a talk I have delivered for the past 4 years at the annual national meeting of the Society of Hospital Medicine, in which I highlight 4 diagnostic tests, therapies, or other clinical practices that are commonly performed even though they are of low value to our inpatients.

There are many reasons hospitalists order unnecessary tests or treatments, or employ unhelpful clinical practices. Unnecessary testing may occur when we are not familiar with the test itselfthe actual costs of the test, the operating characteristics of the test, or the evidence supporting its usefulness in specific situations. Some tests are ordered unnecessarily because we cannot retrieve usable results from a different hospital or even our own electronic medical records. We may order tests or treatments due to patient expectations, a perceived need to practice defensively, or economic incentives.

Finally, we may simply order tests because of our uncertainty in the absence of data or simply because they are traditional practices (the way we've always done it). Physicians often order tests and treatments and institute clinical practices learned in residency or fellowship training.[1, 2] Local norms and practices influence physician behavior.

We created Things We Do for No Reason (TWDFNR) as a platform for provocative discussions of practices that have become common parts of hospital care but have limited supporting evidence, or even have evidence refuting or justifiably challenging their value. Each article in TWDFNR will describe why the test, treatment, or other clinical practice is commonly employed, why it may not be of high value, in what circumstances it may actually be valuable, and what conclusions can be drawn from the evidence provided. TWDFNR pieces are not systematic reviews or meta‐analyses and do not represent black and white conclusions or clinical practice standards; they are meant as a starting place for research and active discussions among hospitalists and patients.

In many respects, the Choosing Wisely: Things We Do for No Reason series is an extension of the Choosing Wisely campaign created by the American Board of Internal Medicine Foundation. Like Choosing Wisely, we are focusing on individual tests, treatments, and other clinical practices that are not beneficial and are potentially harmful to patients. Practices discussed may not cause significant physical or financial harm at the time they are used, but they may have significant downstream effects.

The Choosing Wisely campaign has brilliantly identified 5 important hospital medicine low‐value practices, and we hope to identify many more. We hope this series will serve as a grassroots effort to uncover more Choosing Wisely‐type practices. As institutions create their own high‐value care committees, the Choosing Wisely: Things We Do for No Reason series can provide possible agenda items, or provide the opportunity for sites to carry out analyses of their own practices to see whether any of the TWDFNR topics provide local opportunities for implementing higher‐value practices.

Although we do not believe that reducing the low‐value practices that will appear in TWDFNR will, alone, solve our wasteful practices, we hope that highlighting them will remind individuals, institutions, and systems that targeting low‐value practices is a responsibility that we all must embrace. We accept that not everyone will agree that the practices we present are low value, but the conversation is important to have. We invite you to take part in the Choosing Wisely: Things We Do for No Reason conversation. Let us know whether you think the practices highlighted are low value or whether you disagree with the conclusions. We welcome unsolicited proposals for series topics submitted as a 500‐word prcis. Send us your prcis or ideas on low‐value adult or pediatric patient practices that we should highlight in this series by emailing us at [email protected].

Disclosure: Nothing to report.

In this issue of the Journal of Hospital Medicine, we introduce a new recurring feature, Choosing Wisely: Things We Do for No Reason. The series is based on a talk I have delivered for the past 4 years at the annual national meeting of the Society of Hospital Medicine, in which I highlight 4 diagnostic tests, therapies, or other clinical practices that are commonly performed even though they are of low value to our inpatients.

There are many reasons hospitalists order unnecessary tests or treatments, or employ unhelpful clinical practices. Unnecessary testing may occur when we are not familiar with the test itselfthe actual costs of the test, the operating characteristics of the test, or the evidence supporting its usefulness in specific situations. Some tests are ordered unnecessarily because we cannot retrieve usable results from a different hospital or even our own electronic medical records. We may order tests or treatments due to patient expectations, a perceived need to practice defensively, or economic incentives.

Finally, we may simply order tests because of our uncertainty in the absence of data or simply because they are traditional practices (the way we've always done it). Physicians often order tests and treatments and institute clinical practices learned in residency or fellowship training.[1, 2] Local norms and practices influence physician behavior.

We created Things We Do for No Reason (TWDFNR) as a platform for provocative discussions of practices that have become common parts of hospital care but have limited supporting evidence, or even have evidence refuting or justifiably challenging their value. Each article in TWDFNR will describe why the test, treatment, or other clinical practice is commonly employed, why it may not be of high value, in what circumstances it may actually be valuable, and what conclusions can be drawn from the evidence provided. TWDFNR pieces are not systematic reviews or meta‐analyses and do not represent black and white conclusions or clinical practice standards; they are meant as a starting place for research and active discussions among hospitalists and patients.

In many respects, the Choosing Wisely: Things We Do for No Reason series is an extension of the Choosing Wisely campaign created by the American Board of Internal Medicine Foundation. Like Choosing Wisely, we are focusing on individual tests, treatments, and other clinical practices that are not beneficial and are potentially harmful to patients. Practices discussed may not cause significant physical or financial harm at the time they are used, but they may have significant downstream effects.

The Choosing Wisely campaign has brilliantly identified 5 important hospital medicine low‐value practices, and we hope to identify many more. We hope this series will serve as a grassroots effort to uncover more Choosing Wisely‐type practices. As institutions create their own high‐value care committees, the Choosing Wisely: Things We Do for No Reason series can provide possible agenda items, or provide the opportunity for sites to carry out analyses of their own practices to see whether any of the TWDFNR topics provide local opportunities for implementing higher‐value practices.

Although we do not believe that reducing the low‐value practices that will appear in TWDFNR will, alone, solve our wasteful practices, we hope that highlighting them will remind individuals, institutions, and systems that targeting low‐value practices is a responsibility that we all must embrace. We accept that not everyone will agree that the practices we present are low value, but the conversation is important to have. We invite you to take part in the Choosing Wisely: Things We Do for No Reason conversation. Let us know whether you think the practices highlighted are low value or whether you disagree with the conclusions. We welcome unsolicited proposals for series topics submitted as a 500‐word prcis. Send us your prcis or ideas on low‐value adult or pediatric patient practices that we should highlight in this series by emailing us at [email protected].

Disclosure: Nothing to report.