User login
Study Overview
Objective: This study evaluated whether a clinical-decision-support (CDS) tool that utilizes a real-time algorithm incorporating patient vital sign data can identify hospitalized patients who can forgo overnight vital sign checks and thus reduce delirium incidence.
Design: This was a parallel randomized clinical trial of adult inpatients admitted to the general medical service of a tertiary care academic medical center in the United States. The trial intervention consisted of a CDS notification in the electronic health record (EHR) that informed the physician if a patient had a high likelihood of nighttime vital signs within the reference ranges based on a logistic regression model of real-time patient data input. This notification provided the physician an opportunity to discontinue nighttime vital sign checks, dismiss the notification for 1 hour, or dismiss the notification until the next day.
Setting and participants: This clinical trial was conducted at the University of California, San Francisco Medical Center from March 11 to November 24, 2019. Participants included physicians who served on the primary team (eg, attending, resident) of 1699 patients on the general medical service who were outside of the intensive care unit (ICU). The hospital encounters were randomized (allocation ratio of 1:1) to sleep promotion vitals CDS (SPV CDS) intervention or usual care.
Main outcome and measures: The primary outcome was delirium as determined by bedside nurse assessment using the Nursing Delirium Screening Scale (Nu-DESC) recorded once per nursing shift. The Nu-DESC is a standardized delirium screening tool that defines delirium with a score ≥2. Secondary outcomes included sleep opportunity (ie, EHR-based sleep metrics that reflected the maximum time between iatrogenic interruptions, such as nighttime vital sign checks) and patient satisfaction (ie, patient satisfaction measured by standardized Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS] survey). Potential balancing outcomes were assessed to ensure that reduced vital sign checks were not causing harms; these included ICU transfers, rapid response calls, and code blue alarms. All analyses were conducted on the basis of intention-to-treat.
Main results: A total of 3025 inpatient encounters were screened and 1930 encounters were randomized (966 SPV CDS intervention; 964 usual care). The randomized encounters consisted of 1699 patients; demographic factors between the 2 trial arms were similar. Specifically, the intervention arm included 566 men (59%) and mean (SD) age was 53 (15) years. The incidence of delirium was similar between the intervention and usual care arms: 108 (11%) vs 123 (13%) (P = .32). Compared to the usual care arm, the intervention arm had a higher mean (SD) number of sleep opportunity hours per night (4.95 [1.45] vs 4.57 [1.30], P < .001) and fewer nighttime vital sign checks (0.97 [0.95] vs 1.41 [0.86], P < .001). The post-discharge HCAHPS survey measuring patient satisfaction was completed by only 5% of patients (53 intervention, 49 usual care), and survey results were similar between the 2 arms (P = .86). In addition, safety outcomes including ICU transfers (49 [5%] vs 47 [5%], P = .92), rapid response calls (68 [7%] vs 55 [6%], P = .27), and code blue alarms (2 [0.2%] vs 9 [0.9%], P = .07) were similar between the study arms.
Conclusion: In this randomized clinical trial, a CDS tool utilizing a real-time prediction algorithm embedded in EHR did not reduce the incidence of delirium in hospitalized patients. However, this SPV CDS intervention helped physicians identify clinically stable patients who can forgo routine nighttime vital sign checks and facilitated greater opportunity for patients to sleep. These findings suggest that augmenting physician judgment using a real-time prediction algorithm can help to improve sleep opportunity without an accompanying increased risk of clinical decompensation during acute care.
Commentary
High-quality sleep is fundamental to health and well-being. Sleep deprivation and disorders are associated with many adverse health outcomes, including increased risks for obesity, diabetes, hypertension, myocardial infarction, and depression.1 In hospitalized patients who are acutely ill, restorative sleep is critical to facilitating recovery. However, poor sleep is exceedingly common in hospitalized patients and is associated with deleterious outcomes, such as high blood pressure, hyperglycemia, and delirium.2,3 Moreover, some of these adverse sleep-induced cardiometabolic outcomes, as well as sleep disruption itself, may persist after hospital discharge.4 Factors that precipitate interrupted sleep during hospitalization include iatrogenic causes such as frequent vital sign checks, nighttime procedures or early morning blood draws, and environmental factors such as loud ambient noise.3 Thus, a potential intervention to improve sleep quality in the hospital is to reduce nighttime interruptions such as frequent vital sign checks.
In the current study, Najafi and colleagues conducted a randomized trial to evaluate whether a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, can be utilized to identify patients in whom vital sign checks can be safely discontinued at nighttime. The authors found a modest but statistically significant reduction in the number of nighttime vital sign checks in patients who underwent the SPV CDS intervention, and a corresponding higher sleep opportunity per night in those who received the intervention. Importantly, this reduction in nighttime vital sign checks did not cause a higher risk of clinical decompensation as measured by ICU transfers, rapid response calls, or code blue alarms. Thus, the results demonstrated the feasibility of using a real-time, patient data-driven CDS tool to augment physician judgment in managing sleep disruption, an important hospital-associated stressor and a common hazard of hospitalization in older patients.
Delirium is a common clinical problem in hospitalized older patients that is associated with prolonged hospitalization, functional and cognitive decline, institutionalization, death, and increased health care costs.5 Despite a potential benefit of SPV CDS intervention in reducing vital sign checks and increasing sleep opportunity, this intervention did not reduce the incidence of delirium in hospitalized patients. This finding is not surprising given that delirium has a multifactorial etiology (eg, metabolic derangements, infections, medication side effects and drug toxicity, hospital environment). A small modification in nighttime vital sign checks and sleep opportunity may have limited impact on optimizing sleep quality and does not address other risk factors for delirium. As such, a multicomponent nonpharmacologic approach that includes sleep enhancement, early mobilization, feeding assistance, fluid repletion, infection prevention, and other interventions should guide delirium prevention in the hospital setting. The SPV CDS intervention may play a role in the delivery of a multifaceted, nonpharmacologic delirium prevention intervention in high-risk individuals.
Sleep disruption is one of the multiple hazards of hospitalization frequently experience by hospitalized older patients. Other hazards, or hospital-associated stressors, include mobility restriction (eg, physical restraints such as urinary catheters and intravenous lines, bed elevation and rails), malnourishment and dehydration (eg, frequent use of no-food-by-mouth order, lack of easy access to hydration), and pain (eg, poor pain control). Extended exposures to these stressors may lead to a maladaptive state called allostatic overload that transiently increases vulnerability to post-hospitalization adverse events, including emergency department use, hospital readmission, or death (ie, post-hospital syndrome).6 Thus, the optimization of sleep during hospitalization in vulnerable patients may have benefits that extend beyond delirium prevention. It is perceivable that a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, may be applied to reduce some of these hazards of hospitalization in addition to improving sleep opportunity.
Applications for Clinical Practice
Findings from the current study indicate that a CDS tool embedded in EHR that utilizes a real-time prediction algorithm of patient data may help to safely improve sleep opportunity in hospitalized patients. The participants in the current study were relatively young (53 [15] years). Given that age is a risk factor for delirium, the effects of this intervention on delirium prevention in the most susceptible population (ie, those over the age of 65) remain unknown and further investigation is warranted. Additional studies are needed to determine whether this approach yields similar results in geriatric patients and improves clinical outcomes.
—Fred Ko, MD
1. Institute of Medicine (US) Committee on Sleep Medicine and Research. Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. Colten HR, Altevogt BM, editors. National Academies Press (US); 2006.
2. Pilkington S. Causes and consequences of sleep deprivation in hospitalised patients. Nurs Stand. 2013;27(49):350-342. doi:10.7748/ns2013.08.27.49.35.e7649
3. Stewart NH, Arora VM. Sleep in hospitalized older adults. Sleep Med Clin. 2018;13(1):127-135. doi:10.1016/j.jsmc.2017.09.012
4. Altman MT, Knauert MP, Pisani MA. Sleep disturbance after hospitalization and critical illness: a systematic review. Ann Am Thorac Soc. 2017;14(9):1457-1468. doi:10.1513/AnnalsATS.201702-148SR
5. Oh ES, Fong TG, Hshieh TT, Inouye SK. Delirium in older persons: advances in diagnosis and treatment. JAMA. 2017;318(12):1161-1174. doi:10.1001/jama.2017.12067
6. Goldwater DS, Dharmarajan K, McEwan BS, Krumholz HM. Is posthospital syndrome a result of hospitalization-induced allostatic overload? J Hosp Med. 2018;13(5). doi:10.12788/jhm.2986
Study Overview
Objective: This study evaluated whether a clinical-decision-support (CDS) tool that utilizes a real-time algorithm incorporating patient vital sign data can identify hospitalized patients who can forgo overnight vital sign checks and thus reduce delirium incidence.
Design: This was a parallel randomized clinical trial of adult inpatients admitted to the general medical service of a tertiary care academic medical center in the United States. The trial intervention consisted of a CDS notification in the electronic health record (EHR) that informed the physician if a patient had a high likelihood of nighttime vital signs within the reference ranges based on a logistic regression model of real-time patient data input. This notification provided the physician an opportunity to discontinue nighttime vital sign checks, dismiss the notification for 1 hour, or dismiss the notification until the next day.
Setting and participants: This clinical trial was conducted at the University of California, San Francisco Medical Center from March 11 to November 24, 2019. Participants included physicians who served on the primary team (eg, attending, resident) of 1699 patients on the general medical service who were outside of the intensive care unit (ICU). The hospital encounters were randomized (allocation ratio of 1:1) to sleep promotion vitals CDS (SPV CDS) intervention or usual care.
Main outcome and measures: The primary outcome was delirium as determined by bedside nurse assessment using the Nursing Delirium Screening Scale (Nu-DESC) recorded once per nursing shift. The Nu-DESC is a standardized delirium screening tool that defines delirium with a score ≥2. Secondary outcomes included sleep opportunity (ie, EHR-based sleep metrics that reflected the maximum time between iatrogenic interruptions, such as nighttime vital sign checks) and patient satisfaction (ie, patient satisfaction measured by standardized Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS] survey). Potential balancing outcomes were assessed to ensure that reduced vital sign checks were not causing harms; these included ICU transfers, rapid response calls, and code blue alarms. All analyses were conducted on the basis of intention-to-treat.
Main results: A total of 3025 inpatient encounters were screened and 1930 encounters were randomized (966 SPV CDS intervention; 964 usual care). The randomized encounters consisted of 1699 patients; demographic factors between the 2 trial arms were similar. Specifically, the intervention arm included 566 men (59%) and mean (SD) age was 53 (15) years. The incidence of delirium was similar between the intervention and usual care arms: 108 (11%) vs 123 (13%) (P = .32). Compared to the usual care arm, the intervention arm had a higher mean (SD) number of sleep opportunity hours per night (4.95 [1.45] vs 4.57 [1.30], P < .001) and fewer nighttime vital sign checks (0.97 [0.95] vs 1.41 [0.86], P < .001). The post-discharge HCAHPS survey measuring patient satisfaction was completed by only 5% of patients (53 intervention, 49 usual care), and survey results were similar between the 2 arms (P = .86). In addition, safety outcomes including ICU transfers (49 [5%] vs 47 [5%], P = .92), rapid response calls (68 [7%] vs 55 [6%], P = .27), and code blue alarms (2 [0.2%] vs 9 [0.9%], P = .07) were similar between the study arms.
Conclusion: In this randomized clinical trial, a CDS tool utilizing a real-time prediction algorithm embedded in EHR did not reduce the incidence of delirium in hospitalized patients. However, this SPV CDS intervention helped physicians identify clinically stable patients who can forgo routine nighttime vital sign checks and facilitated greater opportunity for patients to sleep. These findings suggest that augmenting physician judgment using a real-time prediction algorithm can help to improve sleep opportunity without an accompanying increased risk of clinical decompensation during acute care.
Commentary
High-quality sleep is fundamental to health and well-being. Sleep deprivation and disorders are associated with many adverse health outcomes, including increased risks for obesity, diabetes, hypertension, myocardial infarction, and depression.1 In hospitalized patients who are acutely ill, restorative sleep is critical to facilitating recovery. However, poor sleep is exceedingly common in hospitalized patients and is associated with deleterious outcomes, such as high blood pressure, hyperglycemia, and delirium.2,3 Moreover, some of these adverse sleep-induced cardiometabolic outcomes, as well as sleep disruption itself, may persist after hospital discharge.4 Factors that precipitate interrupted sleep during hospitalization include iatrogenic causes such as frequent vital sign checks, nighttime procedures or early morning blood draws, and environmental factors such as loud ambient noise.3 Thus, a potential intervention to improve sleep quality in the hospital is to reduce nighttime interruptions such as frequent vital sign checks.
In the current study, Najafi and colleagues conducted a randomized trial to evaluate whether a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, can be utilized to identify patients in whom vital sign checks can be safely discontinued at nighttime. The authors found a modest but statistically significant reduction in the number of nighttime vital sign checks in patients who underwent the SPV CDS intervention, and a corresponding higher sleep opportunity per night in those who received the intervention. Importantly, this reduction in nighttime vital sign checks did not cause a higher risk of clinical decompensation as measured by ICU transfers, rapid response calls, or code blue alarms. Thus, the results demonstrated the feasibility of using a real-time, patient data-driven CDS tool to augment physician judgment in managing sleep disruption, an important hospital-associated stressor and a common hazard of hospitalization in older patients.
Delirium is a common clinical problem in hospitalized older patients that is associated with prolonged hospitalization, functional and cognitive decline, institutionalization, death, and increased health care costs.5 Despite a potential benefit of SPV CDS intervention in reducing vital sign checks and increasing sleep opportunity, this intervention did not reduce the incidence of delirium in hospitalized patients. This finding is not surprising given that delirium has a multifactorial etiology (eg, metabolic derangements, infections, medication side effects and drug toxicity, hospital environment). A small modification in nighttime vital sign checks and sleep opportunity may have limited impact on optimizing sleep quality and does not address other risk factors for delirium. As such, a multicomponent nonpharmacologic approach that includes sleep enhancement, early mobilization, feeding assistance, fluid repletion, infection prevention, and other interventions should guide delirium prevention in the hospital setting. The SPV CDS intervention may play a role in the delivery of a multifaceted, nonpharmacologic delirium prevention intervention in high-risk individuals.
Sleep disruption is one of the multiple hazards of hospitalization frequently experience by hospitalized older patients. Other hazards, or hospital-associated stressors, include mobility restriction (eg, physical restraints such as urinary catheters and intravenous lines, bed elevation and rails), malnourishment and dehydration (eg, frequent use of no-food-by-mouth order, lack of easy access to hydration), and pain (eg, poor pain control). Extended exposures to these stressors may lead to a maladaptive state called allostatic overload that transiently increases vulnerability to post-hospitalization adverse events, including emergency department use, hospital readmission, or death (ie, post-hospital syndrome).6 Thus, the optimization of sleep during hospitalization in vulnerable patients may have benefits that extend beyond delirium prevention. It is perceivable that a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, may be applied to reduce some of these hazards of hospitalization in addition to improving sleep opportunity.
Applications for Clinical Practice
Findings from the current study indicate that a CDS tool embedded in EHR that utilizes a real-time prediction algorithm of patient data may help to safely improve sleep opportunity in hospitalized patients. The participants in the current study were relatively young (53 [15] years). Given that age is a risk factor for delirium, the effects of this intervention on delirium prevention in the most susceptible population (ie, those over the age of 65) remain unknown and further investigation is warranted. Additional studies are needed to determine whether this approach yields similar results in geriatric patients and improves clinical outcomes.
—Fred Ko, MD
Study Overview
Objective: This study evaluated whether a clinical-decision-support (CDS) tool that utilizes a real-time algorithm incorporating patient vital sign data can identify hospitalized patients who can forgo overnight vital sign checks and thus reduce delirium incidence.
Design: This was a parallel randomized clinical trial of adult inpatients admitted to the general medical service of a tertiary care academic medical center in the United States. The trial intervention consisted of a CDS notification in the electronic health record (EHR) that informed the physician if a patient had a high likelihood of nighttime vital signs within the reference ranges based on a logistic regression model of real-time patient data input. This notification provided the physician an opportunity to discontinue nighttime vital sign checks, dismiss the notification for 1 hour, or dismiss the notification until the next day.
Setting and participants: This clinical trial was conducted at the University of California, San Francisco Medical Center from March 11 to November 24, 2019. Participants included physicians who served on the primary team (eg, attending, resident) of 1699 patients on the general medical service who were outside of the intensive care unit (ICU). The hospital encounters were randomized (allocation ratio of 1:1) to sleep promotion vitals CDS (SPV CDS) intervention or usual care.
Main outcome and measures: The primary outcome was delirium as determined by bedside nurse assessment using the Nursing Delirium Screening Scale (Nu-DESC) recorded once per nursing shift. The Nu-DESC is a standardized delirium screening tool that defines delirium with a score ≥2. Secondary outcomes included sleep opportunity (ie, EHR-based sleep metrics that reflected the maximum time between iatrogenic interruptions, such as nighttime vital sign checks) and patient satisfaction (ie, patient satisfaction measured by standardized Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS] survey). Potential balancing outcomes were assessed to ensure that reduced vital sign checks were not causing harms; these included ICU transfers, rapid response calls, and code blue alarms. All analyses were conducted on the basis of intention-to-treat.
Main results: A total of 3025 inpatient encounters were screened and 1930 encounters were randomized (966 SPV CDS intervention; 964 usual care). The randomized encounters consisted of 1699 patients; demographic factors between the 2 trial arms were similar. Specifically, the intervention arm included 566 men (59%) and mean (SD) age was 53 (15) years. The incidence of delirium was similar between the intervention and usual care arms: 108 (11%) vs 123 (13%) (P = .32). Compared to the usual care arm, the intervention arm had a higher mean (SD) number of sleep opportunity hours per night (4.95 [1.45] vs 4.57 [1.30], P < .001) and fewer nighttime vital sign checks (0.97 [0.95] vs 1.41 [0.86], P < .001). The post-discharge HCAHPS survey measuring patient satisfaction was completed by only 5% of patients (53 intervention, 49 usual care), and survey results were similar between the 2 arms (P = .86). In addition, safety outcomes including ICU transfers (49 [5%] vs 47 [5%], P = .92), rapid response calls (68 [7%] vs 55 [6%], P = .27), and code blue alarms (2 [0.2%] vs 9 [0.9%], P = .07) were similar between the study arms.
Conclusion: In this randomized clinical trial, a CDS tool utilizing a real-time prediction algorithm embedded in EHR did not reduce the incidence of delirium in hospitalized patients. However, this SPV CDS intervention helped physicians identify clinically stable patients who can forgo routine nighttime vital sign checks and facilitated greater opportunity for patients to sleep. These findings suggest that augmenting physician judgment using a real-time prediction algorithm can help to improve sleep opportunity without an accompanying increased risk of clinical decompensation during acute care.
Commentary
High-quality sleep is fundamental to health and well-being. Sleep deprivation and disorders are associated with many adverse health outcomes, including increased risks for obesity, diabetes, hypertension, myocardial infarction, and depression.1 In hospitalized patients who are acutely ill, restorative sleep is critical to facilitating recovery. However, poor sleep is exceedingly common in hospitalized patients and is associated with deleterious outcomes, such as high blood pressure, hyperglycemia, and delirium.2,3 Moreover, some of these adverse sleep-induced cardiometabolic outcomes, as well as sleep disruption itself, may persist after hospital discharge.4 Factors that precipitate interrupted sleep during hospitalization include iatrogenic causes such as frequent vital sign checks, nighttime procedures or early morning blood draws, and environmental factors such as loud ambient noise.3 Thus, a potential intervention to improve sleep quality in the hospital is to reduce nighttime interruptions such as frequent vital sign checks.
In the current study, Najafi and colleagues conducted a randomized trial to evaluate whether a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, can be utilized to identify patients in whom vital sign checks can be safely discontinued at nighttime. The authors found a modest but statistically significant reduction in the number of nighttime vital sign checks in patients who underwent the SPV CDS intervention, and a corresponding higher sleep opportunity per night in those who received the intervention. Importantly, this reduction in nighttime vital sign checks did not cause a higher risk of clinical decompensation as measured by ICU transfers, rapid response calls, or code blue alarms. Thus, the results demonstrated the feasibility of using a real-time, patient data-driven CDS tool to augment physician judgment in managing sleep disruption, an important hospital-associated stressor and a common hazard of hospitalization in older patients.
Delirium is a common clinical problem in hospitalized older patients that is associated with prolonged hospitalization, functional and cognitive decline, institutionalization, death, and increased health care costs.5 Despite a potential benefit of SPV CDS intervention in reducing vital sign checks and increasing sleep opportunity, this intervention did not reduce the incidence of delirium in hospitalized patients. This finding is not surprising given that delirium has a multifactorial etiology (eg, metabolic derangements, infections, medication side effects and drug toxicity, hospital environment). A small modification in nighttime vital sign checks and sleep opportunity may have limited impact on optimizing sleep quality and does not address other risk factors for delirium. As such, a multicomponent nonpharmacologic approach that includes sleep enhancement, early mobilization, feeding assistance, fluid repletion, infection prevention, and other interventions should guide delirium prevention in the hospital setting. The SPV CDS intervention may play a role in the delivery of a multifaceted, nonpharmacologic delirium prevention intervention in high-risk individuals.
Sleep disruption is one of the multiple hazards of hospitalization frequently experience by hospitalized older patients. Other hazards, or hospital-associated stressors, include mobility restriction (eg, physical restraints such as urinary catheters and intravenous lines, bed elevation and rails), malnourishment and dehydration (eg, frequent use of no-food-by-mouth order, lack of easy access to hydration), and pain (eg, poor pain control). Extended exposures to these stressors may lead to a maladaptive state called allostatic overload that transiently increases vulnerability to post-hospitalization adverse events, including emergency department use, hospital readmission, or death (ie, post-hospital syndrome).6 Thus, the optimization of sleep during hospitalization in vulnerable patients may have benefits that extend beyond delirium prevention. It is perceivable that a CDS tool embedded in EHR, powered by a real-time prediction algorithm of patient data, may be applied to reduce some of these hazards of hospitalization in addition to improving sleep opportunity.
Applications for Clinical Practice
Findings from the current study indicate that a CDS tool embedded in EHR that utilizes a real-time prediction algorithm of patient data may help to safely improve sleep opportunity in hospitalized patients. The participants in the current study were relatively young (53 [15] years). Given that age is a risk factor for delirium, the effects of this intervention on delirium prevention in the most susceptible population (ie, those over the age of 65) remain unknown and further investigation is warranted. Additional studies are needed to determine whether this approach yields similar results in geriatric patients and improves clinical outcomes.
—Fred Ko, MD
1. Institute of Medicine (US) Committee on Sleep Medicine and Research. Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. Colten HR, Altevogt BM, editors. National Academies Press (US); 2006.
2. Pilkington S. Causes and consequences of sleep deprivation in hospitalised patients. Nurs Stand. 2013;27(49):350-342. doi:10.7748/ns2013.08.27.49.35.e7649
3. Stewart NH, Arora VM. Sleep in hospitalized older adults. Sleep Med Clin. 2018;13(1):127-135. doi:10.1016/j.jsmc.2017.09.012
4. Altman MT, Knauert MP, Pisani MA. Sleep disturbance after hospitalization and critical illness: a systematic review. Ann Am Thorac Soc. 2017;14(9):1457-1468. doi:10.1513/AnnalsATS.201702-148SR
5. Oh ES, Fong TG, Hshieh TT, Inouye SK. Delirium in older persons: advances in diagnosis and treatment. JAMA. 2017;318(12):1161-1174. doi:10.1001/jama.2017.12067
6. Goldwater DS, Dharmarajan K, McEwan BS, Krumholz HM. Is posthospital syndrome a result of hospitalization-induced allostatic overload? J Hosp Med. 2018;13(5). doi:10.12788/jhm.2986
1. Institute of Medicine (US) Committee on Sleep Medicine and Research. Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. Colten HR, Altevogt BM, editors. National Academies Press (US); 2006.
2. Pilkington S. Causes and consequences of sleep deprivation in hospitalised patients. Nurs Stand. 2013;27(49):350-342. doi:10.7748/ns2013.08.27.49.35.e7649
3. Stewart NH, Arora VM. Sleep in hospitalized older adults. Sleep Med Clin. 2018;13(1):127-135. doi:10.1016/j.jsmc.2017.09.012
4. Altman MT, Knauert MP, Pisani MA. Sleep disturbance after hospitalization and critical illness: a systematic review. Ann Am Thorac Soc. 2017;14(9):1457-1468. doi:10.1513/AnnalsATS.201702-148SR
5. Oh ES, Fong TG, Hshieh TT, Inouye SK. Delirium in older persons: advances in diagnosis and treatment. JAMA. 2017;318(12):1161-1174. doi:10.1001/jama.2017.12067
6. Goldwater DS, Dharmarajan K, McEwan BS, Krumholz HM. Is posthospital syndrome a result of hospitalization-induced allostatic overload? J Hosp Med. 2018;13(5). doi:10.12788/jhm.2986