Babak Mokhlesi, MD, MSc

Although sleep is critical to patient recovery in the hospital, hospitalization is not restful,^1,2 and inpatient sleep deprivation has been linked to poor health outcomes.^1-4 The American Academy of Nursing’s Choosing Wisely^® campaign recommends nurses reduce unnecessary nocturnal care.⁵ However, interventions to improve inpatient sleep are not widely implemented.⁶ Targeting routine disruptions, such as overnight vital signs, by changing default settings in the electronic health record (EHR)with “nudges” could be a cost-effective strategy to improve inpatient sleep.^4,7

We created Sleep for Inpatients: Empowering Staff to Act (SIESTA), which pairs nudges in the EHR with interprofessional education and empowerment,⁸ and tested its effectiveness on objectively and subjectively measured nocturnal sleep disruptors.

Study Design

Two 18-room University of Chicago Medicine general-medicine units were used in this prospective study. The SIESTA-enhanced unit underwent the full sleep intervention: nursing education and empowerment, physician education, and EHR changes. The standard unit did not receive nursing interventions but received all other forms of intervention. Because physicians simultaneously cared for patients on both units, all internal medicine residents and hospitalists received the same education. The study population included physicians, nurses, and awake English-speaking patients who were cognitively intact and admitted to these two units. The University of Chicago Institutional Review Board approved this study (12-1766; 16685B).

Development of SIESTA

To develop SIESTA, patients were surveyed, and focus groups of staff were conducted; overnight vitals, medications, and phlebotomy were identified as major barriers to patient sleep.⁹ We found that physicians did not know how to change the default vital signs order “every 4 hours” or how to batch-order morning phlebotomy at a time other than 4:00 am. Nurses reported having to wake patients up at 1:00 am for q8h subcutaneous heparin.

Behavioral Nudges

The SIESTA team worked with clinical informaticists to change the default orders in Epic^TM (Epic Systems Corporation, 2017, Verona, Wisconsin) in September 2015 so that physicians would be asked, “Continue vital signs throughout the night?”¹⁰ Previously, this question was marked “Yes” by default and hidden. While the default protocol for heparin q8h was maintained, heparin q12h (9:00 am and 9:00 pm) was introduced as an option, since q12h heparin is equally effective for VTE prophylaxis.¹¹ Laboratory ordering was streamlined so that physicians could batch-order laboratory draws at 6:00 am or 10:00 pm.

SIESTA Physician Education

We created a 20-minute presentation on the consequences and causes of in-hospital sleep deprivation and evidence-based behavioral modification. We distributed pocket cards describing the mnemonic SIESTA (Screen patients for sleep disorders, Instruct patients on sleep hygiene, Eliminate disruptions, Shut doors, Treat pain, and Alarm and noise control). Physicians were instructed to consider forgoing overnight vitals, using clinical judgment to identify stable patients, use a sleep-promoting VTE prophylaxis option, and order daily labs at 10:00 pm or 6:00 am. An online educational module was sent to staff who missed live sessions due to days off.

SIESTA-Enhanced Unit

In the SIESTA-enhanced unit, nurses received education using pocket cards and were coached to collaborate with physicians to implement sleep-friendly orders. Customized signage depicting empowered nurses advocating for patients was posted near the huddle board. Because these nurses suggested adding SIESTA to the nurses’ ongoing daily huddles at 4:00 pm and 3:00 am, beginning on January 1, 2016, nurses were asked to identify at least two stable patients for sleep-friendly orders at the huddle. Night nurses incorporated SIESTA into their handoff to day nurses for eligible patients. Day nurses would then call physicians to advocate changing of orders.

Data Collection

Objectively Measured Sleep Disruptors

Adoption of SIESTA orders from March 2015 to March 2016 was assessed with a monthly Epic^TM Clarity report. From August 1, 2015 to April 1, 2016, nocturnal room entries were recorded using the GOJO SMARTLINK^TM Hand Hygiene system (GOJO Industries Inc., 2017, Akron, Ohio). This system includes two components: the hand-sanitizer dispensers, which track dispenses (numerator), and door-mounted Activity Counters, which use heat sensors that react to body heat emitted by a person passing through the doorway (denominator for hand-hygiene compliance). For our analysis, we only used Activity Counter data, which count room entries and exits, regardless of whether sanitizer was dispensed.

Patient-Reported Nighttime Sleep Disruptions

From June 2015 to March 2016, research assistants administered a 10-item Potential Hospital Sleep Disruptions and Noises Questionnaire (PHSDNQ) to patients in both units. Responses to this questionnaire correlate with actigraphy-based sleep measurements.^9,12,13 Surveys were administered every other weekday to patients available to participate (eg, willing to participate, on the unit, awake). Survey data were stored on the REDCap Database (Version 6.14.0; Vanderbilt University, 2016, Nashville, Tennessee). Pre- and post-intervention Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) “top-box ratings” for percent quiet at night and percent pain well controlled were also compared.

Data Analysis

Objectively Measured Potential Sleep Disruptors

The proportion of sleep-friendly orders was analyzed using a two-sample test for proportions pre-post for the SIESTA-enhanced and standard units. The difference in use of SIESTA orders between units was analyzed via multivariable logistic regression, testing for independent associations between post-period, SIESTA-enhanced unit, and an interaction term (post-period × SIESTA unit) on use of sleep-friendly orders.

Room entries per night (11:00 pm–7:00 am) were analyzed via single-group interrupted time-series. Multiple Activity Counter entries within three minutes were counted as a single room entry. In addition, the pre-post cutoff was set to 7:00 am, September 8, 2015; after the SIESTA launch, a second cutoff marking when SIESTA was added to the nurses’ MDI Huddle was added at 7:00 am, January 1, 2016.

Patient-Reported Nighttime Sleep Disruptions

Per prior studies, we defined a score 2 or higher as “sleep disruption.”⁹ Differences between units were evaluated via multivariable logistic regression to examine the association between the interaction of post-period × SIESTA-enhanced unit and odds of not reporting a sleep disruption. Significance was denoted as P = .05.

Between March 2015 and March 2016, 1,083 general-medicine patients were admitted to the SIESTA-enhanced and standard units (Table).

jhm014010038_t1.jpg

Nocturnal Orders

From March 2015 to March 2016, 1,669 Epic^TM general medicine orders were reviewed (Figure). In the SIESTA-enhanced unit, the mean percentage of sleep-friendly orders rose for both vital signs (+31% [95% CI = 25%, 36%]; P < .001, n_pre = 306, n_post = 306] and VTE prophylaxis (+28% [95% CI = 18%, 37%]; P < .001, n_pre = 158, n_post = 173]. Similar changes were observed in the standard unit for sleep-friendly vital signs (+20% [95% CI = 14%, 25%]; P < .001, n_pre = 252, n_post = 219) and VTE prophylaxis (+16% [95% CI = 6%, 25%]; P = .002, n_pre = 130, n_post = 125). Differences between the two units were not statistically significant, and no significant change in timing of laboratory orders postintervention was found.

jhm014010038_f1.jpg

Nighttime Room Entries

Immediately after SIESTA launch, an average decrease of 114 total entries/night were noted in the SIESTA-enhanced unit, ([95% CI = −138, −91]; P < .001), corresponding to a 44% reduction (−6.3 entries/room) from the mean of 14.3 entries per patient room at baseline (Figure). No statistically significant change was seen in the standard unit. After SIESTA was incorporated into nursing huddles, total disruptions/night decreased by 1.31 disruptions/night ([95% CI = −1.64, −0.98]; P < .001) in the SIESTA-enhanced unit; by comparison, no significant changes were observed in the standard unit.

Patient-Reported Nighttime Sleep Disruptions

Between June 2015 and March 2016, 201 patient surveys were collected. A significant interaction was observed between the SIESTA-enhanced unit and post-period, and patients in the SIESTA-enhanced unit were more likely to report not being disrupted by medications (OR 4.08 [95% CI = 1.13–14.07]; P = .031) and vital signs (OR 3.35 [95% CI = 1.00–11.2]; P = .05) than those in the standard unit. HCAHPS top-box scores for the SIESTA unit increased by 7% for the “Quiet at night” category and 9% for the “Pain well controlled” category; by comparison, no major changes (>5%) were observed in the standard unit.

The present SIESTA intervention demonstrated that physician education coupled with EHR default changes are associated with a significant reduction in orders for overnight vital signs and medication administration in both units. However, addition of nursing education and empowerment in the SIESTA-enhanced unit was associated with fewer nocturnal room entries and improvements in patient-reported outcomes compared with those in the standard unit.

This study presents several implications for hospital initiatives aiming to improve patient sleep.¹⁴ Our study is consistent with other research highlighting the hypothesis that altering the default settings of EHR systems can influence physician behavior in a sustainable manner.¹⁵ However, our study also finds that, even when sleep-friendly orders are present, creating a sleep-friendly environment likely depends on the unit-based nurses championing the cause. While the initial decrease in nocturnal room entries post-SIESTA eventually faded, sustainable changes were observed only after SIESTA was added to nursing huddles, which illustrates the importance of using multiple methods to nudge staff.

Our study includes a number of limitations. It is not a randomized controlled trial, we cannot assume causality, and contamination was assumed, as residents and hospitalists worked in both units. Our single-site study may not be generalizable. Low HCAHPS response rates (10%-20%) also prevent demonstration of statistically significant differences. Finally, our convenience sampling strategy means not all inpatients were surveyed, and objective sleep duration was not measured.

In summary, at the University of Chicago, SIESTA could be associated with adoption of sleep-friendly vitals and medication orders, a decrease in nighttime room entries, and improved patient experience.

Disclosures

The authors have nothing to disclose.

Funding

This study was funded by the National Institute on Aging (NIA Grant No. T35AG029795) and the National Heart, Lung, and Blood Institute (NHLBI Grant Nos. R25HL116372 and K24HL136859).

Although sleep is critical to patient recovery in the hospital, hospitalization is not restful,^1,2 and inpatient sleep deprivation has been linked to poor health outcomes.^1-4 The American Academy of Nursing’s Choosing Wisely^® campaign recommends nurses reduce unnecessary nocturnal care.⁵ However, interventions to improve inpatient sleep are not widely implemented.⁶ Targeting routine disruptions, such as overnight vital signs, by changing default settings in the electronic health record (EHR)with “nudges” could be a cost-effective strategy to improve inpatient sleep.^4,7

We created Sleep for Inpatients: Empowering Staff to Act (SIESTA), which pairs nudges in the EHR with interprofessional education and empowerment,⁸ and tested its effectiveness on objectively and subjectively measured nocturnal sleep disruptors.

Study Design

Two 18-room University of Chicago Medicine general-medicine units were used in this prospective study. The SIESTA-enhanced unit underwent the full sleep intervention: nursing education and empowerment, physician education, and EHR changes. The standard unit did not receive nursing interventions but received all other forms of intervention. Because physicians simultaneously cared for patients on both units, all internal medicine residents and hospitalists received the same education. The study population included physicians, nurses, and awake English-speaking patients who were cognitively intact and admitted to these two units. The University of Chicago Institutional Review Board approved this study (12-1766; 16685B).

Development of SIESTA

To develop SIESTA, patients were surveyed, and focus groups of staff were conducted; overnight vitals, medications, and phlebotomy were identified as major barriers to patient sleep.⁹ We found that physicians did not know how to change the default vital signs order “every 4 hours” or how to batch-order morning phlebotomy at a time other than 4:00 am. Nurses reported having to wake patients up at 1:00 am for q8h subcutaneous heparin.

Behavioral Nudges

The SIESTA team worked with clinical informaticists to change the default orders in Epic^TM (Epic Systems Corporation, 2017, Verona, Wisconsin) in September 2015 so that physicians would be asked, “Continue vital signs throughout the night?”¹⁰ Previously, this question was marked “Yes” by default and hidden. While the default protocol for heparin q8h was maintained, heparin q12h (9:00 am and 9:00 pm) was introduced as an option, since q12h heparin is equally effective for VTE prophylaxis.¹¹ Laboratory ordering was streamlined so that physicians could batch-order laboratory draws at 6:00 am or 10:00 pm.

SIESTA Physician Education

We created a 20-minute presentation on the consequences and causes of in-hospital sleep deprivation and evidence-based behavioral modification. We distributed pocket cards describing the mnemonic SIESTA (Screen patients for sleep disorders, Instruct patients on sleep hygiene, Eliminate disruptions, Shut doors, Treat pain, and Alarm and noise control). Physicians were instructed to consider forgoing overnight vitals, using clinical judgment to identify stable patients, use a sleep-promoting VTE prophylaxis option, and order daily labs at 10:00 pm or 6:00 am. An online educational module was sent to staff who missed live sessions due to days off.

SIESTA-Enhanced Unit

In the SIESTA-enhanced unit, nurses received education using pocket cards and were coached to collaborate with physicians to implement sleep-friendly orders. Customized signage depicting empowered nurses advocating for patients was posted near the huddle board. Because these nurses suggested adding SIESTA to the nurses’ ongoing daily huddles at 4:00 pm and 3:00 am, beginning on January 1, 2016, nurses were asked to identify at least two stable patients for sleep-friendly orders at the huddle. Night nurses incorporated SIESTA into their handoff to day nurses for eligible patients. Day nurses would then call physicians to advocate changing of orders.

Data Collection

Objectively Measured Sleep Disruptors

Adoption of SIESTA orders from March 2015 to March 2016 was assessed with a monthly Epic^TM Clarity report. From August 1, 2015 to April 1, 2016, nocturnal room entries were recorded using the GOJO SMARTLINK^TM Hand Hygiene system (GOJO Industries Inc., 2017, Akron, Ohio). This system includes two components: the hand-sanitizer dispensers, which track dispenses (numerator), and door-mounted Activity Counters, which use heat sensors that react to body heat emitted by a person passing through the doorway (denominator for hand-hygiene compliance). For our analysis, we only used Activity Counter data, which count room entries and exits, regardless of whether sanitizer was dispensed.

Patient-Reported Nighttime Sleep Disruptions

From June 2015 to March 2016, research assistants administered a 10-item Potential Hospital Sleep Disruptions and Noises Questionnaire (PHSDNQ) to patients in both units. Responses to this questionnaire correlate with actigraphy-based sleep measurements.^9,12,13 Surveys were administered every other weekday to patients available to participate (eg, willing to participate, on the unit, awake). Survey data were stored on the REDCap Database (Version 6.14.0; Vanderbilt University, 2016, Nashville, Tennessee). Pre- and post-intervention Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) “top-box ratings” for percent quiet at night and percent pain well controlled were also compared.

Data Analysis

Objectively Measured Potential Sleep Disruptors

The proportion of sleep-friendly orders was analyzed using a two-sample test for proportions pre-post for the SIESTA-enhanced and standard units. The difference in use of SIESTA orders between units was analyzed via multivariable logistic regression, testing for independent associations between post-period, SIESTA-enhanced unit, and an interaction term (post-period × SIESTA unit) on use of sleep-friendly orders.

Room entries per night (11:00 pm–7:00 am) were analyzed via single-group interrupted time-series. Multiple Activity Counter entries within three minutes were counted as a single room entry. In addition, the pre-post cutoff was set to 7:00 am, September 8, 2015; after the SIESTA launch, a second cutoff marking when SIESTA was added to the nurses’ MDI Huddle was added at 7:00 am, January 1, 2016.

Patient-Reported Nighttime Sleep Disruptions

Per prior studies, we defined a score 2 or higher as “sleep disruption.”⁹ Differences between units were evaluated via multivariable logistic regression to examine the association between the interaction of post-period × SIESTA-enhanced unit and odds of not reporting a sleep disruption. Significance was denoted as P = .05.

Between March 2015 and March 2016, 1,083 general-medicine patients were admitted to the SIESTA-enhanced and standard units (Table).

jhm014010038_t1.jpg

Nocturnal Orders

From March 2015 to March 2016, 1,669 Epic^TM general medicine orders were reviewed (Figure). In the SIESTA-enhanced unit, the mean percentage of sleep-friendly orders rose for both vital signs (+31% [95% CI = 25%, 36%]; P < .001, n_pre = 306, n_post = 306] and VTE prophylaxis (+28% [95% CI = 18%, 37%]; P < .001, n_pre = 158, n_post = 173]. Similar changes were observed in the standard unit for sleep-friendly vital signs (+20% [95% CI = 14%, 25%]; P < .001, n_pre = 252, n_post = 219) and VTE prophylaxis (+16% [95% CI = 6%, 25%]; P = .002, n_pre = 130, n_post = 125). Differences between the two units were not statistically significant, and no significant change in timing of laboratory orders postintervention was found.

jhm014010038_f1.jpg

Nighttime Room Entries

Immediately after SIESTA launch, an average decrease of 114 total entries/night were noted in the SIESTA-enhanced unit, ([95% CI = −138, −91]; P < .001), corresponding to a 44% reduction (−6.3 entries/room) from the mean of 14.3 entries per patient room at baseline (Figure). No statistically significant change was seen in the standard unit. After SIESTA was incorporated into nursing huddles, total disruptions/night decreased by 1.31 disruptions/night ([95% CI = −1.64, −0.98]; P < .001) in the SIESTA-enhanced unit; by comparison, no significant changes were observed in the standard unit.

Patient-Reported Nighttime Sleep Disruptions

Between June 2015 and March 2016, 201 patient surveys were collected. A significant interaction was observed between the SIESTA-enhanced unit and post-period, and patients in the SIESTA-enhanced unit were more likely to report not being disrupted by medications (OR 4.08 [95% CI = 1.13–14.07]; P = .031) and vital signs (OR 3.35 [95% CI = 1.00–11.2]; P = .05) than those in the standard unit. HCAHPS top-box scores for the SIESTA unit increased by 7% for the “Quiet at night” category and 9% for the “Pain well controlled” category; by comparison, no major changes (>5%) were observed in the standard unit.

The present SIESTA intervention demonstrated that physician education coupled with EHR default changes are associated with a significant reduction in orders for overnight vital signs and medication administration in both units. However, addition of nursing education and empowerment in the SIESTA-enhanced unit was associated with fewer nocturnal room entries and improvements in patient-reported outcomes compared with those in the standard unit.

This study presents several implications for hospital initiatives aiming to improve patient sleep.¹⁴ Our study is consistent with other research highlighting the hypothesis that altering the default settings of EHR systems can influence physician behavior in a sustainable manner.¹⁵ However, our study also finds that, even when sleep-friendly orders are present, creating a sleep-friendly environment likely depends on the unit-based nurses championing the cause. While the initial decrease in nocturnal room entries post-SIESTA eventually faded, sustainable changes were observed only after SIESTA was added to nursing huddles, which illustrates the importance of using multiple methods to nudge staff.

Our study includes a number of limitations. It is not a randomized controlled trial, we cannot assume causality, and contamination was assumed, as residents and hospitalists worked in both units. Our single-site study may not be generalizable. Low HCAHPS response rates (10%-20%) also prevent demonstration of statistically significant differences. Finally, our convenience sampling strategy means not all inpatients were surveyed, and objective sleep duration was not measured.

In summary, at the University of Chicago, SIESTA could be associated with adoption of sleep-friendly vitals and medication orders, a decrease in nighttime room entries, and improved patient experience.

Disclosures

The authors have nothing to disclose.

Funding

This study was funded by the National Institute on Aging (NIA Grant No. T35AG029795) and the National Heart, Lung, and Blood Institute (NHLBI Grant Nos. R25HL116372 and K24HL136859).

Although sleep is critical to patient recovery in the hospital, hospitalization is not restful,^1,2 and inpatient sleep deprivation has been linked to poor health outcomes.^1-4 The American Academy of Nursing’s Choosing Wisely^® campaign recommends nurses reduce unnecessary nocturnal care.⁵ However, interventions to improve inpatient sleep are not widely implemented.⁶ Targeting routine disruptions, such as overnight vital signs, by changing default settings in the electronic health record (EHR)with “nudges” could be a cost-effective strategy to improve inpatient sleep.^4,7

We created Sleep for Inpatients: Empowering Staff to Act (SIESTA), which pairs nudges in the EHR with interprofessional education and empowerment,⁸ and tested its effectiveness on objectively and subjectively measured nocturnal sleep disruptors.

Study Design

Two 18-room University of Chicago Medicine general-medicine units were used in this prospective study. The SIESTA-enhanced unit underwent the full sleep intervention: nursing education and empowerment, physician education, and EHR changes. The standard unit did not receive nursing interventions but received all other forms of intervention. Because physicians simultaneously cared for patients on both units, all internal medicine residents and hospitalists received the same education. The study population included physicians, nurses, and awake English-speaking patients who were cognitively intact and admitted to these two units. The University of Chicago Institutional Review Board approved this study (12-1766; 16685B).

Development of SIESTA

To develop SIESTA, patients were surveyed, and focus groups of staff were conducted; overnight vitals, medications, and phlebotomy were identified as major barriers to patient sleep.⁹ We found that physicians did not know how to change the default vital signs order “every 4 hours” or how to batch-order morning phlebotomy at a time other than 4:00 am. Nurses reported having to wake patients up at 1:00 am for q8h subcutaneous heparin.

Behavioral Nudges

The SIESTA team worked with clinical informaticists to change the default orders in Epic^TM (Epic Systems Corporation, 2017, Verona, Wisconsin) in September 2015 so that physicians would be asked, “Continue vital signs throughout the night?”¹⁰ Previously, this question was marked “Yes” by default and hidden. While the default protocol for heparin q8h was maintained, heparin q12h (9:00 am and 9:00 pm) was introduced as an option, since q12h heparin is equally effective for VTE prophylaxis.¹¹ Laboratory ordering was streamlined so that physicians could batch-order laboratory draws at 6:00 am or 10:00 pm.

SIESTA Physician Education

We created a 20-minute presentation on the consequences and causes of in-hospital sleep deprivation and evidence-based behavioral modification. We distributed pocket cards describing the mnemonic SIESTA (Screen patients for sleep disorders, Instruct patients on sleep hygiene, Eliminate disruptions, Shut doors, Treat pain, and Alarm and noise control). Physicians were instructed to consider forgoing overnight vitals, using clinical judgment to identify stable patients, use a sleep-promoting VTE prophylaxis option, and order daily labs at 10:00 pm or 6:00 am. An online educational module was sent to staff who missed live sessions due to days off.

SIESTA-Enhanced Unit

In the SIESTA-enhanced unit, nurses received education using pocket cards and were coached to collaborate with physicians to implement sleep-friendly orders. Customized signage depicting empowered nurses advocating for patients was posted near the huddle board. Because these nurses suggested adding SIESTA to the nurses’ ongoing daily huddles at 4:00 pm and 3:00 am, beginning on January 1, 2016, nurses were asked to identify at least two stable patients for sleep-friendly orders at the huddle. Night nurses incorporated SIESTA into their handoff to day nurses for eligible patients. Day nurses would then call physicians to advocate changing of orders.

Data Collection

Objectively Measured Sleep Disruptors

Adoption of SIESTA orders from March 2015 to March 2016 was assessed with a monthly Epic^TM Clarity report. From August 1, 2015 to April 1, 2016, nocturnal room entries were recorded using the GOJO SMARTLINK^TM Hand Hygiene system (GOJO Industries Inc., 2017, Akron, Ohio). This system includes two components: the hand-sanitizer dispensers, which track dispenses (numerator), and door-mounted Activity Counters, which use heat sensors that react to body heat emitted by a person passing through the doorway (denominator for hand-hygiene compliance). For our analysis, we only used Activity Counter data, which count room entries and exits, regardless of whether sanitizer was dispensed.

Patient-Reported Nighttime Sleep Disruptions

From June 2015 to March 2016, research assistants administered a 10-item Potential Hospital Sleep Disruptions and Noises Questionnaire (PHSDNQ) to patients in both units. Responses to this questionnaire correlate with actigraphy-based sleep measurements.^9,12,13 Surveys were administered every other weekday to patients available to participate (eg, willing to participate, on the unit, awake). Survey data were stored on the REDCap Database (Version 6.14.0; Vanderbilt University, 2016, Nashville, Tennessee). Pre- and post-intervention Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) “top-box ratings” for percent quiet at night and percent pain well controlled were also compared.

Data Analysis

Objectively Measured Potential Sleep Disruptors

The proportion of sleep-friendly orders was analyzed using a two-sample test for proportions pre-post for the SIESTA-enhanced and standard units. The difference in use of SIESTA orders between units was analyzed via multivariable logistic regression, testing for independent associations between post-period, SIESTA-enhanced unit, and an interaction term (post-period × SIESTA unit) on use of sleep-friendly orders.

Room entries per night (11:00 pm–7:00 am) were analyzed via single-group interrupted time-series. Multiple Activity Counter entries within three minutes were counted as a single room entry. In addition, the pre-post cutoff was set to 7:00 am, September 8, 2015; after the SIESTA launch, a second cutoff marking when SIESTA was added to the nurses’ MDI Huddle was added at 7:00 am, January 1, 2016.

Patient-Reported Nighttime Sleep Disruptions

Per prior studies, we defined a score 2 or higher as “sleep disruption.”⁹ Differences between units were evaluated via multivariable logistic regression to examine the association between the interaction of post-period × SIESTA-enhanced unit and odds of not reporting a sleep disruption. Significance was denoted as P = .05.

Between March 2015 and March 2016, 1,083 general-medicine patients were admitted to the SIESTA-enhanced and standard units (Table).

jhm014010038_t1.jpg

Nocturnal Orders

From March 2015 to March 2016, 1,669 Epic^TM general medicine orders were reviewed (Figure). In the SIESTA-enhanced unit, the mean percentage of sleep-friendly orders rose for both vital signs (+31% [95% CI = 25%, 36%]; P < .001, n_pre = 306, n_post = 306] and VTE prophylaxis (+28% [95% CI = 18%, 37%]; P < .001, n_pre = 158, n_post = 173]. Similar changes were observed in the standard unit for sleep-friendly vital signs (+20% [95% CI = 14%, 25%]; P < .001, n_pre = 252, n_post = 219) and VTE prophylaxis (+16% [95% CI = 6%, 25%]; P = .002, n_pre = 130, n_post = 125). Differences between the two units were not statistically significant, and no significant change in timing of laboratory orders postintervention was found.

jhm014010038_f1.jpg

Nighttime Room Entries

Immediately after SIESTA launch, an average decrease of 114 total entries/night were noted in the SIESTA-enhanced unit, ([95% CI = −138, −91]; P < .001), corresponding to a 44% reduction (−6.3 entries/room) from the mean of 14.3 entries per patient room at baseline (Figure). No statistically significant change was seen in the standard unit. After SIESTA was incorporated into nursing huddles, total disruptions/night decreased by 1.31 disruptions/night ([95% CI = −1.64, −0.98]; P < .001) in the SIESTA-enhanced unit; by comparison, no significant changes were observed in the standard unit.

Patient-Reported Nighttime Sleep Disruptions

Between June 2015 and March 2016, 201 patient surveys were collected. A significant interaction was observed between the SIESTA-enhanced unit and post-period, and patients in the SIESTA-enhanced unit were more likely to report not being disrupted by medications (OR 4.08 [95% CI = 1.13–14.07]; P = .031) and vital signs (OR 3.35 [95% CI = 1.00–11.2]; P = .05) than those in the standard unit. HCAHPS top-box scores for the SIESTA unit increased by 7% for the “Quiet at night” category and 9% for the “Pain well controlled” category; by comparison, no major changes (>5%) were observed in the standard unit.

The present SIESTA intervention demonstrated that physician education coupled with EHR default changes are associated with a significant reduction in orders for overnight vital signs and medication administration in both units. However, addition of nursing education and empowerment in the SIESTA-enhanced unit was associated with fewer nocturnal room entries and improvements in patient-reported outcomes compared with those in the standard unit.

This study presents several implications for hospital initiatives aiming to improve patient sleep.¹⁴ Our study is consistent with other research highlighting the hypothesis that altering the default settings of EHR systems can influence physician behavior in a sustainable manner.¹⁵ However, our study also finds that, even when sleep-friendly orders are present, creating a sleep-friendly environment likely depends on the unit-based nurses championing the cause. While the initial decrease in nocturnal room entries post-SIESTA eventually faded, sustainable changes were observed only after SIESTA was added to nursing huddles, which illustrates the importance of using multiple methods to nudge staff.

Our study includes a number of limitations. It is not a randomized controlled trial, we cannot assume causality, and contamination was assumed, as residents and hospitalists worked in both units. Our single-site study may not be generalizable. Low HCAHPS response rates (10%-20%) also prevent demonstration of statistically significant differences. Finally, our convenience sampling strategy means not all inpatients were surveyed, and objective sleep duration was not measured.

In summary, at the University of Chicago, SIESTA could be associated with adoption of sleep-friendly vitals and medication orders, a decrease in nighttime room entries, and improved patient experience.

Disclosures

The authors have nothing to disclose.

Funding

This study was funded by the National Institute on Aging (NIA Grant No. T35AG029795) and the National Heart, Lung, and Blood Institute (NHLBI Grant Nos. R25HL116372 and K24HL136859).

Chronic opioid and benzodiazepine use is common and increasing.^1-5 Outpatient use of these medications has been associated with hospital readmission and death,^6-12 with concurrent use associated with particularly increased risk.^13,14 Less is known about outcomes for hospitalized patients receiving these medications.

More than half of hospital inpatients in the United States receive opioids,¹⁵ many of which are new prescriptions rather than continuation of chronic therapy.^16,17 Less is known about inpatient benzodiazepine administration, but the prevalence may exceed 10% among elderly populations.¹⁸ Hospitalized patients often have comorbidities or physiological disturbances that might increase their risk related to use of these medications. Opioids can cause central and obstructive sleep apneas,^19-21 and benzodiazepines contribute to respiratory depression and airway relaxation.²² Benzodiazepines also impair psychomotor function and recall,²³ which could mediate the recognized risk for delirium and falls in the hospital.^24,25 These findings suggest pathways by which these medications might contribute to clinical deterioration.

Most studies in hospitalized patients have been limited to specific populations^15,26-28 and have not explicitly controlled for severity of illness over time. It remains unclear whether associations identified within particular groups of patients hold true for the broader population of general ward inpatients. Therefore, we aimed to determine the independent association between opioid and benzodiazepine administration and clinical deterioration in ward patients.

Setting and Study Population

We performed an observational cohort study at a 500-bed urban academic hospital. Data were obtained from all adults hospitalized on the wards between November 1, 2008, and January 21, 2016. The study protocol was approved by the University of Chicago Institutional Review Board (IRB#15-0195).

Data Collection

The study utilized de-identified data from the electronic health record (EHR; Epic Systems Corporation, Verona, Wisconsin) and administrative databases collected by the University of Chicago Clinical Research Data Warehouse. Patient age, sex, race, body mass index (BMI), and ward admission source (ie, emergency department (ED), transferred from the intensive care unit (ICU), or directly admitted to the wards) were collected. International Classification of Diseases, 9^th Revision, Clinical Modification (ICD-9-CM) codes were used to identify Elixhauser Comorbidity Index categories.^29,30 Because patients with similar diagnoses (eg, active cancer) are cohorted within particular areas in our hospital, we obtained the ward unit for all patients. Patients who underwent surgery were identified using the hospital’s admission-transfer-discharge database.

To determine severity of illness, routinely collected vital signs and laboratory values were utilized to calculate the electronic cardiac arrest risk triage (eCART) score, an accurate risk score we previously developed and validated for predicting adverse events among ward patients.³¹ If any vital sign or laboratory value was missing, the next available measurement was carried forward. If any value remained missing after this change, the median value for that location (ie, wards, ICU, or ED) was imputed.^32,33 Additionally, patient-reported pain scores at the time of opioid administration were extracted from nursing flowsheets. If no pain score was present at the time of opioid administration, the patient’s previous score was carried forward.

We excluded patients with sickle-cell disease or seizure history and admissions with diagnoses of alcohol withdrawal from the analysis, because these diagnoses were expected to be associated with different medication administration practices compared to other inpatients. We also excluded patients with a tracheostomy because we expected their respiratory monitoring to differ from the other patients in our cohort. Finally, because ward deaths resulting from a comfort care scenario often involve opioids and/or benzodiazepines, ward segments involving comfort care deaths (defined as death without attempted resuscitation) were excluded from the analysis (Supplemental Figure 1). Patients with sickle-cell disease were identified using ICD-9 codes, and encounters during which a seizure may have occurred were identified using a combination of ICD-9 codes and receipt of anti-epileptic medication (Supplemental Table 1). Patients at risk for alcohol withdrawal were identified by the presence of any Clinical Institute Withdrawal Assessment for Alcohol score within nursing flowsheets, and patients with tracheostomies were identified using documentation of ventilator support within their first 12 hours on the wards. In addition to these exclusion criteria, patients with obstructive sleep apnea (OSA) were identified by the following ICD-9 codes: 278.03, 327.23, 780.51, 780.53, and 780.57.

Medications

Ward administrations of opioids and benzodiazepines—dose, route, and administration time—were collected from the EHR. We excluded all administrations in nonward locations such as the ED, ICU, operating room, or procedure suite. Additionally, because patients emergently intubated may receive sedative and analgesic medications to facilitate intubation, and because patients experiencing cardiac arrest are frequently intubated periresuscitation, we a priori excluded all administrations within 15 minutes of a ward cardiac arrest or an intubation.

For consistent comparisons, opioid doses were converted to oral morphine equivalents³⁴ and adjusted by a factor of 15 to reflect the smallest routinely available oral morphine tablet in our hospital (Supplemental Table 2). Benzodiazepine doses were converted to oral lorazepam equivalents (Supplemental Table 2).³⁴ Thus, the independent variables were oral morphine or lorazepam equivalents administered within each 6-hour window. We a priori presumed opioid doses greater than the 99^th percentile (1200 mg) or benzodiazepine doses greater than 10 mg oral lorazepam equivalents within a 6-hour window to be erroneous entries, and replaced these outlier values with the median value for each medication category.

Outcomes

The primary outcome was the composite of ICU transfer or cardiac arrest (loss of pulse with attempted resuscitation) on the wards, with individual outcomes investigated secondarily. An ICU transfer (patient movement from a ward directly to the ICU) was identified using the hospital’s admission-transfer-discharge database. Cardiac arrests were identified using a prospectively validated quality improvement database.³⁵

Because deaths on the wards resulted either from cardiac arrest or from a comfort care scenario, mortality was not studied as an outcome.

Statistical Analysis

Patient characteristics were compared using Student t tests, Wilcoxon rank sum tests, and chi-squared statistics, as appropriate. Unadjusted and adjusted models were created using discrete-time survival analysis,^36-39 which involved dividing time into discrete 6-hour intervals and employing the predictor variables chronologically closest to the beginning of each time window to forecast whether the outcome occurred within each interval. Predictor variables in the adjusted model included patient characteristics (age, sex, BMI, and Elixhauser Agency for Healthcare Research and Quality-Web comorbidities³⁰ [a priori excluding comorbidities recorded for fewer than 1000 admissions from the model]), ward unit, surgical status, prior ICU admission during the hospitalization, cumulative opioid or benzodiazepine dose during the previous 24 hours, and severity of illness (measured by eCART score). The adjusted model for opioids also included the patient’s pain score. Age, eCART score, and pain score were entered linearly while race, BMI (underweight, less than 18.5 kg/m²; normal, 18.5-24.9 kg/m²; overweight, 25.0-29.9 kg/m²; obese, 30-39.9 kg/m²; and severely obese, 40 mg/m² or greater), and ward unit were modeled as categorical variables.

Since repeat hospitalization could confound the results of our study, we performed a sensitivity analysis including only 1 randomly selected hospital admission per patient. We also performed a sensitivity analysis including receipt of both opioids and benzodiazepines, and an interaction term within each ward segment, as well as an analysis in which zolpidem—the most commonly administered nonbenzodiazepine hypnotic medication in our hospital—was included along with both opioids and benzodiazepines. Finally, we performed a sensitivity analysis replacing missing pain scores with imputed values ranging from 0 to the median ward pain score.

We also performed subgroup analyses of adjusted models across age quartiles and for each BMI category, as well as for surgical status, OSA status, gender, time of medication administration, and route of administration (intravenous vs. oral). We also performed an analysis across pain score severity⁴⁰ to determine whether these medications produce differential effects at various levels of pain.

All tests of significance used a 2-sided P value less than 0.05. Statistical analyses were completed using Stata version 14.1 (StataCorp, LLC, College Station, Texas).

jhm012060428_f1.jpg

Patient Characteristics

A total of 144,895 admissions, from 75,369 patients, had ward vital signs or laboratory values documented during the study period. Ward segments from 634 admissions were excluded due to comfort care status, which resulted in exclusion of 479 complete patient admissions. Additionally, 139 patients with tracheostomies were excluded. Furthermore, 2934 patient admissions with a sickle-cell diagnosis were excluded, of which 95% (n = 2791) received an opioid and 11% (n = 310) received a benzodiazepine. Another 14,029 admissions associated with seizures, 6134 admissions involving alcohol withdrawal, and 1332 with both were excluded, of which 66% (n = 14,174) received an opioid and 35% (n = 7504) received a benzodiazepine. After exclusions, 120,518 admissions were included in the final analysis, with 67% (n = 80,463) associated with at least 1 administration of an opioid and 21% (n = 25,279) associated with at least 1 benzodiazepine administration.

In total, there were 672,851 intervals when an opioid was administered during the study, with a median dose of 12 mg oral morphine equivalents (interquartile range, 8-30). Of these, 21,634 doses were replaced due to outlier status outside the 99^th percentile. Patients receiving opioids were younger (median age 56 vs 61 years), less likely to be African American (48% vs 59%), more likely to have undergone surgery (18% vs 6%), and less likely to have most noncancer medical comorbidities than those who never received an opioid (all P < 0.001) (Table 1).

jhm012060428_t1.jpg

jhm012060428_t2.jpg

Patient Outcomes

The incidence of the composite outcome was higher in admissions with at least 1 opioid medication than those without an opioid (7% vs. 4%, P < 0.001), and in admissions with at least 1 dose of benzodiazepines compared to those without a benzodiazepine (11% vs. 4%, P < 0.001) (Table 2).

Within 6-hour segments, increasing doses of opioids were associated with an initial decrease in the frequency of the composite outcome followed by a dose-related increase in the frequency of the composite outcome with morphine equivalents greater than 45 mg. By contrast, the frequency of the composite outcome increased with additional benzodiazepine equivalents (Figure).

In the adjusted model, opioid administration was associated with increased risk for the composite outcome (Table 3) in a dose-dependent fashion, with each 15 mg oral morphine equivalent associated with a 1.9% increase in the odds of ICU transfer or cardiac arrest within the subsequent 6-hour time interval (odds ratio [OR], 1.019; 95% confidence interval [CI], 1.013-1.026; P < 0.001).

jhm012060428_t3.jpg

Sensitivity Analyses

A sensitivity analysis including 1 randomly selected hospitalization per patient involved 67,097 admissions and found results similar to the primary analysis, with each 15 mg oral morphine equivalent associated with a 1.9% increase in the odds of the composite outcome (OR, 1.019; 95% CI, 1.011-1.028; P < 0.001) and each 1 mg oral lorazepam equivalent associated with a 41% increase in the odds of the composite outcome (OR, 1.41; 95% CI, 1.21-1.65; P < 0.001). Inclusion of both opioids and benzodiazepines in the adjusted model again yielded results similar to the main analysis for both opioids (OR, 1.020; 95% CI, 1.013-1.026; P < 0.001) and benzodiazepines (OR, 1.35; 95% CI, 1.18-1.54; P < 0.001), without a significant interaction detected (P = 0.09). These results were unchanged with the addition of zolpidem to the model as an additional potential confounder, and zolpidem did not increase the risk of the study outcomes (P = 0.2).

A final sensitivity analysis for the opioid model involved replacing missing pain scores with imputed values ranging from 0 to the median ward score, which was 5. The results of these analyses did not differ from the primary model and were consistent regardless of imputation value (OR, 1.018; 95% CI, 1.012-1.023; P < 0.001).

Subgroup Analyses

Analyses of opioid administration by subgroup (sex, age quartiles, BMI categories, OSA diagnosis, surgical status, daytime/nighttime medication administration, IV/PO administration, and pain severity) yielded similar results to the overall analysis (Supplemental Figure 2). Subgroup analysis of patients receiving benzodiazepines revealed similarly increased adjusted odds of the composite outcome across strata of gender, BMI, surgical status, and medication administration time (Supplemental Figure 3). Notably, patients older than 70 years who received a benzodiazepine were at 64% increased odds of the composite outcome (OR, 1.64; 95% CI, 1.30-2.08), compared to 2% to 38% increased risk for patients under 70 years. Finally, IV doses of benzodiazepines were associated with 48% increased odds for deterioration (OR, 1.48; 95% CI, 1.18-1.84; P = 0.001), compared to a nonsignificant 14% increase in the odds for PO doses (OR, 1.14; 95% CI, 0.99-1.31; P = 0.066).

In a large, single-center, observational study of ward inpatients, we found that opioid use was associated with a small but significant increased risk for clinical deterioration on the wards, with every 15 mg oral morphine equivalent increasing the odds of ICU transfer or cardiac arrest in the next 6 hours by 1.9%. Benzodiazepines were associated with a much higher risk: each equivalent of 1 mg of oral lorazepam increased the odds of ICU transfer or cardiac arrest by almost 30%. These results have important implications for care at the bedside of hospitalized ward patients and suggest the need for closer monitoring after receipt of these medications, particularly benzodiazepines.

Previous work has described negative effects of opioid medications among select inpatient populations. In surgical patients, opioids have been associated with hospital readmission, increased length of stay, and hospital mortality.^26,28 More recently, Herzig et al.¹⁵ found more adverse events in nonsurgical ward patients within the hospitals prescribing opioids the most frequently. These studies may have been limited by the populations studied and the inability to control for confounders such as severity of illness and pain score. Our study expands these findings to a more generalizable population and shows that even after adjustment for potential confounders, such as severity of illness, pain score, and medication dose, opioids are associated with increased short-term risk of clinical deterioration.

By contrast, few studies have characterized the risks associated with benzodiazepine use among ward inpatients. Recently, Overdyk et al.²⁷ found that inpatient use of opioids and sedatives was associated with increased risk for cardiac arrest and hospital death. However, this study included ICU patients, which may confound the results, as ICU patients often receive high doses of opioids or benzodiazepines to facilitate mechanical ventilation or other invasive procedures, while also having a particularly high risk of adverse outcomes like cardiac arrest and inhospital death.

Several mechanisms may explain the magnitude of effect seen with regard to benzodiazepines. First, benzodiazepines may directly produce clinical deterioration by decreased respiratory drive, diminished airway tone, or hemodynamic decompensation. It is possible that the broad spectrum of cardiorespiratory side effects of benzodiazepines—and potential unpredictability of these effects—increases the difficulty of observation and management for patients receiving them. This difficulty may be compounded with intravenous administration of benzodiazepines, which was associated with a higher risk for deterioration than oral doses in our cohort. Alternatively, benzodiazepines may contribute to clinical decompensation by masking signs of deterioration such as encephalopathy or vital sign instability like tachycardia or tachypnea that may be mistaken as anxiety. Notably, while our hospital has a nursing-driven protocol for monitoring patients receiving opioids (in which pain is serially assessed, leading to additional bedside observation), we do not have protocols for ward patients receiving benzodiazepines. Finally, although we found that orders for opioids and benzodiazepines were more common in white patients than African American patients, this finding may be due to differences in the types or number of medical comorbidities experienced by these patients.

Our study has several strengths, including the large number of admissions we included. Additionally, we included a broad range of medical and surgical ward admissions, which should increase the generalizability of our results. Further, our rates of ICU transfer are in line with data reported from other groups,^41,42 which again may add to the generalizability of our findings. We also addressed many potential confounders by including patient characteristics, individual ward units, and (for opioids) pain score in our model, and by controlling for severity of illness with the eCART score, an accurate predictor of ICU transfer and ward cardiac arrest within our population.^32,37 Finally, our robust methodology allowed us to include acute and cumulative medication doses, as well as time, in the model. By performing a discrete-time survival analysis, we were able to evaluate receipt of opioids and benzodiazepines—as well as risk for clinical deterioration—longitudinally, lending strength to our results.

Limitations of our study include its single-center cohort, which may reduce generalizability to other populations. Additionally, because we could not validate the accuracy of—or adherence to—outpatient medication lists, we were unable to identify chronic opioid or benzodiazepine users by these lists. However, patients chronically taking opioids or benzodiazepines would likely receive doses each hospital day; by including 24-hour cumulative doses in our model, we attempted to adjust for some portion of their chronic use. Also, because evaluation of delirium was not objectively recorded in our dataset, we were unable to evaluate the relationship between receipt of these medications and development of delirium, which is an important outcome for hospitalized patients. Finally, neither the diagnoses for which these medications were prescribed, nor the reason for ICU transfer, were present in our dataset, which leaves open the possibility of unmeasured confounding.

After adjustment for important confounders including severity of illness, medication dose, and time, opioids were associated with a slight increase in clinical deterioration on the wards, while benzodiazepines were associated with a much larger risk for deterioration. This finding raises concern about the safety of benzodiazepine use among ward patients and suggests that increased monitoring of patients receiving these medications may be warranted.

Acknowledgment

The authors thank Nicole Twu for administrative support.

Disclosure

Drs. Churpek and Edelson have a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients. Dr. Churpek is supported by a career development award from the National Heart, Lung, and Blood Institute (K08 HL121080). Dr. Churpek has received honoraria from Chest for invited speaking engagements. In addition, Dr. Edelson has received research support from Philips Healthcare (Andover, Massachusetts), research support from the American Heart Association (Dallas, Texas) and Laerdal Medical (Stavanger, Norway), and research support from Early Sense (Tel Aviv, Israel). She has ownership interest in Quant HC (Chicago, Illinois), which is developing products for risk stratification of hospitalized patients. Dr. Mokhlesi is supported by National Institutes of Health grant R01HL119161. Dr. Mokhlesi has served as a consultant to Philips/Respironics and has received research support from Philips/Respironics. Preliminary versions of these data were presented as a poster presentation at the 2016 meeting of the American Thoracic Society, May 17, 2016; San Francisco, California.

Chronic opioid and benzodiazepine use is common and increasing.^1-5 Outpatient use of these medications has been associated with hospital readmission and death,^6-12 with concurrent use associated with particularly increased risk.^13,14 Less is known about outcomes for hospitalized patients receiving these medications.

More than half of hospital inpatients in the United States receive opioids,¹⁵ many of which are new prescriptions rather than continuation of chronic therapy.^16,17 Less is known about inpatient benzodiazepine administration, but the prevalence may exceed 10% among elderly populations.¹⁸ Hospitalized patients often have comorbidities or physiological disturbances that might increase their risk related to use of these medications. Opioids can cause central and obstructive sleep apneas,^19-21 and benzodiazepines contribute to respiratory depression and airway relaxation.²² Benzodiazepines also impair psychomotor function and recall,²³ which could mediate the recognized risk for delirium and falls in the hospital.^24,25 These findings suggest pathways by which these medications might contribute to clinical deterioration.

Most studies in hospitalized patients have been limited to specific populations^15,26-28 and have not explicitly controlled for severity of illness over time. It remains unclear whether associations identified within particular groups of patients hold true for the broader population of general ward inpatients. Therefore, we aimed to determine the independent association between opioid and benzodiazepine administration and clinical deterioration in ward patients.

Setting and Study Population

We performed an observational cohort study at a 500-bed urban academic hospital. Data were obtained from all adults hospitalized on the wards between November 1, 2008, and January 21, 2016. The study protocol was approved by the University of Chicago Institutional Review Board (IRB#15-0195).

Data Collection

The study utilized de-identified data from the electronic health record (EHR; Epic Systems Corporation, Verona, Wisconsin) and administrative databases collected by the University of Chicago Clinical Research Data Warehouse. Patient age, sex, race, body mass index (BMI), and ward admission source (ie, emergency department (ED), transferred from the intensive care unit (ICU), or directly admitted to the wards) were collected. International Classification of Diseases, 9^th Revision, Clinical Modification (ICD-9-CM) codes were used to identify Elixhauser Comorbidity Index categories.^29,30 Because patients with similar diagnoses (eg, active cancer) are cohorted within particular areas in our hospital, we obtained the ward unit for all patients. Patients who underwent surgery were identified using the hospital’s admission-transfer-discharge database.

To determine severity of illness, routinely collected vital signs and laboratory values were utilized to calculate the electronic cardiac arrest risk triage (eCART) score, an accurate risk score we previously developed and validated for predicting adverse events among ward patients.³¹ If any vital sign or laboratory value was missing, the next available measurement was carried forward. If any value remained missing after this change, the median value for that location (ie, wards, ICU, or ED) was imputed.^32,33 Additionally, patient-reported pain scores at the time of opioid administration were extracted from nursing flowsheets. If no pain score was present at the time of opioid administration, the patient’s previous score was carried forward.

We excluded patients with sickle-cell disease or seizure history and admissions with diagnoses of alcohol withdrawal from the analysis, because these diagnoses were expected to be associated with different medication administration practices compared to other inpatients. We also excluded patients with a tracheostomy because we expected their respiratory monitoring to differ from the other patients in our cohort. Finally, because ward deaths resulting from a comfort care scenario often involve opioids and/or benzodiazepines, ward segments involving comfort care deaths (defined as death without attempted resuscitation) were excluded from the analysis (Supplemental Figure 1). Patients with sickle-cell disease were identified using ICD-9 codes, and encounters during which a seizure may have occurred were identified using a combination of ICD-9 codes and receipt of anti-epileptic medication (Supplemental Table 1). Patients at risk for alcohol withdrawal were identified by the presence of any Clinical Institute Withdrawal Assessment for Alcohol score within nursing flowsheets, and patients with tracheostomies were identified using documentation of ventilator support within their first 12 hours on the wards. In addition to these exclusion criteria, patients with obstructive sleep apnea (OSA) were identified by the following ICD-9 codes: 278.03, 327.23, 780.51, 780.53, and 780.57.

Medications

Ward administrations of opioids and benzodiazepines—dose, route, and administration time—were collected from the EHR. We excluded all administrations in nonward locations such as the ED, ICU, operating room, or procedure suite. Additionally, because patients emergently intubated may receive sedative and analgesic medications to facilitate intubation, and because patients experiencing cardiac arrest are frequently intubated periresuscitation, we a priori excluded all administrations within 15 minutes of a ward cardiac arrest or an intubation.

For consistent comparisons, opioid doses were converted to oral morphine equivalents³⁴ and adjusted by a factor of 15 to reflect the smallest routinely available oral morphine tablet in our hospital (Supplemental Table 2). Benzodiazepine doses were converted to oral lorazepam equivalents (Supplemental Table 2).³⁴ Thus, the independent variables were oral morphine or lorazepam equivalents administered within each 6-hour window. We a priori presumed opioid doses greater than the 99^th percentile (1200 mg) or benzodiazepine doses greater than 10 mg oral lorazepam equivalents within a 6-hour window to be erroneous entries, and replaced these outlier values with the median value for each medication category.

Outcomes

The primary outcome was the composite of ICU transfer or cardiac arrest (loss of pulse with attempted resuscitation) on the wards, with individual outcomes investigated secondarily. An ICU transfer (patient movement from a ward directly to the ICU) was identified using the hospital’s admission-transfer-discharge database. Cardiac arrests were identified using a prospectively validated quality improvement database.³⁵

Because deaths on the wards resulted either from cardiac arrest or from a comfort care scenario, mortality was not studied as an outcome.

Statistical Analysis

Patient characteristics were compared using Student t tests, Wilcoxon rank sum tests, and chi-squared statistics, as appropriate. Unadjusted and adjusted models were created using discrete-time survival analysis,^36-39 which involved dividing time into discrete 6-hour intervals and employing the predictor variables chronologically closest to the beginning of each time window to forecast whether the outcome occurred within each interval. Predictor variables in the adjusted model included patient characteristics (age, sex, BMI, and Elixhauser Agency for Healthcare Research and Quality-Web comorbidities³⁰ [a priori excluding comorbidities recorded for fewer than 1000 admissions from the model]), ward unit, surgical status, prior ICU admission during the hospitalization, cumulative opioid or benzodiazepine dose during the previous 24 hours, and severity of illness (measured by eCART score). The adjusted model for opioids also included the patient’s pain score. Age, eCART score, and pain score were entered linearly while race, BMI (underweight, less than 18.5 kg/m²; normal, 18.5-24.9 kg/m²; overweight, 25.0-29.9 kg/m²; obese, 30-39.9 kg/m²; and severely obese, 40 mg/m² or greater), and ward unit were modeled as categorical variables.

Since repeat hospitalization could confound the results of our study, we performed a sensitivity analysis including only 1 randomly selected hospital admission per patient. We also performed a sensitivity analysis including receipt of both opioids and benzodiazepines, and an interaction term within each ward segment, as well as an analysis in which zolpidem—the most commonly administered nonbenzodiazepine hypnotic medication in our hospital—was included along with both opioids and benzodiazepines. Finally, we performed a sensitivity analysis replacing missing pain scores with imputed values ranging from 0 to the median ward pain score.

We also performed subgroup analyses of adjusted models across age quartiles and for each BMI category, as well as for surgical status, OSA status, gender, time of medication administration, and route of administration (intravenous vs. oral). We also performed an analysis across pain score severity⁴⁰ to determine whether these medications produce differential effects at various levels of pain.

All tests of significance used a 2-sided P value less than 0.05. Statistical analyses were completed using Stata version 14.1 (StataCorp, LLC, College Station, Texas).

jhm012060428_f1.jpg

Patient Characteristics

A total of 144,895 admissions, from 75,369 patients, had ward vital signs or laboratory values documented during the study period. Ward segments from 634 admissions were excluded due to comfort care status, which resulted in exclusion of 479 complete patient admissions. Additionally, 139 patients with tracheostomies were excluded. Furthermore, 2934 patient admissions with a sickle-cell diagnosis were excluded, of which 95% (n = 2791) received an opioid and 11% (n = 310) received a benzodiazepine. Another 14,029 admissions associated with seizures, 6134 admissions involving alcohol withdrawal, and 1332 with both were excluded, of which 66% (n = 14,174) received an opioid and 35% (n = 7504) received a benzodiazepine. After exclusions, 120,518 admissions were included in the final analysis, with 67% (n = 80,463) associated with at least 1 administration of an opioid and 21% (n = 25,279) associated with at least 1 benzodiazepine administration.

In total, there were 672,851 intervals when an opioid was administered during the study, with a median dose of 12 mg oral morphine equivalents (interquartile range, 8-30). Of these, 21,634 doses were replaced due to outlier status outside the 99^th percentile. Patients receiving opioids were younger (median age 56 vs 61 years), less likely to be African American (48% vs 59%), more likely to have undergone surgery (18% vs 6%), and less likely to have most noncancer medical comorbidities than those who never received an opioid (all P < 0.001) (Table 1).

jhm012060428_t1.jpg

jhm012060428_t2.jpg

Patient Outcomes

The incidence of the composite outcome was higher in admissions with at least 1 opioid medication than those without an opioid (7% vs. 4%, P < 0.001), and in admissions with at least 1 dose of benzodiazepines compared to those without a benzodiazepine (11% vs. 4%, P < 0.001) (Table 2).

Within 6-hour segments, increasing doses of opioids were associated with an initial decrease in the frequency of the composite outcome followed by a dose-related increase in the frequency of the composite outcome with morphine equivalents greater than 45 mg. By contrast, the frequency of the composite outcome increased with additional benzodiazepine equivalents (Figure).

In the adjusted model, opioid administration was associated with increased risk for the composite outcome (Table 3) in a dose-dependent fashion, with each 15 mg oral morphine equivalent associated with a 1.9% increase in the odds of ICU transfer or cardiac arrest within the subsequent 6-hour time interval (odds ratio [OR], 1.019; 95% confidence interval [CI], 1.013-1.026; P < 0.001).

jhm012060428_t3.jpg

Sensitivity Analyses

A sensitivity analysis including 1 randomly selected hospitalization per patient involved 67,097 admissions and found results similar to the primary analysis, with each 15 mg oral morphine equivalent associated with a 1.9% increase in the odds of the composite outcome (OR, 1.019; 95% CI, 1.011-1.028; P < 0.001) and each 1 mg oral lorazepam equivalent associated with a 41% increase in the odds of the composite outcome (OR, 1.41; 95% CI, 1.21-1.65; P < 0.001). Inclusion of both opioids and benzodiazepines in the adjusted model again yielded results similar to the main analysis for both opioids (OR, 1.020; 95% CI, 1.013-1.026; P < 0.001) and benzodiazepines (OR, 1.35; 95% CI, 1.18-1.54; P < 0.001), without a significant interaction detected (P = 0.09). These results were unchanged with the addition of zolpidem to the model as an additional potential confounder, and zolpidem did not increase the risk of the study outcomes (P = 0.2).

A final sensitivity analysis for the opioid model involved replacing missing pain scores with imputed values ranging from 0 to the median ward score, which was 5. The results of these analyses did not differ from the primary model and were consistent regardless of imputation value (OR, 1.018; 95% CI, 1.012-1.023; P < 0.001).

Subgroup Analyses

Analyses of opioid administration by subgroup (sex, age quartiles, BMI categories, OSA diagnosis, surgical status, daytime/nighttime medication administration, IV/PO administration, and pain severity) yielded similar results to the overall analysis (Supplemental Figure 2). Subgroup analysis of patients receiving benzodiazepines revealed similarly increased adjusted odds of the composite outcome across strata of gender, BMI, surgical status, and medication administration time (Supplemental Figure 3). Notably, patients older than 70 years who received a benzodiazepine were at 64% increased odds of the composite outcome (OR, 1.64; 95% CI, 1.30-2.08), compared to 2% to 38% increased risk for patients under 70 years. Finally, IV doses of benzodiazepines were associated with 48% increased odds for deterioration (OR, 1.48; 95% CI, 1.18-1.84; P = 0.001), compared to a nonsignificant 14% increase in the odds for PO doses (OR, 1.14; 95% CI, 0.99-1.31; P = 0.066).

In a large, single-center, observational study of ward inpatients, we found that opioid use was associated with a small but significant increased risk for clinical deterioration on the wards, with every 15 mg oral morphine equivalent increasing the odds of ICU transfer or cardiac arrest in the next 6 hours by 1.9%. Benzodiazepines were associated with a much higher risk: each equivalent of 1 mg of oral lorazepam increased the odds of ICU transfer or cardiac arrest by almost 30%. These results have important implications for care at the bedside of hospitalized ward patients and suggest the need for closer monitoring after receipt of these medications, particularly benzodiazepines.

Previous work has described negative effects of opioid medications among select inpatient populations. In surgical patients, opioids have been associated with hospital readmission, increased length of stay, and hospital mortality.^26,28 More recently, Herzig et al.¹⁵ found more adverse events in nonsurgical ward patients within the hospitals prescribing opioids the most frequently. These studies may have been limited by the populations studied and the inability to control for confounders such as severity of illness and pain score. Our study expands these findings to a more generalizable population and shows that even after adjustment for potential confounders, such as severity of illness, pain score, and medication dose, opioids are associated with increased short-term risk of clinical deterioration.

By contrast, few studies have characterized the risks associated with benzodiazepine use among ward inpatients. Recently, Overdyk et al.²⁷ found that inpatient use of opioids and sedatives was associated with increased risk for cardiac arrest and hospital death. However, this study included ICU patients, which may confound the results, as ICU patients often receive high doses of opioids or benzodiazepines to facilitate mechanical ventilation or other invasive procedures, while also having a particularly high risk of adverse outcomes like cardiac arrest and inhospital death.

Several mechanisms may explain the magnitude of effect seen with regard to benzodiazepines. First, benzodiazepines may directly produce clinical deterioration by decreased respiratory drive, diminished airway tone, or hemodynamic decompensation. It is possible that the broad spectrum of cardiorespiratory side effects of benzodiazepines—and potential unpredictability of these effects—increases the difficulty of observation and management for patients receiving them. This difficulty may be compounded with intravenous administration of benzodiazepines, which was associated with a higher risk for deterioration than oral doses in our cohort. Alternatively, benzodiazepines may contribute to clinical decompensation by masking signs of deterioration such as encephalopathy or vital sign instability like tachycardia or tachypnea that may be mistaken as anxiety. Notably, while our hospital has a nursing-driven protocol for monitoring patients receiving opioids (in which pain is serially assessed, leading to additional bedside observation), we do not have protocols for ward patients receiving benzodiazepines. Finally, although we found that orders for opioids and benzodiazepines were more common in white patients than African American patients, this finding may be due to differences in the types or number of medical comorbidities experienced by these patients.

Our study has several strengths, including the large number of admissions we included. Additionally, we included a broad range of medical and surgical ward admissions, which should increase the generalizability of our results. Further, our rates of ICU transfer are in line with data reported from other groups,^41,42 which again may add to the generalizability of our findings. We also addressed many potential confounders by including patient characteristics, individual ward units, and (for opioids) pain score in our model, and by controlling for severity of illness with the eCART score, an accurate predictor of ICU transfer and ward cardiac arrest within our population.^32,37 Finally, our robust methodology allowed us to include acute and cumulative medication doses, as well as time, in the model. By performing a discrete-time survival analysis, we were able to evaluate receipt of opioids and benzodiazepines—as well as risk for clinical deterioration—longitudinally, lending strength to our results.

Limitations of our study include its single-center cohort, which may reduce generalizability to other populations. Additionally, because we could not validate the accuracy of—or adherence to—outpatient medication lists, we were unable to identify chronic opioid or benzodiazepine users by these lists. However, patients chronically taking opioids or benzodiazepines would likely receive doses each hospital day; by including 24-hour cumulative doses in our model, we attempted to adjust for some portion of their chronic use. Also, because evaluation of delirium was not objectively recorded in our dataset, we were unable to evaluate the relationship between receipt of these medications and development of delirium, which is an important outcome for hospitalized patients. Finally, neither the diagnoses for which these medications were prescribed, nor the reason for ICU transfer, were present in our dataset, which leaves open the possibility of unmeasured confounding.

After adjustment for important confounders including severity of illness, medication dose, and time, opioids were associated with a slight increase in clinical deterioration on the wards, while benzodiazepines were associated with a much larger risk for deterioration. This finding raises concern about the safety of benzodiazepine use among ward patients and suggests that increased monitoring of patients receiving these medications may be warranted.

Acknowledgment

The authors thank Nicole Twu for administrative support.

Disclosure

Drs. Churpek and Edelson have a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients. Dr. Churpek is supported by a career development award from the National Heart, Lung, and Blood Institute (K08 HL121080). Dr. Churpek has received honoraria from Chest for invited speaking engagements. In addition, Dr. Edelson has received research support from Philips Healthcare (Andover, Massachusetts), research support from the American Heart Association (Dallas, Texas) and Laerdal Medical (Stavanger, Norway), and research support from Early Sense (Tel Aviv, Israel). She has ownership interest in Quant HC (Chicago, Illinois), which is developing products for risk stratification of hospitalized patients. Dr. Mokhlesi is supported by National Institutes of Health grant R01HL119161. Dr. Mokhlesi has served as a consultant to Philips/Respironics and has received research support from Philips/Respironics. Preliminary versions of these data were presented as a poster presentation at the 2016 meeting of the American Thoracic Society, May 17, 2016; San Francisco, California.

Chronic opioid and benzodiazepine use is common and increasing.^1-5 Outpatient use of these medications has been associated with hospital readmission and death,^6-12 with concurrent use associated with particularly increased risk.^13,14 Less is known about outcomes for hospitalized patients receiving these medications.

More than half of hospital inpatients in the United States receive opioids,¹⁵ many of which are new prescriptions rather than continuation of chronic therapy.^16,17 Less is known about inpatient benzodiazepine administration, but the prevalence may exceed 10% among elderly populations.¹⁸ Hospitalized patients often have comorbidities or physiological disturbances that might increase their risk related to use of these medications. Opioids can cause central and obstructive sleep apneas,^19-21 and benzodiazepines contribute to respiratory depression and airway relaxation.²² Benzodiazepines also impair psychomotor function and recall,²³ which could mediate the recognized risk for delirium and falls in the hospital.^24,25 These findings suggest pathways by which these medications might contribute to clinical deterioration.

Most studies in hospitalized patients have been limited to specific populations^15,26-28 and have not explicitly controlled for severity of illness over time. It remains unclear whether associations identified within particular groups of patients hold true for the broader population of general ward inpatients. Therefore, we aimed to determine the independent association between opioid and benzodiazepine administration and clinical deterioration in ward patients.

Setting and Study Population

We performed an observational cohort study at a 500-bed urban academic hospital. Data were obtained from all adults hospitalized on the wards between November 1, 2008, and January 21, 2016. The study protocol was approved by the University of Chicago Institutional Review Board (IRB#15-0195).

Data Collection

The study utilized de-identified data from the electronic health record (EHR; Epic Systems Corporation, Verona, Wisconsin) and administrative databases collected by the University of Chicago Clinical Research Data Warehouse. Patient age, sex, race, body mass index (BMI), and ward admission source (ie, emergency department (ED), transferred from the intensive care unit (ICU), or directly admitted to the wards) were collected. International Classification of Diseases, 9^th Revision, Clinical Modification (ICD-9-CM) codes were used to identify Elixhauser Comorbidity Index categories.^29,30 Because patients with similar diagnoses (eg, active cancer) are cohorted within particular areas in our hospital, we obtained the ward unit for all patients. Patients who underwent surgery were identified using the hospital’s admission-transfer-discharge database.

To determine severity of illness, routinely collected vital signs and laboratory values were utilized to calculate the electronic cardiac arrest risk triage (eCART) score, an accurate risk score we previously developed and validated for predicting adverse events among ward patients.³¹ If any vital sign or laboratory value was missing, the next available measurement was carried forward. If any value remained missing after this change, the median value for that location (ie, wards, ICU, or ED) was imputed.^32,33 Additionally, patient-reported pain scores at the time of opioid administration were extracted from nursing flowsheets. If no pain score was present at the time of opioid administration, the patient’s previous score was carried forward.

We excluded patients with sickle-cell disease or seizure history and admissions with diagnoses of alcohol withdrawal from the analysis, because these diagnoses were expected to be associated with different medication administration practices compared to other inpatients. We also excluded patients with a tracheostomy because we expected their respiratory monitoring to differ from the other patients in our cohort. Finally, because ward deaths resulting from a comfort care scenario often involve opioids and/or benzodiazepines, ward segments involving comfort care deaths (defined as death without attempted resuscitation) were excluded from the analysis (Supplemental Figure 1). Patients with sickle-cell disease were identified using ICD-9 codes, and encounters during which a seizure may have occurred were identified using a combination of ICD-9 codes and receipt of anti-epileptic medication (Supplemental Table 1). Patients at risk for alcohol withdrawal were identified by the presence of any Clinical Institute Withdrawal Assessment for Alcohol score within nursing flowsheets, and patients with tracheostomies were identified using documentation of ventilator support within their first 12 hours on the wards. In addition to these exclusion criteria, patients with obstructive sleep apnea (OSA) were identified by the following ICD-9 codes: 278.03, 327.23, 780.51, 780.53, and 780.57.

Medications

Ward administrations of opioids and benzodiazepines—dose, route, and administration time—were collected from the EHR. We excluded all administrations in nonward locations such as the ED, ICU, operating room, or procedure suite. Additionally, because patients emergently intubated may receive sedative and analgesic medications to facilitate intubation, and because patients experiencing cardiac arrest are frequently intubated periresuscitation, we a priori excluded all administrations within 15 minutes of a ward cardiac arrest or an intubation.

For consistent comparisons, opioid doses were converted to oral morphine equivalents³⁴ and adjusted by a factor of 15 to reflect the smallest routinely available oral morphine tablet in our hospital (Supplemental Table 2). Benzodiazepine doses were converted to oral lorazepam equivalents (Supplemental Table 2).³⁴ Thus, the independent variables were oral morphine or lorazepam equivalents administered within each 6-hour window. We a priori presumed opioid doses greater than the 99^th percentile (1200 mg) or benzodiazepine doses greater than 10 mg oral lorazepam equivalents within a 6-hour window to be erroneous entries, and replaced these outlier values with the median value for each medication category.

Outcomes

The primary outcome was the composite of ICU transfer or cardiac arrest (loss of pulse with attempted resuscitation) on the wards, with individual outcomes investigated secondarily. An ICU transfer (patient movement from a ward directly to the ICU) was identified using the hospital’s admission-transfer-discharge database. Cardiac arrests were identified using a prospectively validated quality improvement database.³⁵

Because deaths on the wards resulted either from cardiac arrest or from a comfort care scenario, mortality was not studied as an outcome.

Statistical Analysis

Patient characteristics were compared using Student t tests, Wilcoxon rank sum tests, and chi-squared statistics, as appropriate. Unadjusted and adjusted models were created using discrete-time survival analysis,^36-39 which involved dividing time into discrete 6-hour intervals and employing the predictor variables chronologically closest to the beginning of each time window to forecast whether the outcome occurred within each interval. Predictor variables in the adjusted model included patient characteristics (age, sex, BMI, and Elixhauser Agency for Healthcare Research and Quality-Web comorbidities³⁰ [a priori excluding comorbidities recorded for fewer than 1000 admissions from the model]), ward unit, surgical status, prior ICU admission during the hospitalization, cumulative opioid or benzodiazepine dose during the previous 24 hours, and severity of illness (measured by eCART score). The adjusted model for opioids also included the patient’s pain score. Age, eCART score, and pain score were entered linearly while race, BMI (underweight, less than 18.5 kg/m²; normal, 18.5-24.9 kg/m²; overweight, 25.0-29.9 kg/m²; obese, 30-39.9 kg/m²; and severely obese, 40 mg/m² or greater), and ward unit were modeled as categorical variables.

Since repeat hospitalization could confound the results of our study, we performed a sensitivity analysis including only 1 randomly selected hospital admission per patient. We also performed a sensitivity analysis including receipt of both opioids and benzodiazepines, and an interaction term within each ward segment, as well as an analysis in which zolpidem—the most commonly administered nonbenzodiazepine hypnotic medication in our hospital—was included along with both opioids and benzodiazepines. Finally, we performed a sensitivity analysis replacing missing pain scores with imputed values ranging from 0 to the median ward pain score.

We also performed subgroup analyses of adjusted models across age quartiles and for each BMI category, as well as for surgical status, OSA status, gender, time of medication administration, and route of administration (intravenous vs. oral). We also performed an analysis across pain score severity⁴⁰ to determine whether these medications produce differential effects at various levels of pain.

All tests of significance used a 2-sided P value less than 0.05. Statistical analyses were completed using Stata version 14.1 (StataCorp, LLC, College Station, Texas).

jhm012060428_f1.jpg

Patient Characteristics

A total of 144,895 admissions, from 75,369 patients, had ward vital signs or laboratory values documented during the study period. Ward segments from 634 admissions were excluded due to comfort care status, which resulted in exclusion of 479 complete patient admissions. Additionally, 139 patients with tracheostomies were excluded. Furthermore, 2934 patient admissions with a sickle-cell diagnosis were excluded, of which 95% (n = 2791) received an opioid and 11% (n = 310) received a benzodiazepine. Another 14,029 admissions associated with seizures, 6134 admissions involving alcohol withdrawal, and 1332 with both were excluded, of which 66% (n = 14,174) received an opioid and 35% (n = 7504) received a benzodiazepine. After exclusions, 120,518 admissions were included in the final analysis, with 67% (n = 80,463) associated with at least 1 administration of an opioid and 21% (n = 25,279) associated with at least 1 benzodiazepine administration.

In total, there were 672,851 intervals when an opioid was administered during the study, with a median dose of 12 mg oral morphine equivalents (interquartile range, 8-30). Of these, 21,634 doses were replaced due to outlier status outside the 99^th percentile. Patients receiving opioids were younger (median age 56 vs 61 years), less likely to be African American (48% vs 59%), more likely to have undergone surgery (18% vs 6%), and less likely to have most noncancer medical comorbidities than those who never received an opioid (all P < 0.001) (Table 1).

jhm012060428_t1.jpg

jhm012060428_t2.jpg

Patient Outcomes

The incidence of the composite outcome was higher in admissions with at least 1 opioid medication than those without an opioid (7% vs. 4%, P < 0.001), and in admissions with at least 1 dose of benzodiazepines compared to those without a benzodiazepine (11% vs. 4%, P < 0.001) (Table 2).

Within 6-hour segments, increasing doses of opioids were associated with an initial decrease in the frequency of the composite outcome followed by a dose-related increase in the frequency of the composite outcome with morphine equivalents greater than 45 mg. By contrast, the frequency of the composite outcome increased with additional benzodiazepine equivalents (Figure).

In the adjusted model, opioid administration was associated with increased risk for the composite outcome (Table 3) in a dose-dependent fashion, with each 15 mg oral morphine equivalent associated with a 1.9% increase in the odds of ICU transfer or cardiac arrest within the subsequent 6-hour time interval (odds ratio [OR], 1.019; 95% confidence interval [CI], 1.013-1.026; P < 0.001).

jhm012060428_t3.jpg

Sensitivity Analyses

A sensitivity analysis including 1 randomly selected hospitalization per patient involved 67,097 admissions and found results similar to the primary analysis, with each 15 mg oral morphine equivalent associated with a 1.9% increase in the odds of the composite outcome (OR, 1.019; 95% CI, 1.011-1.028; P < 0.001) and each 1 mg oral lorazepam equivalent associated with a 41% increase in the odds of the composite outcome (OR, 1.41; 95% CI, 1.21-1.65; P < 0.001). Inclusion of both opioids and benzodiazepines in the adjusted model again yielded results similar to the main analysis for both opioids (OR, 1.020; 95% CI, 1.013-1.026; P < 0.001) and benzodiazepines (OR, 1.35; 95% CI, 1.18-1.54; P < 0.001), without a significant interaction detected (P = 0.09). These results were unchanged with the addition of zolpidem to the model as an additional potential confounder, and zolpidem did not increase the risk of the study outcomes (P = 0.2).

A final sensitivity analysis for the opioid model involved replacing missing pain scores with imputed values ranging from 0 to the median ward score, which was 5. The results of these analyses did not differ from the primary model and were consistent regardless of imputation value (OR, 1.018; 95% CI, 1.012-1.023; P < 0.001).

Subgroup Analyses

Analyses of opioid administration by subgroup (sex, age quartiles, BMI categories, OSA diagnosis, surgical status, daytime/nighttime medication administration, IV/PO administration, and pain severity) yielded similar results to the overall analysis (Supplemental Figure 2). Subgroup analysis of patients receiving benzodiazepines revealed similarly increased adjusted odds of the composite outcome across strata of gender, BMI, surgical status, and medication administration time (Supplemental Figure 3). Notably, patients older than 70 years who received a benzodiazepine were at 64% increased odds of the composite outcome (OR, 1.64; 95% CI, 1.30-2.08), compared to 2% to 38% increased risk for patients under 70 years. Finally, IV doses of benzodiazepines were associated with 48% increased odds for deterioration (OR, 1.48; 95% CI, 1.18-1.84; P = 0.001), compared to a nonsignificant 14% increase in the odds for PO doses (OR, 1.14; 95% CI, 0.99-1.31; P = 0.066).

In a large, single-center, observational study of ward inpatients, we found that opioid use was associated with a small but significant increased risk for clinical deterioration on the wards, with every 15 mg oral morphine equivalent increasing the odds of ICU transfer or cardiac arrest in the next 6 hours by 1.9%. Benzodiazepines were associated with a much higher risk: each equivalent of 1 mg of oral lorazepam increased the odds of ICU transfer or cardiac arrest by almost 30%. These results have important implications for care at the bedside of hospitalized ward patients and suggest the need for closer monitoring after receipt of these medications, particularly benzodiazepines.

Previous work has described negative effects of opioid medications among select inpatient populations. In surgical patients, opioids have been associated with hospital readmission, increased length of stay, and hospital mortality.^26,28 More recently, Herzig et al.¹⁵ found more adverse events in nonsurgical ward patients within the hospitals prescribing opioids the most frequently. These studies may have been limited by the populations studied and the inability to control for confounders such as severity of illness and pain score. Our study expands these findings to a more generalizable population and shows that even after adjustment for potential confounders, such as severity of illness, pain score, and medication dose, opioids are associated with increased short-term risk of clinical deterioration.

By contrast, few studies have characterized the risks associated with benzodiazepine use among ward inpatients. Recently, Overdyk et al.²⁷ found that inpatient use of opioids and sedatives was associated with increased risk for cardiac arrest and hospital death. However, this study included ICU patients, which may confound the results, as ICU patients often receive high doses of opioids or benzodiazepines to facilitate mechanical ventilation or other invasive procedures, while also having a particularly high risk of adverse outcomes like cardiac arrest and inhospital death.

Several mechanisms may explain the magnitude of effect seen with regard to benzodiazepines. First, benzodiazepines may directly produce clinical deterioration by decreased respiratory drive, diminished airway tone, or hemodynamic decompensation. It is possible that the broad spectrum of cardiorespiratory side effects of benzodiazepines—and potential unpredictability of these effects—increases the difficulty of observation and management for patients receiving them. This difficulty may be compounded with intravenous administration of benzodiazepines, which was associated with a higher risk for deterioration than oral doses in our cohort. Alternatively, benzodiazepines may contribute to clinical decompensation by masking signs of deterioration such as encephalopathy or vital sign instability like tachycardia or tachypnea that may be mistaken as anxiety. Notably, while our hospital has a nursing-driven protocol for monitoring patients receiving opioids (in which pain is serially assessed, leading to additional bedside observation), we do not have protocols for ward patients receiving benzodiazepines. Finally, although we found that orders for opioids and benzodiazepines were more common in white patients than African American patients, this finding may be due to differences in the types or number of medical comorbidities experienced by these patients.

Our study has several strengths, including the large number of admissions we included. Additionally, we included a broad range of medical and surgical ward admissions, which should increase the generalizability of our results. Further, our rates of ICU transfer are in line with data reported from other groups,^41,42 which again may add to the generalizability of our findings. We also addressed many potential confounders by including patient characteristics, individual ward units, and (for opioids) pain score in our model, and by controlling for severity of illness with the eCART score, an accurate predictor of ICU transfer and ward cardiac arrest within our population.^32,37 Finally, our robust methodology allowed us to include acute and cumulative medication doses, as well as time, in the model. By performing a discrete-time survival analysis, we were able to evaluate receipt of opioids and benzodiazepines—as well as risk for clinical deterioration—longitudinally, lending strength to our results.

Limitations of our study include its single-center cohort, which may reduce generalizability to other populations. Additionally, because we could not validate the accuracy of—or adherence to—outpatient medication lists, we were unable to identify chronic opioid or benzodiazepine users by these lists. However, patients chronically taking opioids or benzodiazepines would likely receive doses each hospital day; by including 24-hour cumulative doses in our model, we attempted to adjust for some portion of their chronic use. Also, because evaluation of delirium was not objectively recorded in our dataset, we were unable to evaluate the relationship between receipt of these medications and development of delirium, which is an important outcome for hospitalized patients. Finally, neither the diagnoses for which these medications were prescribed, nor the reason for ICU transfer, were present in our dataset, which leaves open the possibility of unmeasured confounding.

After adjustment for important confounders including severity of illness, medication dose, and time, opioids were associated with a slight increase in clinical deterioration on the wards, while benzodiazepines were associated with a much larger risk for deterioration. This finding raises concern about the safety of benzodiazepine use among ward patients and suggests that increased monitoring of patients receiving these medications may be warranted.

Acknowledgment

The authors thank Nicole Twu for administrative support.

Disclosure

Drs. Churpek and Edelson have a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients. Dr. Churpek is supported by a career development award from the National Heart, Lung, and Blood Institute (K08 HL121080). Dr. Churpek has received honoraria from Chest for invited speaking engagements. In addition, Dr. Edelson has received research support from Philips Healthcare (Andover, Massachusetts), research support from the American Heart Association (Dallas, Texas) and Laerdal Medical (Stavanger, Norway), and research support from Early Sense (Tel Aviv, Israel). She has ownership interest in Quant HC (Chicago, Illinois), which is developing products for risk stratification of hospitalized patients. Dr. Mokhlesi is supported by National Institutes of Health grant R01HL119161. Dr. Mokhlesi has served as a consultant to Philips/Respironics and has received research support from Philips/Respironics. Preliminary versions of these data were presented as a poster presentation at the 2016 meeting of the American Thoracic Society, May 17, 2016; San Francisco, California.

Yes. Obesity hypoventilation syndrome (OHS) is often undiagnosed and greatly increases perioperative risk. Therefore, we recommend trying to detect OHS in a timely manner. Treatment should begin without delay to avoid adverse perioperative outcomes, which can include acute-on-chronic respiratory failure requiring intensive-care monitoring and invasive mechanical ventilation, or death.

ALSO CALLED PICKWICKIAN SYNDROME

OHS is also known as Pickwickian syndrome, named for a character—a “fat boy” who is constantly falling asleep—in The Posthumous Papers of the Pickwick Club by Charles Dickens.

Salient features of OHS are:

• Obesity (body mass index ≥ 30 kg/m²)
• Sleep-disordered breathing (most patients with OHS are morbidly obese and have severe obstructive sleep apnea1)
• Chronic daytime alveolar hypoventilation: ie, Paco₂ ≥ 45 mm Hg (normal range 35–45 mm Hg) and Pao₂ < 70 mm Hg1 (normal range 85–95 mm Hg)
• No other identifiable cause of hypoventilation such as pulmonary disease (severe obstructive or restrictive), chest wall deformities, severe hypothyroidism, or neuromuscular disease.

WHY SCREEN FOR OHS?

Both obstructive sleep apnea and OHS worsen quality of life and increase the risk of serious disease and death.^2–3 Patients with severe sleep apnea, particularly those with hypercapnia (ie, OHS) are at higher risk of cardiopulmonary complications in the perioperative period.

Compared with eucapnic patients with obstructive sleep apnea, patients with OHS have higher health care expenses, are at higher risk of developing serious cardiovascular diseases such as pulmonary hypertension and congestive heart failure, and are more likely to die sooner.^4,5

Nowbar et al⁵ prospectively followed a group of severely obese patients after hospital discharge. At 18 months, 23% of those with OHS had died, compared with 9% of those without OHS. The groups were well matched for body mass index, age, and a number of comorbid conditions. Most of the deaths occurred in the first 3 months after hospital discharge. During the hospital stay, more patients with OHS were admitted to the intensive care unit and needed endotracheal intubation and mechanical ventilation, and more were discharged to a long-term facility.

A high level of suspicion can lead to early recognition and treatment, which may reduce the rate of adverse outcomes associated with undiagnosed and untreated OHS. Routine screening for hypercapnia in patients with sleep apnea might help to identify patients with OHS and allow for modifications in surgical approach, anesthetic technique, and postoperative monitoring, increasing patient safety.

HOW PREVALENT IS OHS?

Obstructive sleep apnea affects up to 20% of US adults and is undiagnosed and untreated in up to 90% of cases.⁶ Simple screening questionnaires have been shown to reliably identify patients at risk.^7,8

To date, no population-based prevalence studies of OHS have been done.

The overall prevalence of OHS in patients with obstructive sleep apnea is better studied: multiple prospective and retrospective studies across various geographic regions with a variety of racial or ethnic populations have shown it to be between 10% and 20%.^1,9 This range is very consistent among studies performed in Europe, the United States, and Japan, whether retrospective or prospective, and whether large or small.

The prevalence of OHS in the general adult population in the United States can, however, be estimated. If approximately 5% of the general US population has severe obesity (body mass index ≤ 40 kg/m²), if half of patients with severe obesity have obstructive sleep apnea,¹⁰ and if 15% of severely obese patients with sleep apnea have OHS, then a conservative estimated prevalence of OHS in the general adult US population is 0.37% (1 in 270 adults).

WHAT CAN BE DONE BEFORE ELECTIVE SURGERY?

Patients with OHS have an elevated serum bicarbonate level due to metabolic compensation for chronic respiratory acidosis. Moreover, they may have mild hypoxemia during wakefulness as measured by finger pulse oximetry.

The serum venous bicarbonate level is an easy and reasonable test to screen for hypercapnia in obese patients with obstructive sleep apnea because it is readily available, physiologically sensible, and less invasive than arterial puncture to measure blood gases.⁹

Arterial blood gas measurements, however, should be obtained to confirm the presence and severity of daytime hypercapnia in obese patients with hypoxemia during wakefulness or an elevated serum bicarbonate level.

Pulmonary function testing and chest imaging can exclude other causes of hypercapnia if hypercapnia is confirmed.

An overnight, attended polysomnographic study in a sleep laboratory is ultimately needed to establish the diagnosis and severity of obstructive sleep apnea and to titrate continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BPAP) therapy. Since most patients with OHS have severe obstructive sleep apnea, in-laboratory attended polysomnography allows the clinician to both diagnose and intervene with PAP therapy (a “split-night” study). Home titration with an auto-CPAP device is not recommended because it does not have the ability to titrate PAP pressures in response to hypoxemia or hypoventilation. Patients with OHS require attended, laboratory-based PAP titration with or without supplemental oxygen.

CPAP or BPAP therapy should be started during the few days or weeks before surgery, and adherence should be emphasized. Anesthesiologists might reconsider the choice of anesthetic technique in favor of regional anesthesia and modify postoperative pain management to reduce opioid requirements. Reinstituting CPAP or BPAP therapy upon extubation or arrival in the postoperative recovery unit can further reduce the risk of respiratory complications. Additional monitoring such as continuous pulse oximetry when the patient is on the general ward should be considered.

Yes. Obesity hypoventilation syndrome (OHS) is often undiagnosed and greatly increases perioperative risk. Therefore, we recommend trying to detect OHS in a timely manner. Treatment should begin without delay to avoid adverse perioperative outcomes, which can include acute-on-chronic respiratory failure requiring intensive-care monitoring and invasive mechanical ventilation, or death.

ALSO CALLED PICKWICKIAN SYNDROME

OHS is also known as Pickwickian syndrome, named for a character—a “fat boy” who is constantly falling asleep—in The Posthumous Papers of the Pickwick Club by Charles Dickens.

Salient features of OHS are:

• Obesity (body mass index ≥ 30 kg/m²)
• Sleep-disordered breathing (most patients with OHS are morbidly obese and have severe obstructive sleep apnea1)
• Chronic daytime alveolar hypoventilation: ie, Paco₂ ≥ 45 mm Hg (normal range 35–45 mm Hg) and Pao₂ < 70 mm Hg1 (normal range 85–95 mm Hg)
• No other identifiable cause of hypoventilation such as pulmonary disease (severe obstructive or restrictive), chest wall deformities, severe hypothyroidism, or neuromuscular disease.

WHY SCREEN FOR OHS?

Both obstructive sleep apnea and OHS worsen quality of life and increase the risk of serious disease and death.^2–3 Patients with severe sleep apnea, particularly those with hypercapnia (ie, OHS) are at higher risk of cardiopulmonary complications in the perioperative period.

Compared with eucapnic patients with obstructive sleep apnea, patients with OHS have higher health care expenses, are at higher risk of developing serious cardiovascular diseases such as pulmonary hypertension and congestive heart failure, and are more likely to die sooner.^4,5

Nowbar et al⁵ prospectively followed a group of severely obese patients after hospital discharge. At 18 months, 23% of those with OHS had died, compared with 9% of those without OHS. The groups were well matched for body mass index, age, and a number of comorbid conditions. Most of the deaths occurred in the first 3 months after hospital discharge. During the hospital stay, more patients with OHS were admitted to the intensive care unit and needed endotracheal intubation and mechanical ventilation, and more were discharged to a long-term facility.

A high level of suspicion can lead to early recognition and treatment, which may reduce the rate of adverse outcomes associated with undiagnosed and untreated OHS. Routine screening for hypercapnia in patients with sleep apnea might help to identify patients with OHS and allow for modifications in surgical approach, anesthetic technique, and postoperative monitoring, increasing patient safety.

HOW PREVALENT IS OHS?

Obstructive sleep apnea affects up to 20% of US adults and is undiagnosed and untreated in up to 90% of cases.⁶ Simple screening questionnaires have been shown to reliably identify patients at risk.^7,8

To date, no population-based prevalence studies of OHS have been done.

The overall prevalence of OHS in patients with obstructive sleep apnea is better studied: multiple prospective and retrospective studies across various geographic regions with a variety of racial or ethnic populations have shown it to be between 10% and 20%.^1,9 This range is very consistent among studies performed in Europe, the United States, and Japan, whether retrospective or prospective, and whether large or small.

The prevalence of OHS in the general adult population in the United States can, however, be estimated. If approximately 5% of the general US population has severe obesity (body mass index ≤ 40 kg/m²), if half of patients with severe obesity have obstructive sleep apnea,¹⁰ and if 15% of severely obese patients with sleep apnea have OHS, then a conservative estimated prevalence of OHS in the general adult US population is 0.37% (1 in 270 adults).

WHAT CAN BE DONE BEFORE ELECTIVE SURGERY?

Patients with OHS have an elevated serum bicarbonate level due to metabolic compensation for chronic respiratory acidosis. Moreover, they may have mild hypoxemia during wakefulness as measured by finger pulse oximetry.

The serum venous bicarbonate level is an easy and reasonable test to screen for hypercapnia in obese patients with obstructive sleep apnea because it is readily available, physiologically sensible, and less invasive than arterial puncture to measure blood gases.⁹

Arterial blood gas measurements, however, should be obtained to confirm the presence and severity of daytime hypercapnia in obese patients with hypoxemia during wakefulness or an elevated serum bicarbonate level.

Pulmonary function testing and chest imaging can exclude other causes of hypercapnia if hypercapnia is confirmed.

An overnight, attended polysomnographic study in a sleep laboratory is ultimately needed to establish the diagnosis and severity of obstructive sleep apnea and to titrate continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BPAP) therapy. Since most patients with OHS have severe obstructive sleep apnea, in-laboratory attended polysomnography allows the clinician to both diagnose and intervene with PAP therapy (a “split-night” study). Home titration with an auto-CPAP device is not recommended because it does not have the ability to titrate PAP pressures in response to hypoxemia or hypoventilation. Patients with OHS require attended, laboratory-based PAP titration with or without supplemental oxygen.

CPAP or BPAP therapy should be started during the few days or weeks before surgery, and adherence should be emphasized. Anesthesiologists might reconsider the choice of anesthetic technique in favor of regional anesthesia and modify postoperative pain management to reduce opioid requirements. Reinstituting CPAP or BPAP therapy upon extubation or arrival in the postoperative recovery unit can further reduce the risk of respiratory complications. Additional monitoring such as continuous pulse oximetry when the patient is on the general ward should be considered.

Yes. Obesity hypoventilation syndrome (OHS) is often undiagnosed and greatly increases perioperative risk. Therefore, we recommend trying to detect OHS in a timely manner. Treatment should begin without delay to avoid adverse perioperative outcomes, which can include acute-on-chronic respiratory failure requiring intensive-care monitoring and invasive mechanical ventilation, or death.

ALSO CALLED PICKWICKIAN SYNDROME

OHS is also known as Pickwickian syndrome, named for a character—a “fat boy” who is constantly falling asleep—in The Posthumous Papers of the Pickwick Club by Charles Dickens.

Salient features of OHS are:

• Obesity (body mass index ≥ 30 kg/m²)
• Sleep-disordered breathing (most patients with OHS are morbidly obese and have severe obstructive sleep apnea1)
• Chronic daytime alveolar hypoventilation: ie, Paco₂ ≥ 45 mm Hg (normal range 35–45 mm Hg) and Pao₂ < 70 mm Hg1 (normal range 85–95 mm Hg)
• No other identifiable cause of hypoventilation such as pulmonary disease (severe obstructive or restrictive), chest wall deformities, severe hypothyroidism, or neuromuscular disease.

WHY SCREEN FOR OHS?

Both obstructive sleep apnea and OHS worsen quality of life and increase the risk of serious disease and death.^2–3 Patients with severe sleep apnea, particularly those with hypercapnia (ie, OHS) are at higher risk of cardiopulmonary complications in the perioperative period.

Compared with eucapnic patients with obstructive sleep apnea, patients with OHS have higher health care expenses, are at higher risk of developing serious cardiovascular diseases such as pulmonary hypertension and congestive heart failure, and are more likely to die sooner.^4,5

Nowbar et al⁵ prospectively followed a group of severely obese patients after hospital discharge. At 18 months, 23% of those with OHS had died, compared with 9% of those without OHS. The groups were well matched for body mass index, age, and a number of comorbid conditions. Most of the deaths occurred in the first 3 months after hospital discharge. During the hospital stay, more patients with OHS were admitted to the intensive care unit and needed endotracheal intubation and mechanical ventilation, and more were discharged to a long-term facility.

A high level of suspicion can lead to early recognition and treatment, which may reduce the rate of adverse outcomes associated with undiagnosed and untreated OHS. Routine screening for hypercapnia in patients with sleep apnea might help to identify patients with OHS and allow for modifications in surgical approach, anesthetic technique, and postoperative monitoring, increasing patient safety.

HOW PREVALENT IS OHS?

Obstructive sleep apnea affects up to 20% of US adults and is undiagnosed and untreated in up to 90% of cases.⁶ Simple screening questionnaires have been shown to reliably identify patients at risk.^7,8

To date, no population-based prevalence studies of OHS have been done.

The overall prevalence of OHS in patients with obstructive sleep apnea is better studied: multiple prospective and retrospective studies across various geographic regions with a variety of racial or ethnic populations have shown it to be between 10% and 20%.^1,9 This range is very consistent among studies performed in Europe, the United States, and Japan, whether retrospective or prospective, and whether large or small.

The prevalence of OHS in the general adult population in the United States can, however, be estimated. If approximately 5% of the general US population has severe obesity (body mass index ≤ 40 kg/m²), if half of patients with severe obesity have obstructive sleep apnea,¹⁰ and if 15% of severely obese patients with sleep apnea have OHS, then a conservative estimated prevalence of OHS in the general adult US population is 0.37% (1 in 270 adults).

WHAT CAN BE DONE BEFORE ELECTIVE SURGERY?

Patients with OHS have an elevated serum bicarbonate level due to metabolic compensation for chronic respiratory acidosis. Moreover, they may have mild hypoxemia during wakefulness as measured by finger pulse oximetry.

The serum venous bicarbonate level is an easy and reasonable test to screen for hypercapnia in obese patients with obstructive sleep apnea because it is readily available, physiologically sensible, and less invasive than arterial puncture to measure blood gases.⁹

Arterial blood gas measurements, however, should be obtained to confirm the presence and severity of daytime hypercapnia in obese patients with hypoxemia during wakefulness or an elevated serum bicarbonate level.

Pulmonary function testing and chest imaging can exclude other causes of hypercapnia if hypercapnia is confirmed.

An overnight, attended polysomnographic study in a sleep laboratory is ultimately needed to establish the diagnosis and severity of obstructive sleep apnea and to titrate continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BPAP) therapy. Since most patients with OHS have severe obstructive sleep apnea, in-laboratory attended polysomnography allows the clinician to both diagnose and intervene with PAP therapy (a “split-night” study). Home titration with an auto-CPAP device is not recommended because it does not have the ability to titrate PAP pressures in response to hypoxemia or hypoventilation. Patients with OHS require attended, laboratory-based PAP titration with or without supplemental oxygen.

CPAP or BPAP therapy should be started during the few days or weeks before surgery, and adherence should be emphasized. Anesthesiologists might reconsider the choice of anesthetic technique in favor of regional anesthesia and modify postoperative pain management to reduce opioid requirements. Reinstituting CPAP or BPAP therapy upon extubation or arrival in the postoperative recovery unit can further reduce the risk of respiratory complications. Additional monitoring such as continuous pulse oximetry when the patient is on the general ward should be considered.