Phd

In this issue of the Journal of Hospital Medicine, McAlister et al.¹ compared the ability of the Clinical Frailty Scale (CFS) and the Hospital Frailty Risk Score (HFRS) to predict 30-day readmission or death. The authors prospectively assessed adult patients aged ≥18 years without cognitive impairment being discharged back to the community after medical admissions. They demonstrated only modest overlap in frailty designation between HFRS and CFS and concluded that CFS is better than HFRS for predicting the outcomes of interest.

Before a prediction rule is widely adopted for use in routine practice, robust external validation is needed.² Factors such as the prevalence of disease in a population, the clinical competencies of a health system, the socioeconomic status, and the ethnicity of the population can all affect how well a clinical rule performs, but may not become apparent until a prospective validation in a different population is attempted.

In developing the HFRS, Gilbert et al. aimed to create a low-cost, highly generalizable method of identifying frailty using International Classification of Diseases (ICD) 10 billing codes.³ The derivation and validation cohorts for HFRS included older adults aged >75 years in the United Kingdom, many of whom had cognitive impairment. Therefore, it is not surprising that the tool behaved very differently in the younger Canadian cohort described by McAlister et al. where persons with cognitive impairment were excluded. That the HFRS had less predictability in the Canadian cohort may simply indicate that it performs better in an older population with cognitive vulnerabilities; given the frailty constructs of the CFS, it may provide less insights in older populations.

We applaud the efforts to find a way to better identify high-risk groups of adults. We also appreciate the increasing attention to function and other frailty-related domains in risk prediction models. Nevertheless, we recommend caution in using any of the many existing frailty indices⁴ in risk prediction tools unless it is clear what domains of frailty are most relevant for the predicted outcome and what population is the subject of interest.

One of the challenges of choosing an appropriate frailty tool is that different tools are measuring different domains or constructs of frailty. Most consider frailty either as a physical phenotype⁵ or as a more multifaceted construct with impairments in physical and mental health, function, and social interaction.⁶ There is often poor overlap between those individuals identified as frail by different measures, highlighting that they are in fact identifying different people within the population studied and have different predictive abilities.

An ideal frailty tool for clinical use would allow clinicians to identify high-risk patients relative to specific outcome(s) in real time prior to discharge from hospital or prior to a sentinel event in the community. CFS can be calculated at the bedside, but HFRS calculation can only be done retrospectively when medical records are coded for claims after discharge. This makes HFRS more suited to research or post hoc quality measure work and CFS more suited to clinical use as the authors describe.

Although using a frailty indicator to help determine those at high risk of early readmission is an important objective, the presence of frailty accounts for only part of a person’s risk for readmission or other untoward events. Reasons for readmissions are complex and often heavily weighted on a lack of social and community supports. A deeper understanding of the reasons for readmission is needed to establish whether readmission of these complex patients has more to do with frailty or other drivers such as poor transitions of care.

The prevalence of frailty will continue to increase as our population ages. Definitions of frailty vary, but there is a broad agreement that frailty, regardless of how it is constructed, increases with age, results in multisystem changes, and leads to increased healthcare utilization and costs. Preventing the development of frailty, identifying frailty, and developing interventions to address frailty in and out of the hospital setting are all vital. We welcome further research regarding the biopsychosocial constructs of frailty, how they overlap with the frailty phenotype, and how these constructs inform both our understanding of frailty and the use of frailty tools.

Disclosures

The authors have no conflicts of interest to report.

In this issue of the Journal of Hospital Medicine, McAlister et al.¹ compared the ability of the Clinical Frailty Scale (CFS) and the Hospital Frailty Risk Score (HFRS) to predict 30-day readmission or death. The authors prospectively assessed adult patients aged ≥18 years without cognitive impairment being discharged back to the community after medical admissions. They demonstrated only modest overlap in frailty designation between HFRS and CFS and concluded that CFS is better than HFRS for predicting the outcomes of interest.

Before a prediction rule is widely adopted for use in routine practice, robust external validation is needed.² Factors such as the prevalence of disease in a population, the clinical competencies of a health system, the socioeconomic status, and the ethnicity of the population can all affect how well a clinical rule performs, but may not become apparent until a prospective validation in a different population is attempted.

In developing the HFRS, Gilbert et al. aimed to create a low-cost, highly generalizable method of identifying frailty using International Classification of Diseases (ICD) 10 billing codes.³ The derivation and validation cohorts for HFRS included older adults aged >75 years in the United Kingdom, many of whom had cognitive impairment. Therefore, it is not surprising that the tool behaved very differently in the younger Canadian cohort described by McAlister et al. where persons with cognitive impairment were excluded. That the HFRS had less predictability in the Canadian cohort may simply indicate that it performs better in an older population with cognitive vulnerabilities; given the frailty constructs of the CFS, it may provide less insights in older populations.

We applaud the efforts to find a way to better identify high-risk groups of adults. We also appreciate the increasing attention to function and other frailty-related domains in risk prediction models. Nevertheless, we recommend caution in using any of the many existing frailty indices⁴ in risk prediction tools unless it is clear what domains of frailty are most relevant for the predicted outcome and what population is the subject of interest.

One of the challenges of choosing an appropriate frailty tool is that different tools are measuring different domains or constructs of frailty. Most consider frailty either as a physical phenotype⁵ or as a more multifaceted construct with impairments in physical and mental health, function, and social interaction.⁶ There is often poor overlap between those individuals identified as frail by different measures, highlighting that they are in fact identifying different people within the population studied and have different predictive abilities.

An ideal frailty tool for clinical use would allow clinicians to identify high-risk patients relative to specific outcome(s) in real time prior to discharge from hospital or prior to a sentinel event in the community. CFS can be calculated at the bedside, but HFRS calculation can only be done retrospectively when medical records are coded for claims after discharge. This makes HFRS more suited to research or post hoc quality measure work and CFS more suited to clinical use as the authors describe.

Although using a frailty indicator to help determine those at high risk of early readmission is an important objective, the presence of frailty accounts for only part of a person’s risk for readmission or other untoward events. Reasons for readmissions are complex and often heavily weighted on a lack of social and community supports. A deeper understanding of the reasons for readmission is needed to establish whether readmission of these complex patients has more to do with frailty or other drivers such as poor transitions of care.

The prevalence of frailty will continue to increase as our population ages. Definitions of frailty vary, but there is a broad agreement that frailty, regardless of how it is constructed, increases with age, results in multisystem changes, and leads to increased healthcare utilization and costs. Preventing the development of frailty, identifying frailty, and developing interventions to address frailty in and out of the hospital setting are all vital. We welcome further research regarding the biopsychosocial constructs of frailty, how they overlap with the frailty phenotype, and how these constructs inform both our understanding of frailty and the use of frailty tools.

Disclosures

The authors have no conflicts of interest to report.

In this issue of the Journal of Hospital Medicine, McAlister et al.¹ compared the ability of the Clinical Frailty Scale (CFS) and the Hospital Frailty Risk Score (HFRS) to predict 30-day readmission or death. The authors prospectively assessed adult patients aged ≥18 years without cognitive impairment being discharged back to the community after medical admissions. They demonstrated only modest overlap in frailty designation between HFRS and CFS and concluded that CFS is better than HFRS for predicting the outcomes of interest.

Before a prediction rule is widely adopted for use in routine practice, robust external validation is needed.² Factors such as the prevalence of disease in a population, the clinical competencies of a health system, the socioeconomic status, and the ethnicity of the population can all affect how well a clinical rule performs, but may not become apparent until a prospective validation in a different population is attempted.

In developing the HFRS, Gilbert et al. aimed to create a low-cost, highly generalizable method of identifying frailty using International Classification of Diseases (ICD) 10 billing codes.³ The derivation and validation cohorts for HFRS included older adults aged >75 years in the United Kingdom, many of whom had cognitive impairment. Therefore, it is not surprising that the tool behaved very differently in the younger Canadian cohort described by McAlister et al. where persons with cognitive impairment were excluded. That the HFRS had less predictability in the Canadian cohort may simply indicate that it performs better in an older population with cognitive vulnerabilities; given the frailty constructs of the CFS, it may provide less insights in older populations.

We applaud the efforts to find a way to better identify high-risk groups of adults. We also appreciate the increasing attention to function and other frailty-related domains in risk prediction models. Nevertheless, we recommend caution in using any of the many existing frailty indices⁴ in risk prediction tools unless it is clear what domains of frailty are most relevant for the predicted outcome and what population is the subject of interest.

One of the challenges of choosing an appropriate frailty tool is that different tools are measuring different domains or constructs of frailty. Most consider frailty either as a physical phenotype⁵ or as a more multifaceted construct with impairments in physical and mental health, function, and social interaction.⁶ There is often poor overlap between those individuals identified as frail by different measures, highlighting that they are in fact identifying different people within the population studied and have different predictive abilities.

An ideal frailty tool for clinical use would allow clinicians to identify high-risk patients relative to specific outcome(s) in real time prior to discharge from hospital or prior to a sentinel event in the community. CFS can be calculated at the bedside, but HFRS calculation can only be done retrospectively when medical records are coded for claims after discharge. This makes HFRS more suited to research or post hoc quality measure work and CFS more suited to clinical use as the authors describe.

Although using a frailty indicator to help determine those at high risk of early readmission is an important objective, the presence of frailty accounts for only part of a person’s risk for readmission or other untoward events. Reasons for readmissions are complex and often heavily weighted on a lack of social and community supports. A deeper understanding of the reasons for readmission is needed to establish whether readmission of these complex patients has more to do with frailty or other drivers such as poor transitions of care.

The prevalence of frailty will continue to increase as our population ages. Definitions of frailty vary, but there is a broad agreement that frailty, regardless of how it is constructed, increases with age, results in multisystem changes, and leads to increased healthcare utilization and costs. Preventing the development of frailty, identifying frailty, and developing interventions to address frailty in and out of the hospital setting are all vital. We welcome further research regarding the biopsychosocial constructs of frailty, how they overlap with the frailty phenotype, and how these constructs inform both our understanding of frailty and the use of frailty tools.

Disclosures

The authors have no conflicts of interest to report.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

Increasingly, metrics such as length of stay (LOS) and readmissions are being utilized in the United States to assess quality of healthcare because these factors may represent opportunities to reduce cost and improve healthcare delivery.^1-8 However, the relatively low rate of pediatric readmissions,⁹ coupled with limited data regarding recommended LOS or best practices to prevent readmissions in children, challenges the ability of hospitals to safely reduce LOS and readmission rates for children.^10–12

In adults, weekend admission is associated with prolonged LOS, increased readmission rates, and increased risk of mortality.^13-21 This association is referred to as the “weekend effect.” While the weekend effect has been examined in children, the results of these studies have been variable, with some studies supporting this association and others refuting it.^22-31 In contrast to patient demographic and clinical characteristics that are known to affect LOS and readmissions,³² the weekend effect represents a potentially modifiable aspect of a hospitalization that could be targeted to improve healthcare delivery.

With increasing national attention toward improving quality of care and reducing LOS and healthcare costs, more definitive evidence of the weekend effect is necessary to prioritize resource use at both the local and national levels. Therefore, we sought to determine the association of weekend admission and weekend discharge on LOS and 30-day readmissions, respectively, among a national cohort of children. We hypothesized that children admitted on the weekend would have longer LOS, whereas those discharged on the weekend would have higher readmission rates.

Study Design and Data Source

We conducted a multicenter, retrospective, cross-sectional study. Data were obtained from the Pediatric Health Information System (PHIS), an administrative and billing database of 46 free-standing tertiary care pediatric hospitals affiliated with the Children’s Hospital Association (Lenexa, Kansas). Patient data are de-identified within PHIS; however, encrypted patient identifiers allow individual patients to be followed across visits. This study was not considered human subjects research by the policies of the Cincinnati Children’s Hospital Institutional Review Board.

Participants

We included hospitalizations to a PHIS-participating hospital for children aged 0-17 years between October 1, 2014 and September 30, 2015. We excluded children who were transferred from/to another institution, left against medical advice, or died in the hospital because these indications may result in incomplete LOS information and would not consistently contribute to readmission rates. We also excluded birth hospitalizations and children admitted for planned procedures. Birth hospitalizations were defined as hospitalizations that began on the day of birth. Planned procedures were identified using methodology previously described by Berry et al.⁹ With the use of this methodology, a planned procedure was identified if the coded primary procedure was one in which >80% of cases (eg, spinal fusion) are scheduled in advance. Finally, we excluded data from three hospitals due to incomplete data (eg, no admission or discharge time recorded).

Main Exposures

No standard definition of weekend admission or discharge was identified in the literature.³³ Thus, we defined a weekend admission as an admission between 3:00 pm Friday and 2:59 pm Sunday and a weekend discharge as a discharge between 3:00 pm Friday and 11:59 pm Sunday. These times were chosen by group consensus to account for the potential differences in hospital care during weekend hours (eg, decreased levels of provider staffing, access to ancillary services). To allow for a full 30-day readmission window, we defined an index admission as a hospitalization with no admission within the preceding 30 days. Individual children may contribute more than one index hospitalization to the dataset.

Main Outcomes

Our outcomes included LOS for weekend admission and 30-day readmissions for weekend discharge. LOS, measured in hours, was defined using the reported admission and discharge times. Readmissions were defined as a return to the same hospital within the subsequent 30 days following discharge.

Patient Demographics and Other Study Variables

Patient demographics included age, gender, race/ethnicity, payer, and median household income quartile based on the patient’s home ZIP code. Other study variables included presence of a complex chronic condition (CCC),³⁴ technology dependence,³⁴ number of chronic conditions of any complexity, admission through the emergency department, intensive care unit (ICU) admission, and case mix index. ICU admission and case mix index were chosen as markers for severity of illness. ICU admission was defined as any child who incurred ICU charges at any time following admission. Case mix index in PHIS is a relative weight assigned to each discharge based on the All-Patient Refined Diagnostic Group (APR-DRG; 3M) assignment and APR-DRG severity of illness, which ranges from 1 (minor) to 4 (extreme). The weights are derived by the Children’s Hospital Association from the HCUP KID 2012 database as the ratio of the average cost for discharges within a specific APR-DRG severity of illness combination to the average cost for all discharges in the database.

Statistical Analysis

Continuous variables were summarized with medians and interquartile ranges, while categorical variables were summarized with frequencies and percentages. Differences in admission and discharge characteristics between weekend and weekday were assessed using Wilcoxon rank sum tests for continuous variables and chi-square tests of association for categorical variables. We used generalized linear mixed modeling (GLMM) techniques to assess the impact of weekend admission on LOS and weekend discharge on readmission, adjusting for important patient demographic and clinical characteristics. Furthermore, we used GLMM point estimates to describe the variation across hospitals of the impact of weekday versus weekend care on LOS and readmissions. We assumed an underlying log-normal distribution for LOS and an underlying binomial distribution for 30-day readmission. All GLMMs included a random intercept for each hospital to account for patient clustering within a hospital. All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, North Carolina), and P values <.05 were considered statistically significant.

We identified 390,745 hospitalizations that met inclusion criteria (Supplementary Figure 1). The median LOS among our cohort was 41 hours (interquartile range [IQR] 24-71) and the median 30-day readmission rate was 8.2% (IQR 7.2-9.4).

Admission Demographics for Weekends and Weekdays

Among the included hospitalizations, 92,266 (23.6%) admissions occurred on a weekend (Supplementary Table 1). Overall, a higher percentage of children <5 years of age were admitted on a weekend compared with those admitted on a weekday (53.3% vs 49.1%, P < .001). We observed a small but statistically significant difference in the proportion of weekend versus weekday admissions according to gender, race/ethnicity, payer, and median household income quartile. Children with medical complexity and those with technology dependence were admitted less frequently on a weekend. A higher proportion of children were admitted through the emergency department on a weekend and a higher frequency of ICU utilization was observed for children admitted on a weekend compared with those admitted on a weekday.

Association Between Study Variables and Length of Stay

In comparing adjusted LOS for weekend versus weekday admissions across 43 hospitals, not only did LOS vary across hospitals (P < .001), but the association between LOS and weekend versus weekday care also varied across hospitals (P < .001) (Figure 1). Weekend admission was associated with a significantly longer LOS at eight (18.6%) hospitals and a significantly shorter LOS at four (9.3%) hospitals with nonstatistically significant differences at the remaining hospitals.

In adjusted analyses, we observed that infants ≤30 days of age, on average, had an adjusted LOS that was 24% longer than that of 15- to 17-year-olds, while children aged 1-14 years had an adjusted LOS that was 6%-18% shorter (Table 1). ICU utilization, admission through the emergency department, and number of chronic conditions had the greatest association with LOS. As the number of chronic conditions increased, the LOS increased. No association was found between weekend versus weekday admission and LOS (adjusted LOS [95% CI]: weekend 63.70 [61.01-66.52] hours versus weekday 63.40 [60.73-66.19] hours, P = .112).

Discharge Demographics for Weekends and Weekdays

Of the included hospitalizations, 127,421 (32.6%) discharges occurred on a weekend (Supplementary Table 2). Overall, a greater percentage of weekend discharges comprised children <5 years of age compared with the percentage of weekday discharges for children <5 years of age (51.5% vs 49.5%, P < .001). No statistically significant differences were found in gender, payer, or median household income quartile between those children discharged on a weekend versus those discharged on a weekday. We found small, statistically significant differences in the proportion of weekend versus weekday discharges according to race/ethnicity, with fewer non-Hispanic white children being discharged on the weekend versus weekday. Children with medical complexity, technology dependence, and patients with ICU utilization were less frequently discharged on a weekend compared with those discharged on a weekday.

Association Between Study Variables and Readmissions

In comparing the adjusted odds of readmissions for weekend versus weekday discharges across 43 PHIS hospitals, we observed significant variation (P < .001) in readmission rates from hospital to hospital (Figure 2). However, the direction of impact of weekend care on readmissions was similar (P = .314) across hospitals (ie, for 37 of 43 hospitals, the readmission rate was greater for weekend discharges compared with that for weekday discharges). For 17 (39.5%) of 43 hospitals, weekend discharge was associated with a significantly higher readmission rate, while the differences between weekday and weekend discharge were not statistically significant for the remaining hospitals.

In adjusted analyses, we observed that infants <1 year were more likely to be readmitted compared with 15- to 17-year-olds, while children 5-14 years of age were less likely to be readmitted (Table 2). Medical complexity and the number of chronic conditions had the greatest association with readmissions, with increased likelihood of readmission observed as the number of chronic conditions increased. Weekend discharge was associated with increased probability of readmission compared with weekday discharge (adjusted probability of readmission [95% CI]: weekend 0.13 [0.12-0.13] vs weekday 0.11 [0.11-0.12], P < .001).

In this multicenter retrospective study, we observed substantial variation across hospitals in the relationship between weekend admission and LOS and weekend discharge and readmission rates. Overall, we did not observe an association between weekend admission and LOS. However, significant associations were noted between weekend admission and LOS at some hospitals, although the magnitude and direction of the effect varied. We observed a modestly increased risk of readmission among those discharged on the weekend. At the hospital level, the association between weekend discharge and increased readmissions was statistically significant at 39.5% of hospitals. Not surprisingly, certain patient demographic and clinical characteristics, including medical complexity and number of chronic conditions, were also associated with LOS and readmission risk. Taken together, our findings demonstrate that among a large sample of children, the degree to which a weekend admission or discharge impacts LOS or readmission risk varies considerably according to specific patient characteristics and individual hospital.

While the reasons for the weekend effect are unclear, data supporting this difference have been observed across many diverse patient groups and health systems both nationally and internationally.^13-27,31 Weekend care is thought to differ from weekday care because of differences in physician and nurse staffing, availability of ancillary services, access to diagnostic testing and therapeutic interventions, ability to arrange outpatient follow-up, and individual patient clinical factors, including acuity of illness. Few studies have assessed the effect of weekend discharges on patient or system outcomes. Among children within a single health system, readmission risk was associated with weekend admission but not with weekend discharge.²² This observation suggests that if differential care exists, then it occurs during initial clinical management rather than during discharge planning. Consequently, understanding the interaction of weekend admission and LOS is important. In addition, the relative paucity of pediatric data examining a weekend discharge effect limits the ability to generalize these findings across other hospitals or health systems.

In contrast to prior work, we observed a modest increased risk for readmission among those discharged on the weekend in a large sample of children. Auger and Davis reported a lack of association between weekend discharge and readmissions at one tertiary care children’s hospital, citing reduced discharge volumes on the weekend, especially among children with medical complexity, as a possible driver for their observation.²² The inclusion of a much larger population across 43 hospitals in our study may explain our different findings compared with previous research. In addition, the inclusion/exclusion criteria differed between the two studies; we excluded index admissions for planned procedures in this study (which are more likely to occur during the week), which may have contributed to the differing conclusions. Although Auger and Davis suggest that differences in initial clinical management may be responsible for the weekend effect,²² our observations suggest that discharge planning practices may also contribute to readmission risk. For example, a family’s inability to access compounded medications at a local pharmacy or to access primary care following discharge could reasonably contribute to treatment failure and increased readmission risk. Attention to improving and standardizing discharge practices may alleviate differences in readmission risk among children discharged on a weekend.

Individual patient characteristics greatly influence LOS and readmission risk. Congruent with prior studies, medical complexity and technology dependence were among the factors in our study that had the strongest association with LOS and readmission risk.³² As with prior studies²², we observed that children with medical complexity and technology dependence were less frequently admitted and discharged on a weekend than on a weekday, which suggests that physicians may avoid complicated discharges on the weekend. Children with medical complexity present a unique challenge to physicians when assessing discharge readiness, given that these children frequently require careful coordination of durable medical equipment, obtainment of special medication preparations, and possibly the resumption or establishment of home health services. Notably, we cannot discern from our data what proportion of discharges may be delayed over the weekend secondary to challenges involved in coordinating care for children with medical complexity. Future investigations aimed at assessing physician decision making and discharge readiness in relation to discharge timing among children with medical complexity may establish this relationship more clearly.

We observed substantial variation in LOS and readmission risk across 43 tertiary care children’s hospitals. Since the 1970s, numerous studies have reported worse outcomes among patients admitted on the weekend. While the majority of studies support the weekend effect, several recent studies suggest that patients admitted on the weekend are at no greater risk of adverse outcomes than those admitted during the week.^35-37 Our work builds on the existing literature, demonstrating a complex and variable relationship between weekend admission/discharge, LOS, and readmission risk across hospitals. Notably, while many hospitals in our study experienced a significant weekend effect in LOS or readmission risk, only four hospitals experienced a statistically significant weekend effect for both LOS and readmission risk (three hospitals experienced increased risk for both, while one hospital experienced increased readmission risk but decreased LOS). Future investigations of the weekend effect should focus on exploring the differences in admission/discharge practices and staffing patterns of hospitals that did or did not experience a weekend effect.

This study has several limitations. We may have underestimated the total number of readmissions because we are unable to capture readmissions to other institutions by using the PHIS database. Our definition of a weekend admission or discharge did not account for three-day weekends or other holidays where staffing issues would be expected to be similar to that on weekends; consequently, our approach would be expected to bias the results toward null. Thus, a possible (but unlikely) result is that our approach masked a weekend effect that might have been more prominent had holidays been included. Although prior studies suggest that low physician/nurse staffing volumes and high patient workload are associated with worse patient outcomes,^38,39 we are unable to discern the role of differential staffing patterns, patient workload, or service availability in our observations using the PHIS database. Moreover, the PHIS database does not allow for any assessment of the preventability of a readmission or the impact of patient/family preference on the decision to admit or discharge, factors that could reasonably contribute to some of the observed variation. Finally, the PHIS database contains administrative data only, thus limiting our ability to fully adjust for patient severity of illness and sociodemographic factors that may have affected clinical decision making, including discharge decision making.

In a study of 43 children’s hospitals, children discharged on the weekend had a slightly increased readmission risk compared with children discharged on a weekday. Wide variation in the weekend effect on LOS and readmission risk was evident across hospitals. Individual patient characteristics had a greater impact on LOS and readmission risk than the weekend effect. Future investigations aimed at understanding which factors contribute most strongly to a weekend effect within individual hospitals (eg, differences in institutional admission/discharge practices) may help alleviate the weekend effect and improve healthcare quality.

Acknowledgments

This manuscript resulted from “Paper in a Day,” a Pediatric Research in Inpatient Settings (PRIS) Network-sponsored workshop presented at the Pediatric Hospital Medicine 2017 annual meeting. Workshop participants learned how to ask and answer a health services research question and efficiently prepare a manuscript for publication. The following are the members of the PRIS Network who contributed to this work: Jessica L. Bettenhausen, MD; Rebecca M. Cantu, MD, MPH; Jillian M Cotter, MD; Megan Deisz, MD; Teresa Frazer, MD; Pratichi Goenka, MD; Ashley Jenkins, MD; Kathryn E. Kyler, MD; Janet T. Lau, MD; Brian E. Lee, MD; Christiane Lenzen, MD; Trisha Marshall, MD; John M. Morrison MD, PhD; Lauren Nassetta, MD; Raymond Parlar-Chun, MD; Sonya Tang Girdwood MD, PhD; Tony R Tarchichi, MD; Irina G. Trifonova, MD; Jacqueline M. Walker, MD, MHPE; and Susan C. Walley, MD. See appendix for contact information for members of the PRIS Network.

Funding

The authors have no financial relationships relevant to this article to disclose.

Disclosures

The authors have no conflicts of interest to disclose.

The treatment landscape for hematologic malignancies is evolving faster than ever before, with a range of available therapeutic options that is now almost as diverse as this group of tumors. Immunotherapy in particular is front and center in the battle to control these diseases. Here, we describe the latest promising developments.

Exploiting T cells

The treatment landscape for hematologic malignancies is diverse, but one particular type of therapy has led the charge in improving patient outcomes. Several features of hematologic malignancies may make them particularly amenable to immunotherapy, including the fact that they are derived from corrupt immune cells and come into constant contact with other immune cells within the hematopoietic environment in which they reside. One of the oldest forms of immunotherapy, hematopoietic stem-cell transplantation (HSCT), remains the only curative option for many patients with hematologic malignancies.^1,2

Given the central role of T lymphocytes in antitumor immunity, research efforts have focused on harnessing their activity for cancer treatment. One example of this is adoptive cellular therapy (ACT), in which T cells are collected from a patient, grown outside the body to increase their number and then reinfused back to the patient. Allogeneic HSCT, in which the stem cells are collected from a matching donor and transplanted into the patient, is a crude example of ACT. The graft-versus-tumor effect is driven by donor cells present in the transplant, but is limited by the development of graft-versus-host disease (GvHD), whereby the donor T cells attack healthy host tissue.

Other types of ACT have been developed in an effort to capitalize on the anti-tumor effects of the patients own T cells and thus avoid the potentially fatal complication of GvHD. Tumor-infiltrating lymphocyte (TIL) therapy was developed to exploit the presence of tumor-specific T cells in the tumor microenvironment. To date, the efficacy of TIL therapy has been predominantly limited to melanoma.^1,3,4

Most recently, there has been a substantial buzz around the idea of genetically engineering T cells before they are reintroduced into the patient, to increase their anti-tumor efficacy and minimize damage to healthy tissue. This is achieved either by manipulating the antigen binding portion of the T-cell receptor to alter its specificity (TCR T cells) or by generating artificial fusion receptors known as chimeric antigen receptors (CAR T cells; Figure 1). The former is limited by the need for the TCR to be genetically matched to the patient’s immune type, whereas the latter is more flexible in this regard and has proved most successful.

Headlining advancements with CAR T cells

CAR T cells directed against the CD19 antigen, found on the surface of many hematologic malignancies, are the most clinically advanced in this rapidly evolving field (Table 1). Durable remissions have been demonstrated in patients with relapsed and refractory hematologic malignancies, including non-Hodgkin lymphoma (NHL), chronic lymphocytic leukemia (CLL), and acute lymphoblastic lymphoma (ALL), with efficacy in both the pre- and posttransplant setting and in patients with chemotherapy-refractory disease.^4,5

CTL019, a CD19-targeted CAR-T cell therapy, also known as tisagenlecleucel-T, has received breakthrough therapy designation from the US Food and Drug Administration (FDA) for the treatment of pediatric and adult patients with relapsed/refractory B-cell ALL and, more recently, for the treatment of adult patients with relapsed/refractory diffuse large B cell lymphoma.⁶

It is edging closer to FDA approval for the ALL indication, having been granted priority review in March on the basis of the phase 2 ELIANA trial, in which 50 patients received a single infusion of CTL019. Data presented at the American Society of Hematology annual meeting in December 2016 showed that 82% of patients achieved either complete remission (CR) or CR with incomplete blood count recovery (CRi) 3 months after treatment.⁷

Meanwhile, Kite Pharma has a rolling submission with the FDA for KTE-C19 (axicabtagene ciloleucel) for the treatment of patients with relapsed/refractory B-cell NHL who are ineligible for HSCT. In the ZUMA-1 trial, this therapy demonstrated an overall response rate (ORR) of 71%.⁸ Juno Therapeutics is developing several CAR T-cell therapies, including JCAR017, which elicited CR in 60% of patients with relapsed/refractory NHL.⁹

Target antigens other than CD19 are being explored, but these are mostly in the early stages of clinical development. While the focus has predominantly been on the treatment of lymphoma and leukemia, a presentation at the American Society for Clinical Oncology annual meeting in June reported the efficacy of a CAR-T cell therapy targeting the B-cell maturation antigen in patients with multiple myeloma. Results from 19 patients enrolled in an ongoing phase 1 trial in China showed that 14 had achieved stringent CR, 1 partial remission (PR) and 4 very good partial remission (VGPR).¹⁰

Antibodies evolve

Another type of immunotherapy that has revolutionized the treatment of hematologic malignancies is monoclonal antibodies (mAbs), targeting antigens on the surface of malignant B and T cells, in particular CD20. The approval of CD20-targeting mAb rituximab in 1997 was the first coup for the development of immunotherapy for the treatment of hematologic malignancies. It has become part of the standard treatment regimen for B-cell malignancies, including NHL and CLL, in combination with various types of chemotherapy.

Several other CD20-targeting antibodies have been developed (Table 2), some of which work in the same way as rituximab (eg, ofatumumab) and some that have a slightly different mechanism of action (eg, obinutuzumab).¹¹ Both types of antibody have proved highly effective; ofatumumab is FDA approved for the treatment of advanced CLL and is being evaluated in phase 3 trials in other hematologic malignancies, while obinutuzumab has received regulatory approval for the first-line treatment of CLL, replacing the standard rituximab-containing regimen.¹²

Innovative design

Newer drug designs, which have sought to take mAb therapy to the next level, have also shown significant efficacy in hematologic malignancies. Antibody-drug conjugates (ADCs) combine the cytotoxic efficacy of chemotherapeutic agents with the specificity of a mAb targeting a tumor-specific antigen. This essentially creates a targeted payload that improves upon the efficacy of mAb monotherapy but mitigates some of the side effects of chemotherapy related to their indiscriminate killing of both cancerous and healthy cells.

The development of ADCs has been somewhat of a rollercoaster ride, with the approval and subsequent withdrawal of the first-in-class drug gemtuzumab ozogamicin in 2010, but the field was reinvigorated with the successful development of brentuximab vedotin, which targets the CD30 antigen and is approved for the treatment of multiple different hematologic malignancies, including, most recently, for posttransplant consolidation therapy in patients with Hodgkin lymphoma at high risk of relapse or progression.¹⁸

Brentuximab vedotin may soon be joined by another FDA-approved ADC, this one targeting CD22. Inotuzumab ozogamicin was recently granted priority review for the treatment of relapsed/refractory ALL. The FDA is reviewing data from the phase 3 INO-VATE study in which inotuzumab ozogamicin reduced the risk of disease progression or death by 55% compared with standard therapy, and a decision is expected by August.¹⁹ Other ADC targets being investigated in clinical trials include CD138, CD19, and CD33 (Table 3). Meanwhile, a meta-analysis of randomized trials suggested that the withdrawal of gemtuzumab ozogamicin may have been premature, indicating that it does improve long-term overall survival (OS) and reduces the risk of relapse.²⁰

21

B-cell signaling a ripe target

Beyond immunotherapy, molecularly targeted drugs directed against key drivers of hematologic malignancies are also showing great promise. In particular, the B-cell receptor (BCR) signaling pathway, a central regulator of B-cell function, and its constituent kinases that are frequently dysregulated in B cell malignancies, has emerged as an exciting therapeutic avenue.

A variety of small molecule inhibitors targeting different nodes of the BCR pathway have been developed (Table 4), but the greatest success to date has been achieved with drugs targeting Bruton’s tyrosine kinase (BTK). Their clinical development culminated in the approval of ibrutinib for the treatment of patients with mantle cell lymphoma in 2013 and subsequently for patients with CLL, Waldenström macroglobulinemia, and most recently for patients with marginal zone lymphoma.

The treatment landscape for hematologic malignancies is evolving faster than ever before, with a range of available therapeutic options that is now almost as diverse as this group of tumors. Immunotherapy in particular is front and center in the battle to control these diseases. Here, we describe the latest promising developments.

Exploiting T cells

The treatment landscape for hematologic malignancies is diverse, but one particular type of therapy has led the charge in improving patient outcomes. Several features of hematologic malignancies may make them particularly amenable to immunotherapy, including the fact that they are derived from corrupt immune cells and come into constant contact with other immune cells within the hematopoietic environment in which they reside. One of the oldest forms of immunotherapy, hematopoietic stem-cell transplantation (HSCT), remains the only curative option for many patients with hematologic malignancies.^1,2

Given the central role of T lymphocytes in antitumor immunity, research efforts have focused on harnessing their activity for cancer treatment. One example of this is adoptive cellular therapy (ACT), in which T cells are collected from a patient, grown outside the body to increase their number and then reinfused back to the patient. Allogeneic HSCT, in which the stem cells are collected from a matching donor and transplanted into the patient, is a crude example of ACT. The graft-versus-tumor effect is driven by donor cells present in the transplant, but is limited by the development of graft-versus-host disease (GvHD), whereby the donor T cells attack healthy host tissue.

Other types of ACT have been developed in an effort to capitalize on the anti-tumor effects of the patients own T cells and thus avoid the potentially fatal complication of GvHD. Tumor-infiltrating lymphocyte (TIL) therapy was developed to exploit the presence of tumor-specific T cells in the tumor microenvironment. To date, the efficacy of TIL therapy has been predominantly limited to melanoma.^1,3,4

Most recently, there has been a substantial buzz around the idea of genetically engineering T cells before they are reintroduced into the patient, to increase their anti-tumor efficacy and minimize damage to healthy tissue. This is achieved either by manipulating the antigen binding portion of the T-cell receptor to alter its specificity (TCR T cells) or by generating artificial fusion receptors known as chimeric antigen receptors (CAR T cells; Figure 1). The former is limited by the need for the TCR to be genetically matched to the patient’s immune type, whereas the latter is more flexible in this regard and has proved most successful.

Headlining advancements with CAR T cells

CAR T cells directed against the CD19 antigen, found on the surface of many hematologic malignancies, are the most clinically advanced in this rapidly evolving field (Table 1). Durable remissions have been demonstrated in patients with relapsed and refractory hematologic malignancies, including non-Hodgkin lymphoma (NHL), chronic lymphocytic leukemia (CLL), and acute lymphoblastic lymphoma (ALL), with efficacy in both the pre- and posttransplant setting and in patients with chemotherapy-refractory disease.^4,5

CTL019, a CD19-targeted CAR-T cell therapy, also known as tisagenlecleucel-T, has received breakthrough therapy designation from the US Food and Drug Administration (FDA) for the treatment of pediatric and adult patients with relapsed/refractory B-cell ALL and, more recently, for the treatment of adult patients with relapsed/refractory diffuse large B cell lymphoma.⁶

It is edging closer to FDA approval for the ALL indication, having been granted priority review in March on the basis of the phase 2 ELIANA trial, in which 50 patients received a single infusion of CTL019. Data presented at the American Society of Hematology annual meeting in December 2016 showed that 82% of patients achieved either complete remission (CR) or CR with incomplete blood count recovery (CRi) 3 months after treatment.⁷

Meanwhile, Kite Pharma has a rolling submission with the FDA for KTE-C19 (axicabtagene ciloleucel) for the treatment of patients with relapsed/refractory B-cell NHL who are ineligible for HSCT. In the ZUMA-1 trial, this therapy demonstrated an overall response rate (ORR) of 71%.⁸ Juno Therapeutics is developing several CAR T-cell therapies, including JCAR017, which elicited CR in 60% of patients with relapsed/refractory NHL.⁹

Target antigens other than CD19 are being explored, but these are mostly in the early stages of clinical development. While the focus has predominantly been on the treatment of lymphoma and leukemia, a presentation at the American Society for Clinical Oncology annual meeting in June reported the efficacy of a CAR-T cell therapy targeting the B-cell maturation antigen in patients with multiple myeloma. Results from 19 patients enrolled in an ongoing phase 1 trial in China showed that 14 had achieved stringent CR, 1 partial remission (PR) and 4 very good partial remission (VGPR).¹⁰

Antibodies evolve

Another type of immunotherapy that has revolutionized the treatment of hematologic malignancies is monoclonal antibodies (mAbs), targeting antigens on the surface of malignant B and T cells, in particular CD20. The approval of CD20-targeting mAb rituximab in 1997 was the first coup for the development of immunotherapy for the treatment of hematologic malignancies. It has become part of the standard treatment regimen for B-cell malignancies, including NHL and CLL, in combination with various types of chemotherapy.

Several other CD20-targeting antibodies have been developed (Table 2), some of which work in the same way as rituximab (eg, ofatumumab) and some that have a slightly different mechanism of action (eg, obinutuzumab).¹¹ Both types of antibody have proved highly effective; ofatumumab is FDA approved for the treatment of advanced CLL and is being evaluated in phase 3 trials in other hematologic malignancies, while obinutuzumab has received regulatory approval for the first-line treatment of CLL, replacing the standard rituximab-containing regimen.¹²

Innovative design

Newer drug designs, which have sought to take mAb therapy to the next level, have also shown significant efficacy in hematologic malignancies. Antibody-drug conjugates (ADCs) combine the cytotoxic efficacy of chemotherapeutic agents with the specificity of a mAb targeting a tumor-specific antigen. This essentially creates a targeted payload that improves upon the efficacy of mAb monotherapy but mitigates some of the side effects of chemotherapy related to their indiscriminate killing of both cancerous and healthy cells.

The development of ADCs has been somewhat of a rollercoaster ride, with the approval and subsequent withdrawal of the first-in-class drug gemtuzumab ozogamicin in 2010, but the field was reinvigorated with the successful development of brentuximab vedotin, which targets the CD30 antigen and is approved for the treatment of multiple different hematologic malignancies, including, most recently, for posttransplant consolidation therapy in patients with Hodgkin lymphoma at high risk of relapse or progression.¹⁸

Brentuximab vedotin may soon be joined by another FDA-approved ADC, this one targeting CD22. Inotuzumab ozogamicin was recently granted priority review for the treatment of relapsed/refractory ALL. The FDA is reviewing data from the phase 3 INO-VATE study in which inotuzumab ozogamicin reduced the risk of disease progression or death by 55% compared with standard therapy, and a decision is expected by August.¹⁹ Other ADC targets being investigated in clinical trials include CD138, CD19, and CD33 (Table 3). Meanwhile, a meta-analysis of randomized trials suggested that the withdrawal of gemtuzumab ozogamicin may have been premature, indicating that it does improve long-term overall survival (OS) and reduces the risk of relapse.²⁰

21

B-cell signaling a ripe target

Beyond immunotherapy, molecularly targeted drugs directed against key drivers of hematologic malignancies are also showing great promise. In particular, the B-cell receptor (BCR) signaling pathway, a central regulator of B-cell function, and its constituent kinases that are frequently dysregulated in B cell malignancies, has emerged as an exciting therapeutic avenue.

A variety of small molecule inhibitors targeting different nodes of the BCR pathway have been developed (Table 4), but the greatest success to date has been achieved with drugs targeting Bruton’s tyrosine kinase (BTK). Their clinical development culminated in the approval of ibrutinib for the treatment of patients with mantle cell lymphoma in 2013 and subsequently for patients with CLL, Waldenström macroglobulinemia, and most recently for patients with marginal zone lymphoma.

The treatment landscape for hematologic malignancies is evolving faster than ever before, with a range of available therapeutic options that is now almost as diverse as this group of tumors. Immunotherapy in particular is front and center in the battle to control these diseases. Here, we describe the latest promising developments.

Exploiting T cells

The treatment landscape for hematologic malignancies is diverse, but one particular type of therapy has led the charge in improving patient outcomes. Several features of hematologic malignancies may make them particularly amenable to immunotherapy, including the fact that they are derived from corrupt immune cells and come into constant contact with other immune cells within the hematopoietic environment in which they reside. One of the oldest forms of immunotherapy, hematopoietic stem-cell transplantation (HSCT), remains the only curative option for many patients with hematologic malignancies.^1,2

Given the central role of T lymphocytes in antitumor immunity, research efforts have focused on harnessing their activity for cancer treatment. One example of this is adoptive cellular therapy (ACT), in which T cells are collected from a patient, grown outside the body to increase their number and then reinfused back to the patient. Allogeneic HSCT, in which the stem cells are collected from a matching donor and transplanted into the patient, is a crude example of ACT. The graft-versus-tumor effect is driven by donor cells present in the transplant, but is limited by the development of graft-versus-host disease (GvHD), whereby the donor T cells attack healthy host tissue.

Other types of ACT have been developed in an effort to capitalize on the anti-tumor effects of the patients own T cells and thus avoid the potentially fatal complication of GvHD. Tumor-infiltrating lymphocyte (TIL) therapy was developed to exploit the presence of tumor-specific T cells in the tumor microenvironment. To date, the efficacy of TIL therapy has been predominantly limited to melanoma.^1,3,4

Most recently, there has been a substantial buzz around the idea of genetically engineering T cells before they are reintroduced into the patient, to increase their anti-tumor efficacy and minimize damage to healthy tissue. This is achieved either by manipulating the antigen binding portion of the T-cell receptor to alter its specificity (TCR T cells) or by generating artificial fusion receptors known as chimeric antigen receptors (CAR T cells; Figure 1). The former is limited by the need for the TCR to be genetically matched to the patient’s immune type, whereas the latter is more flexible in this regard and has proved most successful.

Headlining advancements with CAR T cells

CAR T cells directed against the CD19 antigen, found on the surface of many hematologic malignancies, are the most clinically advanced in this rapidly evolving field (Table 1). Durable remissions have been demonstrated in patients with relapsed and refractory hematologic malignancies, including non-Hodgkin lymphoma (NHL), chronic lymphocytic leukemia (CLL), and acute lymphoblastic lymphoma (ALL), with efficacy in both the pre- and posttransplant setting and in patients with chemotherapy-refractory disease.^4,5

CTL019, a CD19-targeted CAR-T cell therapy, also known as tisagenlecleucel-T, has received breakthrough therapy designation from the US Food and Drug Administration (FDA) for the treatment of pediatric and adult patients with relapsed/refractory B-cell ALL and, more recently, for the treatment of adult patients with relapsed/refractory diffuse large B cell lymphoma.⁶

It is edging closer to FDA approval for the ALL indication, having been granted priority review in March on the basis of the phase 2 ELIANA trial, in which 50 patients received a single infusion of CTL019. Data presented at the American Society of Hematology annual meeting in December 2016 showed that 82% of patients achieved either complete remission (CR) or CR with incomplete blood count recovery (CRi) 3 months after treatment.⁷

Meanwhile, Kite Pharma has a rolling submission with the FDA for KTE-C19 (axicabtagene ciloleucel) for the treatment of patients with relapsed/refractory B-cell NHL who are ineligible for HSCT. In the ZUMA-1 trial, this therapy demonstrated an overall response rate (ORR) of 71%.⁸ Juno Therapeutics is developing several CAR T-cell therapies, including JCAR017, which elicited CR in 60% of patients with relapsed/refractory NHL.⁹

Target antigens other than CD19 are being explored, but these are mostly in the early stages of clinical development. While the focus has predominantly been on the treatment of lymphoma and leukemia, a presentation at the American Society for Clinical Oncology annual meeting in June reported the efficacy of a CAR-T cell therapy targeting the B-cell maturation antigen in patients with multiple myeloma. Results from 19 patients enrolled in an ongoing phase 1 trial in China showed that 14 had achieved stringent CR, 1 partial remission (PR) and 4 very good partial remission (VGPR).¹⁰

Antibodies evolve

Another type of immunotherapy that has revolutionized the treatment of hematologic malignancies is monoclonal antibodies (mAbs), targeting antigens on the surface of malignant B and T cells, in particular CD20. The approval of CD20-targeting mAb rituximab in 1997 was the first coup for the development of immunotherapy for the treatment of hematologic malignancies. It has become part of the standard treatment regimen for B-cell malignancies, including NHL and CLL, in combination with various types of chemotherapy.

Several other CD20-targeting antibodies have been developed (Table 2), some of which work in the same way as rituximab (eg, ofatumumab) and some that have a slightly different mechanism of action (eg, obinutuzumab).¹¹ Both types of antibody have proved highly effective; ofatumumab is FDA approved for the treatment of advanced CLL and is being evaluated in phase 3 trials in other hematologic malignancies, while obinutuzumab has received regulatory approval for the first-line treatment of CLL, replacing the standard rituximab-containing regimen.¹²

Innovative design

Newer drug designs, which have sought to take mAb therapy to the next level, have also shown significant efficacy in hematologic malignancies. Antibody-drug conjugates (ADCs) combine the cytotoxic efficacy of chemotherapeutic agents with the specificity of a mAb targeting a tumor-specific antigen. This essentially creates a targeted payload that improves upon the efficacy of mAb monotherapy but mitigates some of the side effects of chemotherapy related to their indiscriminate killing of both cancerous and healthy cells.

The development of ADCs has been somewhat of a rollercoaster ride, with the approval and subsequent withdrawal of the first-in-class drug gemtuzumab ozogamicin in 2010, but the field was reinvigorated with the successful development of brentuximab vedotin, which targets the CD30 antigen and is approved for the treatment of multiple different hematologic malignancies, including, most recently, for posttransplant consolidation therapy in patients with Hodgkin lymphoma at high risk of relapse or progression.¹⁸

Brentuximab vedotin may soon be joined by another FDA-approved ADC, this one targeting CD22. Inotuzumab ozogamicin was recently granted priority review for the treatment of relapsed/refractory ALL. The FDA is reviewing data from the phase 3 INO-VATE study in which inotuzumab ozogamicin reduced the risk of disease progression or death by 55% compared with standard therapy, and a decision is expected by August.¹⁹ Other ADC targets being investigated in clinical trials include CD138, CD19, and CD33 (Table 3). Meanwhile, a meta-analysis of randomized trials suggested that the withdrawal of gemtuzumab ozogamicin may have been premature, indicating that it does improve long-term overall survival (OS) and reduces the risk of relapse.²⁰

21

B-cell signaling a ripe target

Beyond immunotherapy, molecularly targeted drugs directed against key drivers of hematologic malignancies are also showing great promise. In particular, the B-cell receptor (BCR) signaling pathway, a central regulator of B-cell function, and its constituent kinases that are frequently dysregulated in B cell malignancies, has emerged as an exciting therapeutic avenue.

A variety of small molecule inhibitors targeting different nodes of the BCR pathway have been developed (Table 4), but the greatest success to date has been achieved with drugs targeting Bruton’s tyrosine kinase (BTK). Their clinical development culminated in the approval of ibrutinib for the treatment of patients with mantle cell lymphoma in 2013 and subsequently for patients with CLL, Waldenström macroglobulinemia, and most recently for patients with marginal zone lymphoma.

After discharge from the hospital, patients have a significantly elevated risk for adverse events, including emergency department use, hospital readmission, and death. More than 1 in 3 patients discharged from the hospital require acute care in the month after hospital discharge, and more than 1 in 6 require readmission, with readmission diagnoses frequently differing from those of the preceding hospitalization.^1-4 This heightened susceptibility to adverse events persists beyond 30 days but levels off by 7 weeks after discharge, suggesting that the period of increased risk is transient and dynamic.⁵

The term posthospital syndrome (PHS) describes this period of vulnerability to major adverse events following hospitalization.⁶ In addition to increased risk for readmission and mortality, patients in this period often show evidence of generalized dysfunction with new cognitive impairment, mobility disability, or functional decline.^7-12 To date, the etiology of this vulnerability is neither well understood nor effectively addressed by transitional care interventions.¹³

One hypothesis to explain PHS is that stressors associated with the experience of hospitalization contribute to transient multisystem dysfunction that induces susceptibility to a broad range of medical maladies. These stressors include frequent sleep disruption, noxious sounds, painful stimuli, mobility restrictions, and poor nutrition.¹² The stress hypothesis as a cause of PHS is therefore based, in large part, on evidence about allostasis and the deleterious effects of allostatic overload.

Allostasis defines a system functioning within normal stress-response parameters to promote adaptation and survival.¹⁴ In allostasis, the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic and parasympathetic branches of the autonomic nervous system (ANS) exist in homeostatic balance and respond to environmental stimuli within a range of healthy physiologic parameters. The hallmark of a system in allostasis is the ability to rapidly activate, then successfully deactivate, a stress response once the stressor (ie, threat) has resolved.^14,15 To promote survival and potentiate “fight or flight” mechanisms, an appropriate stress response necessarily impacts multiple physiologic systems that result in hemodynamic augmentation and gluconeogenesis to support the anticipated action of large muscle groups, heightened vigilance and memory capabilities to improve rapid decision-making, and enhancement of innate and adaptive immune capabilities to prepare for wound repair and infection defense.^14-16 The stress response is subsequently terminated by negative feedback mechanisms of glucocorticoids as well as a shift of the ANS from sympathetic to parasympathetic tone.^17,18

Extended or repetitive stress exposure, however, leads to dysregulation of allostatic mechanisms responsible for stress adaptation and hinders an efficient and effective stress response. After extended stress exposure, baseline (ie, resting) HPA activity resets, causing a disruption of normal diurnal cortisol rhythm and an increase in total cortisol concentration. Moreover, in response to stress, HPA and ANS system excitation becomes impaired, and negative feedback properties are undermined.^14,15 This maladaptive state, known as allostatic overload, disrupts the finely tuned mechanisms that are the foundation of mind-body balance and yields pathophysiologic consequences to multiple organ systems. Downstream ramifications of allostatic overload include cognitive deterioration, cardiovascular and immune system dysfunction, and functional decline.^14,15,19

Although a stress response is an expected and necessary aspect of acute illness that promotes survival, the central thesis of this work is that additional environmental and social stressors inherent in hospitalization may unnecessarily compound stress and increase the risk of HPA axis dysfunction, allostatic overload, and subsequent multisystem dysfunction, predisposing individuals to adverse outcomes after hospital discharge. Based on data from both human subjects and animal models, we present a possible pathophysiologic mechanism for the postdischarge vulnerability of PHS, encourage critical contemplation of traditional hospitalization, and suggest interventions that might improve outcomes.

Posthospital syndrome (PHS) describes a transient period of vulnerability after hospitalization during which patients are at elevated risk for adverse events from a broad range of conditions. In support of this characterization, epidemiologic data have demonstrated high rates of adverse outcomes following hospitalization. For example, data have shown that more than 1 in 6 older adults is readmitted to the hospital within 30 days of discharge.²⁰ Death is also common in this first month, during which rates of postdischarge mortality may exceed initial inpatient mortality.^21,22 Elevated vulnerability after hospitalization is not restricted to older adults, as readmission risk among younger patients 18 to 64 years of age may be even higher for selected conditions, such as heart failure.^3,23

Vulnerability after hospitalization is broad. In patients over age 65 initially admitted for heart failure or acute myocardial infarction, only 35% and 10% of readmissions are for recurrent heart failure or reinfarction, respectively.¹ Nearly half of readmissions are for noncardiovascular causes.¹ Similarly, following hospitalization for pneumonia, more than 60 percent of readmissions are for nonpulmonary etiologies. Moreover, the risk for all these causes of readmission is much higher than baseline risk, indicating an extended period of lack of resilience to many types of illness.²⁴ These patterns of broad susceptibility also extend to younger adults hospitalized with common medical conditions.³

Accumulating evidence suggests that hospitalized patients face functional decline, debility, and risk for adverse events despite resolution of the presenting illness, implying perhaps that the hospital environment itself is hazardous to patients’ health. In 1993, Creditor hypothesized that the “hazards of hospitalization,” including enforced bed-rest, sensory deprivation, social isolation, and malnutrition lead to a “cascade of dependency” in which a collection of small insults to multiple organ systems precipitates loss of function and debility despite cure or resolution of presenting illness.¹² Covinsky (2011) later defined hospitalization-associated disability as an iatrogenic hospital-related “disorder” characterized by new impairments in abilities to perform basic activities of daily living such as bathing, feeding, toileting, dressing, transferring, and walking at the time of hospital discharge.¹¹ Others have described a postintensive-care syndrome (PICS),²⁵ characterized by cognitive, psychiatric, and physical impairments acquired during hospitalization for critical illness that persist postdischarge and increase the long-term risk for adverse outcomes, including elevated mortality rates,^26,27 readmission rates,²⁸ and physical disabilities.²⁹ Similar to the “hazards of hospitalization,” PICS is thought to be related to common experiences of ICU stays, including mobility restriction, sensory deprivation, sleep disruption, sedation, malnutrition, and polypharmacy.^30-33

Taken together, these data suggest that adverse health consequences attributable to hospitalization extend across the spectrum of age, presenting disease severity, and hospital treatment location. As detailed below, the PHS hypothesis is rooted in a mechanistic understanding of the role of exogenous stressors in producing physiologic dysregulation and subsequent adverse health effects across multiple organ systems.

Nature of Stress in the Hospital

Compounding the stress of acute illness, hospitalized patients are routinely and repetitively exposed to a wide variety of environmental stressors that may have downstream adverse consequences (Table 1). In the absence of overt clinical manifestations of harm, the possible subclinical physiologic dysfunction generated by the following stress exposures may increase patients’ susceptibility to the manifestations of PHS.

Sleep Disruption

Sleep disruptions trigger potent stress responses,^34,35 yet they are common occurrences during hospitalization. In surveys, about half of patients report poor sleep quality during hospitalization that persists for many months after discharge.³⁶ In a simulated hospital setting, test subjects exposed to typical hospital sounds (paging system, machine alarms, etc.) experienced significant sleep-wake cycle abnormalities.³⁷ Although no work has yet focused specifically on the physiologic consequences of sleep disruption and stress in hospitalized patients, in healthy humans, mild sleep disruption has clear effects on allostasis by disrupting HPA activity, raising cortisol levels, diminishing parasympathetic tone, and impairing cognitive performance.^{18,34,35,38,39}

Malnourishment

Malnourishment in hospitalized patients is common, with one-fifth of hospitalized patients receiving nothing per mouth or clear liquid diets for more than 3 continuous days,⁴⁰ and one-fifth of hospitalized elderly patients receiving less than half of their calculated nutrition requirements.⁴¹ Although the relationship between food restriction, cortisol levels, and postdischarge outcomes has not been fully explored, in healthy humans, meal anticipation, meal withdrawal (withholding an expected meal), and self-reported dietary restraint are known to generate stress responses.^42,43 Furthermore, malnourishment during hospitalization is associated with increased 90-day and 1-year mortality after discharge,⁴⁴ adding malnourishment to the list of plausible components of hospital-related stress.

Mobility Restriction

Physical activity counterbalances stress responses and minimizes downstream consequences of allostatic load,¹⁵ yet mobility limitations via physical and chemical restraints are common in hospitalized patients, particularly among the elderly.^45-47 Many patients are tethered to devices that make ambulation hazardous, such as urinary catheters and infusion pumps. Even without physical or chemical restraints or a limited mobility order, patients may be hesitant to leave the room so as not to miss transport to a diagnostic study or an unscheduled physician’s visit. Indeed, mobility limitations of hospitalized patients increase the risk for adverse events after discharge, while interventions designed to encourage mobility are associated with improved postdischarge outcomes.^47,48

Other Stressors

Other hospital-related aversive stimuli are less commonly quantified, but clearly exist. According to surveys of hospitalized patients, sources of emotional stress include social isolation; loss of autonomy and privacy; fear of serious illness; lack of control over activities of daily living; lack of clear communication between treatment team and patients; and death of a patient roommate.^49,50 Furthermore, consider the physical discomfort and emotional distress of patients with urinary incontinence awaiting assistance for a diaper or bedding change or the pain of repetitive blood draws or other invasive testing. Although individualized, the subjective discomfort and emotional distress associated with these experiences undoubtedly contribute to the stress of hospitalization.

Animal Models of Stress

Laboratory techniques reminiscent of the numerous environmental stressors associated with hospitalization have been used to reliably trigger allostatic overload in healthy young animals.⁵¹ These techniques include sequential exposure to aversive stimuli, including food and water deprivation, continuous overnight illumination, paired housing with known and unknown cagemates, mobility restriction, soiled cage conditions, and continuous noise. All of these techniques have been shown to cause HPA axis and ANS dysfunction, allostatic overload, and subsequent stress-mediated consequences to multiple organ systems.^19,52-54 Given the remarkable similarity of these protocols to common experiences during hospitalization, animal models of stress may be useful in understanding the spectrum of maladaptive consequences experienced by patients within the hospital (Figure 1).

These animal models of stress have resulted in a number of instructive findings. For example, in rodents, extended stress exposure induces structural and functional remodeling of neuronal networks that precipitate learning and memory, working memory, and attention impairments.^55-57 These exposures also result in cardiovascular abnormalities, including dyslipidemia, progressive atherosclerosis,^58,59 and enhanced inflammatory cytokine expression,⁶⁰ all of which increase both atherosclerotic burden and susceptibility to plaque rupture, leading to elevated risk for major cardiovascular adverse events. Moreover, these extended stress exposures in animals increase susceptibility to both bacterial and viral infections and increase their severity.^16,61 This outcome appears to be driven by a stress-induced elevation of glucocorticoid levels, decreased leukocyte proliferation, altered leukocyte trafficking, and a transition to a proinflammatory cytokine environment.^{16, 61} Allostatic overload has also been shown to contribute to metabolic dysregulation involving insulin resistance, persistence of hyperglycemia, dyslipidemia, catabolism of lean muscle, and visceral adipose tissue deposition.^62-64 In addition to cardiovascular, immune, and metabolic consequences of allostatic overload, the spectrum of physiologic dysfunction in animal models is broad and includes mood disorder symptoms,⁶⁵ intestinal barrier abnormalities,⁶⁶ airway reactivity exacerbation,⁶⁷ and enhanced tumor growth.⁶⁸

Although the majority of this research highlights the multisystem effects of variable stress exposure in healthy animals, preliminary evidence suggests that aged or diseased animals subjected to additional stressors display a heightened inflammatory cytokine response that contributes to exaggerated sickness behavior and greater and prolonged cognitive deficits.⁶⁹ Future studies exploring the consequences of extended stress exposure in animals with existing disease or debility may therefore more closely simulate the experience of hospitalized patients and perhaps further our understanding of PHS.

Hospitalized Patients

While no intervention studies have examined the effects of potential hospital stressors on the development of allostatic overload, there is evidence from small studies that dysregulated stress responses during hospitalization are associated with adverse events. For example, high serum cortisol, catecholamine, and proinflammatory cytokine levels during hospitalization have individually been associated with the development of cognitive dysfunction,^70-72 increased risk of cardiovascular events such as myocardial infarction and stroke in the year following discharge,^73-76 and the development of wound infections after discharge.⁷⁷ Moreover, elevated plasma glucose during admission for myocardial infarction in patients with or without diabetes has been associated with greater in-hospital and 1-year mortality,⁷⁸ with a similar relationship seen between elevated plasma glucose and survival after admission for stroke⁷⁹ and pneumonia.⁸⁰ Furthermore, in addition to atherothrombosis, stress may contribute to the risk for venous thromboembolism,⁸¹ resulting in readmissions for deep vein thrombosis or pulmonary embolism posthospitalization. Although potentially surrogate markers of illness acuity, a handful of studies have shown that these stress biomarkers are actually only weakly correlated with,⁸² or independent of,^72,76 disease severity. As discussed in detail below, future studies utilizing a summative measure of multisystem physiologic dysfunction as opposed to individual biomarkers may more accurately reflect the cumulative stress effects of hospitalization and subsequent risk for adverse events.

Additional Considerations

Elderly patients, in particular, may have heightened susceptibility to the consequences of allostatic overload due to common geriatric issues such as multimorbidity and frailty. Patients with chronic diseases display both baseline HPA axis abnormalities as well as dysregulated stress responses and may therefore be more vulnerable to hospitalization-related stress. For example, when subjected to psychosocial stress, patients with chronic conditions such as diabetes, heart failure, or atherosclerosis demonstrate elevated cortisol levels, increased circulating markers of inflammation, as well as prolonged hemodynamic recovery after stress resolution compared with normal controls.^83-85 Additionally, frailty may affect an individual’s susceptibility to exogenous stress. Indeed, frailty identified on hospital admission increases the risk for adverse outcomes during hospitalization and postdischarge.⁸⁶ Although the specific etiology of this relationship is unclear, persons with frailty are known to have elevated levels of cortisol and other inflammatory markers,^87,88 which may contribute to adverse outcomes in the face of additional stressors.

A large body of evidence stretching from bench to bedside suggests that environmental stressors associated with hospitalization are toxic. Understanding PHS within the context of hospital-induced allostatic overload presents a unifying theory for the interrelated multisystem dysfunction and increased susceptibility to adverse events that patients experience after discharge (Figure 2). Furthermore, it defines a potential pathophysiological mechanism for the cognitive impairment, elevated cardiovascular risk, immune system dysfunction, metabolic derangements, and functional decline associated with PHS. Additionally, this theory highlights environmental interventions to limit PHS development and suggests mechanisms to promote stress resilience. Although it is difficult to disentangle the consequences of the endogenous stress triggered by an acute illness from the exogenous stressors related to hospitalization, it is likely that the 2 simultaneous exposures compound risk for stress system dysregulation and allostatic overload. Moreover, hospitalized patients with preexisting HPA axis dysfunction at baseline from chronic disease or advancing age may be even more susceptible to these adverse outcomes. If this hypothesis is true, a reduction in PHS would require mitigation of the modifiable environmental stressors encountered by patients during hospitalization. Directed efforts to diminish ambient noise, limit nighttime disruptions, thoughtfully plan procedures, consider ongoing nutritional status, and promote opportunities for patients to exert some control over their environment may diminish the burden of extrinsic stressors encountered by all patients in the hospital and improve outcomes after discharge.

Hospitals are increasingly recognizing the importance of improving patients’ experience of hospitalization by reducing exposure to potential toxicities. For example, many hospitals are now attempting to reduce sleep disturbances and sleep latency through reduced nighttime noise and light levels, fewer nighttime interruptions for vital signs checks and medication administration, and commonsensical interventions like massages, herbal teas, and warm milk prior to bedtime.⁸⁹ Likewise, intensive care units are targeting environmental and physical stressors with a multifaceted approach to decrease sedative use, promote healthy sleep cycles, and encourage exercise and ambulation even in those patients who are mechanically ventilated.³⁰ Another promising development has been the increase of Hospital at Home programs. In these programs, patients who meet the criteria for inpatient admission are instead comprehensively managed at home for their acute illness through a multidisciplinary effort between physicians, nurses, social workers, physical therapists, and others. Patients hospitalized at home report higher levels of satisfaction and have modest functional gains, improved health-related quality of life, and decreased risk of mortality at 6 months compared with hospitalized patients.^90,91 With some admitting diagnoses (eg, heart failure), hospitalization at home may be associated with decreased readmission risk.⁹² Although not yet investigated on a physiologic level, perhaps the benefits of hospital at home are partially due to the dramatic difference in exposure to environmental stressors.

A tool that quantifies hospital-associated stress may help health providers appreciate the experience of patients and better target interventions to aspects of their structure and process that contribute to allostatic overload. Importantly, allostatic overload cannot be identified by one biomarker of stress but instead requires evidence of dysregulation across inflammatory, neuroendocrine, hormonal, and cardiometabolic systems. Future studies to address the burden of stress faced by hospitalized patients should consider a summative measure of multisystem dysregulation as opposed to isolated assessments of individual biomarkers. Allostatic load has previously been operationalized as the summation of a variety of hemodynamic, hormonal, and metabolic factors, including blood pressure, lipid profile, glycosylated hemoglobin, cortisol, catecholamine levels, and inflammatory markers.⁹³ To develop a hospital-associated allostatic load index, models should ideally be adjusted for acute illness severity, patient-reported stress, and capacity for stress resilience. This tool could then be used to quantify hospitalization-related allostatic load and identify those at greatest risk for adverse events after discharge, as well as measure the effectiveness of strategic environmental interventions (Table 2). A natural first experiment may be a comparison of the allostatic load of hospitalized patients versus those hospitalized at home.

After discharge from the hospital, patients have a significantly elevated risk for adverse events, including emergency department use, hospital readmission, and death. More than 1 in 3 patients discharged from the hospital require acute care in the month after hospital discharge, and more than 1 in 6 require readmission, with readmission diagnoses frequently differing from those of the preceding hospitalization.^1-4 This heightened susceptibility to adverse events persists beyond 30 days but levels off by 7 weeks after discharge, suggesting that the period of increased risk is transient and dynamic.⁵

The term posthospital syndrome (PHS) describes this period of vulnerability to major adverse events following hospitalization.⁶ In addition to increased risk for readmission and mortality, patients in this period often show evidence of generalized dysfunction with new cognitive impairment, mobility disability, or functional decline.^7-12 To date, the etiology of this vulnerability is neither well understood nor effectively addressed by transitional care interventions.¹³

One hypothesis to explain PHS is that stressors associated with the experience of hospitalization contribute to transient multisystem dysfunction that induces susceptibility to a broad range of medical maladies. These stressors include frequent sleep disruption, noxious sounds, painful stimuli, mobility restrictions, and poor nutrition.¹² The stress hypothesis as a cause of PHS is therefore based, in large part, on evidence about allostasis and the deleterious effects of allostatic overload.

Allostasis defines a system functioning within normal stress-response parameters to promote adaptation and survival.¹⁴ In allostasis, the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic and parasympathetic branches of the autonomic nervous system (ANS) exist in homeostatic balance and respond to environmental stimuli within a range of healthy physiologic parameters. The hallmark of a system in allostasis is the ability to rapidly activate, then successfully deactivate, a stress response once the stressor (ie, threat) has resolved.^14,15 To promote survival and potentiate “fight or flight” mechanisms, an appropriate stress response necessarily impacts multiple physiologic systems that result in hemodynamic augmentation and gluconeogenesis to support the anticipated action of large muscle groups, heightened vigilance and memory capabilities to improve rapid decision-making, and enhancement of innate and adaptive immune capabilities to prepare for wound repair and infection defense.^14-16 The stress response is subsequently terminated by negative feedback mechanisms of glucocorticoids as well as a shift of the ANS from sympathetic to parasympathetic tone.^17,18

Extended or repetitive stress exposure, however, leads to dysregulation of allostatic mechanisms responsible for stress adaptation and hinders an efficient and effective stress response. After extended stress exposure, baseline (ie, resting) HPA activity resets, causing a disruption of normal diurnal cortisol rhythm and an increase in total cortisol concentration. Moreover, in response to stress, HPA and ANS system excitation becomes impaired, and negative feedback properties are undermined.^14,15 This maladaptive state, known as allostatic overload, disrupts the finely tuned mechanisms that are the foundation of mind-body balance and yields pathophysiologic consequences to multiple organ systems. Downstream ramifications of allostatic overload include cognitive deterioration, cardiovascular and immune system dysfunction, and functional decline.^14,15,19

Although a stress response is an expected and necessary aspect of acute illness that promotes survival, the central thesis of this work is that additional environmental and social stressors inherent in hospitalization may unnecessarily compound stress and increase the risk of HPA axis dysfunction, allostatic overload, and subsequent multisystem dysfunction, predisposing individuals to adverse outcomes after hospital discharge. Based on data from both human subjects and animal models, we present a possible pathophysiologic mechanism for the postdischarge vulnerability of PHS, encourage critical contemplation of traditional hospitalization, and suggest interventions that might improve outcomes.

Posthospital syndrome (PHS) describes a transient period of vulnerability after hospitalization during which patients are at elevated risk for adverse events from a broad range of conditions. In support of this characterization, epidemiologic data have demonstrated high rates of adverse outcomes following hospitalization. For example, data have shown that more than 1 in 6 older adults is readmitted to the hospital within 30 days of discharge.²⁰ Death is also common in this first month, during which rates of postdischarge mortality may exceed initial inpatient mortality.^21,22 Elevated vulnerability after hospitalization is not restricted to older adults, as readmission risk among younger patients 18 to 64 years of age may be even higher for selected conditions, such as heart failure.^3,23

Vulnerability after hospitalization is broad. In patients over age 65 initially admitted for heart failure or acute myocardial infarction, only 35% and 10% of readmissions are for recurrent heart failure or reinfarction, respectively.¹ Nearly half of readmissions are for noncardiovascular causes.¹ Similarly, following hospitalization for pneumonia, more than 60 percent of readmissions are for nonpulmonary etiologies. Moreover, the risk for all these causes of readmission is much higher than baseline risk, indicating an extended period of lack of resilience to many types of illness.²⁴ These patterns of broad susceptibility also extend to younger adults hospitalized with common medical conditions.³

Accumulating evidence suggests that hospitalized patients face functional decline, debility, and risk for adverse events despite resolution of the presenting illness, implying perhaps that the hospital environment itself is hazardous to patients’ health. In 1993, Creditor hypothesized that the “hazards of hospitalization,” including enforced bed-rest, sensory deprivation, social isolation, and malnutrition lead to a “cascade of dependency” in which a collection of small insults to multiple organ systems precipitates loss of function and debility despite cure or resolution of presenting illness.¹² Covinsky (2011) later defined hospitalization-associated disability as an iatrogenic hospital-related “disorder” characterized by new impairments in abilities to perform basic activities of daily living such as bathing, feeding, toileting, dressing, transferring, and walking at the time of hospital discharge.¹¹ Others have described a postintensive-care syndrome (PICS),²⁵ characterized by cognitive, psychiatric, and physical impairments acquired during hospitalization for critical illness that persist postdischarge and increase the long-term risk for adverse outcomes, including elevated mortality rates,^26,27 readmission rates,²⁸ and physical disabilities.²⁹ Similar to the “hazards of hospitalization,” PICS is thought to be related to common experiences of ICU stays, including mobility restriction, sensory deprivation, sleep disruption, sedation, malnutrition, and polypharmacy.^30-33

Taken together, these data suggest that adverse health consequences attributable to hospitalization extend across the spectrum of age, presenting disease severity, and hospital treatment location. As detailed below, the PHS hypothesis is rooted in a mechanistic understanding of the role of exogenous stressors in producing physiologic dysregulation and subsequent adverse health effects across multiple organ systems.

Nature of Stress in the Hospital

Compounding the stress of acute illness, hospitalized patients are routinely and repetitively exposed to a wide variety of environmental stressors that may have downstream adverse consequences (Table 1). In the absence of overt clinical manifestations of harm, the possible subclinical physiologic dysfunction generated by the following stress exposures may increase patients’ susceptibility to the manifestations of PHS.

Sleep Disruption

Sleep disruptions trigger potent stress responses,^34,35 yet they are common occurrences during hospitalization. In surveys, about half of patients report poor sleep quality during hospitalization that persists for many months after discharge.³⁶ In a simulated hospital setting, test subjects exposed to typical hospital sounds (paging system, machine alarms, etc.) experienced significant sleep-wake cycle abnormalities.³⁷ Although no work has yet focused specifically on the physiologic consequences of sleep disruption and stress in hospitalized patients, in healthy humans, mild sleep disruption has clear effects on allostasis by disrupting HPA activity, raising cortisol levels, diminishing parasympathetic tone, and impairing cognitive performance.^{18,34,35,38,39}

Malnourishment

Malnourishment in hospitalized patients is common, with one-fifth of hospitalized patients receiving nothing per mouth or clear liquid diets for more than 3 continuous days,⁴⁰ and one-fifth of hospitalized elderly patients receiving less than half of their calculated nutrition requirements.⁴¹ Although the relationship between food restriction, cortisol levels, and postdischarge outcomes has not been fully explored, in healthy humans, meal anticipation, meal withdrawal (withholding an expected meal), and self-reported dietary restraint are known to generate stress responses.^42,43 Furthermore, malnourishment during hospitalization is associated with increased 90-day and 1-year mortality after discharge,⁴⁴ adding malnourishment to the list of plausible components of hospital-related stress.

Mobility Restriction

Physical activity counterbalances stress responses and minimizes downstream consequences of allostatic load,¹⁵ yet mobility limitations via physical and chemical restraints are common in hospitalized patients, particularly among the elderly.^45-47 Many patients are tethered to devices that make ambulation hazardous, such as urinary catheters and infusion pumps. Even without physical or chemical restraints or a limited mobility order, patients may be hesitant to leave the room so as not to miss transport to a diagnostic study or an unscheduled physician’s visit. Indeed, mobility limitations of hospitalized patients increase the risk for adverse events after discharge, while interventions designed to encourage mobility are associated with improved postdischarge outcomes.^47,48

Other Stressors

Other hospital-related aversive stimuli are less commonly quantified, but clearly exist. According to surveys of hospitalized patients, sources of emotional stress include social isolation; loss of autonomy and privacy; fear of serious illness; lack of control over activities of daily living; lack of clear communication between treatment team and patients; and death of a patient roommate.^49,50 Furthermore, consider the physical discomfort and emotional distress of patients with urinary incontinence awaiting assistance for a diaper or bedding change or the pain of repetitive blood draws or other invasive testing. Although individualized, the subjective discomfort and emotional distress associated with these experiences undoubtedly contribute to the stress of hospitalization.

Animal Models of Stress

Laboratory techniques reminiscent of the numerous environmental stressors associated with hospitalization have been used to reliably trigger allostatic overload in healthy young animals.⁵¹ These techniques include sequential exposure to aversive stimuli, including food and water deprivation, continuous overnight illumination, paired housing with known and unknown cagemates, mobility restriction, soiled cage conditions, and continuous noise. All of these techniques have been shown to cause HPA axis and ANS dysfunction, allostatic overload, and subsequent stress-mediated consequences to multiple organ systems.^19,52-54 Given the remarkable similarity of these protocols to common experiences during hospitalization, animal models of stress may be useful in understanding the spectrum of maladaptive consequences experienced by patients within the hospital (Figure 1).

These animal models of stress have resulted in a number of instructive findings. For example, in rodents, extended stress exposure induces structural and functional remodeling of neuronal networks that precipitate learning and memory, working memory, and attention impairments.^55-57 These exposures also result in cardiovascular abnormalities, including dyslipidemia, progressive atherosclerosis,^58,59 and enhanced inflammatory cytokine expression,⁶⁰ all of which increase both atherosclerotic burden and susceptibility to plaque rupture, leading to elevated risk for major cardiovascular adverse events. Moreover, these extended stress exposures in animals increase susceptibility to both bacterial and viral infections and increase their severity.^16,61 This outcome appears to be driven by a stress-induced elevation of glucocorticoid levels, decreased leukocyte proliferation, altered leukocyte trafficking, and a transition to a proinflammatory cytokine environment.^{16, 61} Allostatic overload has also been shown to contribute to metabolic dysregulation involving insulin resistance, persistence of hyperglycemia, dyslipidemia, catabolism of lean muscle, and visceral adipose tissue deposition.^62-64 In addition to cardiovascular, immune, and metabolic consequences of allostatic overload, the spectrum of physiologic dysfunction in animal models is broad and includes mood disorder symptoms,⁶⁵ intestinal barrier abnormalities,⁶⁶ airway reactivity exacerbation,⁶⁷ and enhanced tumor growth.⁶⁸

Although the majority of this research highlights the multisystem effects of variable stress exposure in healthy animals, preliminary evidence suggests that aged or diseased animals subjected to additional stressors display a heightened inflammatory cytokine response that contributes to exaggerated sickness behavior and greater and prolonged cognitive deficits.⁶⁹ Future studies exploring the consequences of extended stress exposure in animals with existing disease or debility may therefore more closely simulate the experience of hospitalized patients and perhaps further our understanding of PHS.

Hospitalized Patients

While no intervention studies have examined the effects of potential hospital stressors on the development of allostatic overload, there is evidence from small studies that dysregulated stress responses during hospitalization are associated with adverse events. For example, high serum cortisol, catecholamine, and proinflammatory cytokine levels during hospitalization have individually been associated with the development of cognitive dysfunction,^70-72 increased risk of cardiovascular events such as myocardial infarction and stroke in the year following discharge,^73-76 and the development of wound infections after discharge.⁷⁷ Moreover, elevated plasma glucose during admission for myocardial infarction in patients with or without diabetes has been associated with greater in-hospital and 1-year mortality,⁷⁸ with a similar relationship seen between elevated plasma glucose and survival after admission for stroke⁷⁹ and pneumonia.⁸⁰ Furthermore, in addition to atherothrombosis, stress may contribute to the risk for venous thromboembolism,⁸¹ resulting in readmissions for deep vein thrombosis or pulmonary embolism posthospitalization. Although potentially surrogate markers of illness acuity, a handful of studies have shown that these stress biomarkers are actually only weakly correlated with,⁸² or independent of,^72,76 disease severity. As discussed in detail below, future studies utilizing a summative measure of multisystem physiologic dysfunction as opposed to individual biomarkers may more accurately reflect the cumulative stress effects of hospitalization and subsequent risk for adverse events.

Additional Considerations

Elderly patients, in particular, may have heightened susceptibility to the consequences of allostatic overload due to common geriatric issues such as multimorbidity and frailty. Patients with chronic diseases display both baseline HPA axis abnormalities as well as dysregulated stress responses and may therefore be more vulnerable to hospitalization-related stress. For example, when subjected to psychosocial stress, patients with chronic conditions such as diabetes, heart failure, or atherosclerosis demonstrate elevated cortisol levels, increased circulating markers of inflammation, as well as prolonged hemodynamic recovery after stress resolution compared with normal controls.^83-85 Additionally, frailty may affect an individual’s susceptibility to exogenous stress. Indeed, frailty identified on hospital admission increases the risk for adverse outcomes during hospitalization and postdischarge.⁸⁶ Although the specific etiology of this relationship is unclear, persons with frailty are known to have elevated levels of cortisol and other inflammatory markers,^87,88 which may contribute to adverse outcomes in the face of additional stressors.

A large body of evidence stretching from bench to bedside suggests that environmental stressors associated with hospitalization are toxic. Understanding PHS within the context of hospital-induced allostatic overload presents a unifying theory for the interrelated multisystem dysfunction and increased susceptibility to adverse events that patients experience after discharge (Figure 2). Furthermore, it defines a potential pathophysiological mechanism for the cognitive impairment, elevated cardiovascular risk, immune system dysfunction, metabolic derangements, and functional decline associated with PHS. Additionally, this theory highlights environmental interventions to limit PHS development and suggests mechanisms to promote stress resilience. Although it is difficult to disentangle the consequences of the endogenous stress triggered by an acute illness from the exogenous stressors related to hospitalization, it is likely that the 2 simultaneous exposures compound risk for stress system dysregulation and allostatic overload. Moreover, hospitalized patients with preexisting HPA axis dysfunction at baseline from chronic disease or advancing age may be even more susceptible to these adverse outcomes. If this hypothesis is true, a reduction in PHS would require mitigation of the modifiable environmental stressors encountered by patients during hospitalization. Directed efforts to diminish ambient noise, limit nighttime disruptions, thoughtfully plan procedures, consider ongoing nutritional status, and promote opportunities for patients to exert some control over their environment may diminish the burden of extrinsic stressors encountered by all patients in the hospital and improve outcomes after discharge.

Hospitals are increasingly recognizing the importance of improving patients’ experience of hospitalization by reducing exposure to potential toxicities. For example, many hospitals are now attempting to reduce sleep disturbances and sleep latency through reduced nighttime noise and light levels, fewer nighttime interruptions for vital signs checks and medication administration, and commonsensical interventions like massages, herbal teas, and warm milk prior to bedtime.⁸⁹ Likewise, intensive care units are targeting environmental and physical stressors with a multifaceted approach to decrease sedative use, promote healthy sleep cycles, and encourage exercise and ambulation even in those patients who are mechanically ventilated.³⁰ Another promising development has been the increase of Hospital at Home programs. In these programs, patients who meet the criteria for inpatient admission are instead comprehensively managed at home for their acute illness through a multidisciplinary effort between physicians, nurses, social workers, physical therapists, and others. Patients hospitalized at home report higher levels of satisfaction and have modest functional gains, improved health-related quality of life, and decreased risk of mortality at 6 months compared with hospitalized patients.^90,91 With some admitting diagnoses (eg, heart failure), hospitalization at home may be associated with decreased readmission risk.⁹² Although not yet investigated on a physiologic level, perhaps the benefits of hospital at home are partially due to the dramatic difference in exposure to environmental stressors.

A tool that quantifies hospital-associated stress may help health providers appreciate the experience of patients and better target interventions to aspects of their structure and process that contribute to allostatic overload. Importantly, allostatic overload cannot be identified by one biomarker of stress but instead requires evidence of dysregulation across inflammatory, neuroendocrine, hormonal, and cardiometabolic systems. Future studies to address the burden of stress faced by hospitalized patients should consider a summative measure of multisystem dysregulation as opposed to isolated assessments of individual biomarkers. Allostatic load has previously been operationalized as the summation of a variety of hemodynamic, hormonal, and metabolic factors, including blood pressure, lipid profile, glycosylated hemoglobin, cortisol, catecholamine levels, and inflammatory markers.⁹³ To develop a hospital-associated allostatic load index, models should ideally be adjusted for acute illness severity, patient-reported stress, and capacity for stress resilience. This tool could then be used to quantify hospitalization-related allostatic load and identify those at greatest risk for adverse events after discharge, as well as measure the effectiveness of strategic environmental interventions (Table 2). A natural first experiment may be a comparison of the allostatic load of hospitalized patients versus those hospitalized at home.

After discharge from the hospital, patients have a significantly elevated risk for adverse events, including emergency department use, hospital readmission, and death. More than 1 in 3 patients discharged from the hospital require acute care in the month after hospital discharge, and more than 1 in 6 require readmission, with readmission diagnoses frequently differing from those of the preceding hospitalization.^1-4 This heightened susceptibility to adverse events persists beyond 30 days but levels off by 7 weeks after discharge, suggesting that the period of increased risk is transient and dynamic.⁵

The term posthospital syndrome (PHS) describes this period of vulnerability to major adverse events following hospitalization.⁶ In addition to increased risk for readmission and mortality, patients in this period often show evidence of generalized dysfunction with new cognitive impairment, mobility disability, or functional decline.^7-12 To date, the etiology of this vulnerability is neither well understood nor effectively addressed by transitional care interventions.¹³

One hypothesis to explain PHS is that stressors associated with the experience of hospitalization contribute to transient multisystem dysfunction that induces susceptibility to a broad range of medical maladies. These stressors include frequent sleep disruption, noxious sounds, painful stimuli, mobility restrictions, and poor nutrition.¹² The stress hypothesis as a cause of PHS is therefore based, in large part, on evidence about allostasis and the deleterious effects of allostatic overload.

Allostasis defines a system functioning within normal stress-response parameters to promote adaptation and survival.¹⁴ In allostasis, the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic and parasympathetic branches of the autonomic nervous system (ANS) exist in homeostatic balance and respond to environmental stimuli within a range of healthy physiologic parameters. The hallmark of a system in allostasis is the ability to rapidly activate, then successfully deactivate, a stress response once the stressor (ie, threat) has resolved.^14,15 To promote survival and potentiate “fight or flight” mechanisms, an appropriate stress response necessarily impacts multiple physiologic systems that result in hemodynamic augmentation and gluconeogenesis to support the anticipated action of large muscle groups, heightened vigilance and memory capabilities to improve rapid decision-making, and enhancement of innate and adaptive immune capabilities to prepare for wound repair and infection defense.^14-16 The stress response is subsequently terminated by negative feedback mechanisms of glucocorticoids as well as a shift of the ANS from sympathetic to parasympathetic tone.^17,18

Extended or repetitive stress exposure, however, leads to dysregulation of allostatic mechanisms responsible for stress adaptation and hinders an efficient and effective stress response. After extended stress exposure, baseline (ie, resting) HPA activity resets, causing a disruption of normal diurnal cortisol rhythm and an increase in total cortisol concentration. Moreover, in response to stress, HPA and ANS system excitation becomes impaired, and negative feedback properties are undermined.^14,15 This maladaptive state, known as allostatic overload, disrupts the finely tuned mechanisms that are the foundation of mind-body balance and yields pathophysiologic consequences to multiple organ systems. Downstream ramifications of allostatic overload include cognitive deterioration, cardiovascular and immune system dysfunction, and functional decline.^14,15,19

Although a stress response is an expected and necessary aspect of acute illness that promotes survival, the central thesis of this work is that additional environmental and social stressors inherent in hospitalization may unnecessarily compound stress and increase the risk of HPA axis dysfunction, allostatic overload, and subsequent multisystem dysfunction, predisposing individuals to adverse outcomes after hospital discharge. Based on data from both human subjects and animal models, we present a possible pathophysiologic mechanism for the postdischarge vulnerability of PHS, encourage critical contemplation of traditional hospitalization, and suggest interventions that might improve outcomes.

Posthospital syndrome (PHS) describes a transient period of vulnerability after hospitalization during which patients are at elevated risk for adverse events from a broad range of conditions. In support of this characterization, epidemiologic data have demonstrated high rates of adverse outcomes following hospitalization. For example, data have shown that more than 1 in 6 older adults is readmitted to the hospital within 30 days of discharge.²⁰ Death is also common in this first month, during which rates of postdischarge mortality may exceed initial inpatient mortality.^21,22 Elevated vulnerability after hospitalization is not restricted to older adults, as readmission risk among younger patients 18 to 64 years of age may be even higher for selected conditions, such as heart failure.^3,23

Vulnerability after hospitalization is broad. In patients over age 65 initially admitted for heart failure or acute myocardial infarction, only 35% and 10% of readmissions are for recurrent heart failure or reinfarction, respectively.¹ Nearly half of readmissions are for noncardiovascular causes.¹ Similarly, following hospitalization for pneumonia, more than 60 percent of readmissions are for nonpulmonary etiologies. Moreover, the risk for all these causes of readmission is much higher than baseline risk, indicating an extended period of lack of resilience to many types of illness.²⁴ These patterns of broad susceptibility also extend to younger adults hospitalized with common medical conditions.³

Accumulating evidence suggests that hospitalized patients face functional decline, debility, and risk for adverse events despite resolution of the presenting illness, implying perhaps that the hospital environment itself is hazardous to patients’ health. In 1993, Creditor hypothesized that the “hazards of hospitalization,” including enforced bed-rest, sensory deprivation, social isolation, and malnutrition lead to a “cascade of dependency” in which a collection of small insults to multiple organ systems precipitates loss of function and debility despite cure or resolution of presenting illness.¹² Covinsky (2011) later defined hospitalization-associated disability as an iatrogenic hospital-related “disorder” characterized by new impairments in abilities to perform basic activities of daily living such as bathing, feeding, toileting, dressing, transferring, and walking at the time of hospital discharge.¹¹ Others have described a postintensive-care syndrome (PICS),²⁵ characterized by cognitive, psychiatric, and physical impairments acquired during hospitalization for critical illness that persist postdischarge and increase the long-term risk for adverse outcomes, including elevated mortality rates,^26,27 readmission rates,²⁸ and physical disabilities.²⁹ Similar to the “hazards of hospitalization,” PICS is thought to be related to common experiences of ICU stays, including mobility restriction, sensory deprivation, sleep disruption, sedation, malnutrition, and polypharmacy.^30-33

Taken together, these data suggest that adverse health consequences attributable to hospitalization extend across the spectrum of age, presenting disease severity, and hospital treatment location. As detailed below, the PHS hypothesis is rooted in a mechanistic understanding of the role of exogenous stressors in producing physiologic dysregulation and subsequent adverse health effects across multiple organ systems.

Nature of Stress in the Hospital

Compounding the stress of acute illness, hospitalized patients are routinely and repetitively exposed to a wide variety of environmental stressors that may have downstream adverse consequences (Table 1). In the absence of overt clinical manifestations of harm, the possible subclinical physiologic dysfunction generated by the following stress exposures may increase patients’ susceptibility to the manifestations of PHS.

Sleep Disruption

Sleep disruptions trigger potent stress responses,^34,35 yet they are common occurrences during hospitalization. In surveys, about half of patients report poor sleep quality during hospitalization that persists for many months after discharge.³⁶ In a simulated hospital setting, test subjects exposed to typical hospital sounds (paging system, machine alarms, etc.) experienced significant sleep-wake cycle abnormalities.³⁷ Although no work has yet focused specifically on the physiologic consequences of sleep disruption and stress in hospitalized patients, in healthy humans, mild sleep disruption has clear effects on allostasis by disrupting HPA activity, raising cortisol levels, diminishing parasympathetic tone, and impairing cognitive performance.^{18,34,35,38,39}

Malnourishment

Malnourishment in hospitalized patients is common, with one-fifth of hospitalized patients receiving nothing per mouth or clear liquid diets for more than 3 continuous days,⁴⁰ and one-fifth of hospitalized elderly patients receiving less than half of their calculated nutrition requirements.⁴¹ Although the relationship between food restriction, cortisol levels, and postdischarge outcomes has not been fully explored, in healthy humans, meal anticipation, meal withdrawal (withholding an expected meal), and self-reported dietary restraint are known to generate stress responses.^42,43 Furthermore, malnourishment during hospitalization is associated with increased 90-day and 1-year mortality after discharge,⁴⁴ adding malnourishment to the list of plausible components of hospital-related stress.

Mobility Restriction

Physical activity counterbalances stress responses and minimizes downstream consequences of allostatic load,¹⁵ yet mobility limitations via physical and chemical restraints are common in hospitalized patients, particularly among the elderly.^45-47 Many patients are tethered to devices that make ambulation hazardous, such as urinary catheters and infusion pumps. Even without physical or chemical restraints or a limited mobility order, patients may be hesitant to leave the room so as not to miss transport to a diagnostic study or an unscheduled physician’s visit. Indeed, mobility limitations of hospitalized patients increase the risk for adverse events after discharge, while interventions designed to encourage mobility are associated with improved postdischarge outcomes.^47,48

Other Stressors

Other hospital-related aversive stimuli are less commonly quantified, but clearly exist. According to surveys of hospitalized patients, sources of emotional stress include social isolation; loss of autonomy and privacy; fear of serious illness; lack of control over activities of daily living; lack of clear communication between treatment team and patients; and death of a patient roommate.^49,50 Furthermore, consider the physical discomfort and emotional distress of patients with urinary incontinence awaiting assistance for a diaper or bedding change or the pain of repetitive blood draws or other invasive testing. Although individualized, the subjective discomfort and emotional distress associated with these experiences undoubtedly contribute to the stress of hospitalization.

Animal Models of Stress

Laboratory techniques reminiscent of the numerous environmental stressors associated with hospitalization have been used to reliably trigger allostatic overload in healthy young animals.⁵¹ These techniques include sequential exposure to aversive stimuli, including food and water deprivation, continuous overnight illumination, paired housing with known and unknown cagemates, mobility restriction, soiled cage conditions, and continuous noise. All of these techniques have been shown to cause HPA axis and ANS dysfunction, allostatic overload, and subsequent stress-mediated consequences to multiple organ systems.^19,52-54 Given the remarkable similarity of these protocols to common experiences during hospitalization, animal models of stress may be useful in understanding the spectrum of maladaptive consequences experienced by patients within the hospital (Figure 1).

These animal models of stress have resulted in a number of instructive findings. For example, in rodents, extended stress exposure induces structural and functional remodeling of neuronal networks that precipitate learning and memory, working memory, and attention impairments.^55-57 These exposures also result in cardiovascular abnormalities, including dyslipidemia, progressive atherosclerosis,^58,59 and enhanced inflammatory cytokine expression,⁶⁰ all of which increase both atherosclerotic burden and susceptibility to plaque rupture, leading to elevated risk for major cardiovascular adverse events. Moreover, these extended stress exposures in animals increase susceptibility to both bacterial and viral infections and increase their severity.^16,61 This outcome appears to be driven by a stress-induced elevation of glucocorticoid levels, decreased leukocyte proliferation, altered leukocyte trafficking, and a transition to a proinflammatory cytokine environment.^{16, 61} Allostatic overload has also been shown to contribute to metabolic dysregulation involving insulin resistance, persistence of hyperglycemia, dyslipidemia, catabolism of lean muscle, and visceral adipose tissue deposition.^62-64 In addition to cardiovascular, immune, and metabolic consequences of allostatic overload, the spectrum of physiologic dysfunction in animal models is broad and includes mood disorder symptoms,⁶⁵ intestinal barrier abnormalities,⁶⁶ airway reactivity exacerbation,⁶⁷ and enhanced tumor growth.⁶⁸

Although the majority of this research highlights the multisystem effects of variable stress exposure in healthy animals, preliminary evidence suggests that aged or diseased animals subjected to additional stressors display a heightened inflammatory cytokine response that contributes to exaggerated sickness behavior and greater and prolonged cognitive deficits.⁶⁹ Future studies exploring the consequences of extended stress exposure in animals with existing disease or debility may therefore more closely simulate the experience of hospitalized patients and perhaps further our understanding of PHS.

Hospitalized Patients

While no intervention studies have examined the effects of potential hospital stressors on the development of allostatic overload, there is evidence from small studies that dysregulated stress responses during hospitalization are associated with adverse events. For example, high serum cortisol, catecholamine, and proinflammatory cytokine levels during hospitalization have individually been associated with the development of cognitive dysfunction,^70-72 increased risk of cardiovascular events such as myocardial infarction and stroke in the year following discharge,^73-76 and the development of wound infections after discharge.⁷⁷ Moreover, elevated plasma glucose during admission for myocardial infarction in patients with or without diabetes has been associated with greater in-hospital and 1-year mortality,⁷⁸ with a similar relationship seen between elevated plasma glucose and survival after admission for stroke⁷⁹ and pneumonia.⁸⁰ Furthermore, in addition to atherothrombosis, stress may contribute to the risk for venous thromboembolism,⁸¹ resulting in readmissions for deep vein thrombosis or pulmonary embolism posthospitalization. Although potentially surrogate markers of illness acuity, a handful of studies have shown that these stress biomarkers are actually only weakly correlated with,⁸² or independent of,^72,76 disease severity. As discussed in detail below, future studies utilizing a summative measure of multisystem physiologic dysfunction as opposed to individual biomarkers may more accurately reflect the cumulative stress effects of hospitalization and subsequent risk for adverse events.

Additional Considerations

Elderly patients, in particular, may have heightened susceptibility to the consequences of allostatic overload due to common geriatric issues such as multimorbidity and frailty. Patients with chronic diseases display both baseline HPA axis abnormalities as well as dysregulated stress responses and may therefore be more vulnerable to hospitalization-related stress. For example, when subjected to psychosocial stress, patients with chronic conditions such as diabetes, heart failure, or atherosclerosis demonstrate elevated cortisol levels, increased circulating markers of inflammation, as well as prolonged hemodynamic recovery after stress resolution compared with normal controls.^83-85 Additionally, frailty may affect an individual’s susceptibility to exogenous stress. Indeed, frailty identified on hospital admission increases the risk for adverse outcomes during hospitalization and postdischarge.⁸⁶ Although the specific etiology of this relationship is unclear, persons with frailty are known to have elevated levels of cortisol and other inflammatory markers,^87,88 which may contribute to adverse outcomes in the face of additional stressors.

A large body of evidence stretching from bench to bedside suggests that environmental stressors associated with hospitalization are toxic. Understanding PHS within the context of hospital-induced allostatic overload presents a unifying theory for the interrelated multisystem dysfunction and increased susceptibility to adverse events that patients experience after discharge (Figure 2). Furthermore, it defines a potential pathophysiological mechanism for the cognitive impairment, elevated cardiovascular risk, immune system dysfunction, metabolic derangements, and functional decline associated with PHS. Additionally, this theory highlights environmental interventions to limit PHS development and suggests mechanisms to promote stress resilience. Although it is difficult to disentangle the consequences of the endogenous stress triggered by an acute illness from the exogenous stressors related to hospitalization, it is likely that the 2 simultaneous exposures compound risk for stress system dysregulation and allostatic overload. Moreover, hospitalized patients with preexisting HPA axis dysfunction at baseline from chronic disease or advancing age may be even more susceptible to these adverse outcomes. If this hypothesis is true, a reduction in PHS would require mitigation of the modifiable environmental stressors encountered by patients during hospitalization. Directed efforts to diminish ambient noise, limit nighttime disruptions, thoughtfully plan procedures, consider ongoing nutritional status, and promote opportunities for patients to exert some control over their environment may diminish the burden of extrinsic stressors encountered by all patients in the hospital and improve outcomes after discharge.

Hospitals are increasingly recognizing the importance of improving patients’ experience of hospitalization by reducing exposure to potential toxicities. For example, many hospitals are now attempting to reduce sleep disturbances and sleep latency through reduced nighttime noise and light levels, fewer nighttime interruptions for vital signs checks and medication administration, and commonsensical interventions like massages, herbal teas, and warm milk prior to bedtime.⁸⁹ Likewise, intensive care units are targeting environmental and physical stressors with a multifaceted approach to decrease sedative use, promote healthy sleep cycles, and encourage exercise and ambulation even in those patients who are mechanically ventilated.³⁰ Another promising development has been the increase of Hospital at Home programs. In these programs, patients who meet the criteria for inpatient admission are instead comprehensively managed at home for their acute illness through a multidisciplinary effort between physicians, nurses, social workers, physical therapists, and others. Patients hospitalized at home report higher levels of satisfaction and have modest functional gains, improved health-related quality of life, and decreased risk of mortality at 6 months compared with hospitalized patients.^90,91 With some admitting diagnoses (eg, heart failure), hospitalization at home may be associated with decreased readmission risk.⁹² Although not yet investigated on a physiologic level, perhaps the benefits of hospital at home are partially due to the dramatic difference in exposure to environmental stressors.

A tool that quantifies hospital-associated stress may help health providers appreciate the experience of patients and better target interventions to aspects of their structure and process that contribute to allostatic overload. Importantly, allostatic overload cannot be identified by one biomarker of stress but instead requires evidence of dysregulation across inflammatory, neuroendocrine, hormonal, and cardiometabolic systems. Future studies to address the burden of stress faced by hospitalized patients should consider a summative measure of multisystem dysregulation as opposed to isolated assessments of individual biomarkers. Allostatic load has previously been operationalized as the summation of a variety of hemodynamic, hormonal, and metabolic factors, including blood pressure, lipid profile, glycosylated hemoglobin, cortisol, catecholamine levels, and inflammatory markers.⁹³ To develop a hospital-associated allostatic load index, models should ideally be adjusted for acute illness severity, patient-reported stress, and capacity for stress resilience. This tool could then be used to quantify hospitalization-related allostatic load and identify those at greatest risk for adverse events after discharge, as well as measure the effectiveness of strategic environmental interventions (Table 2). A natural first experiment may be a comparison of the allostatic load of hospitalized patients versus those hospitalized at home.