Dataset Information

Out-of-hours discharge from intensive care, in-hospital mortality and intensive care readmission rates: a systematic review and meta-analysis.

ABSTRACT:

Purpose

Discharge from an intensive care unit (ICU) out of hours is common. We undertook a systematic review and meta-analysis to explore the association between time of discharge and mortality/ICU readmission.

Methods

We searched Medline, Embase, Web of Knowledge, CINAHL, the Cochrane Library and OpenGrey to June 2017. We included studies reporting in-hospital mortality and/or ICU readmission rates by ICU discharge "out-of-hours" and "in-hours". Inclusion was limited to patients aged???16 years discharged alive from a non-specialist ICU to a lower level of hospital care. Studies restricted to specific diseases were excluded. We assessed study quality using the Newcastle Ottowa Scale. We extracted published data, summarising using a random-effects meta-analysis.

Results

Our searches identified 1961 studies. We included unadjusted data from 1,191,178 patients from 18 cohort studies (presenting data from 1994 to 2014). "Out of hours" had multiple definitions, beginning between 16:00 and 22:00 and ending between 05:59 and 09:00. Patients discharged out of hours had higher in-hospital mortality [relative risk (95% CI) 1.39 (1.24, 1.57) p?2 90.1% for mortality and 90.2% for readmission), resulting from differences in effect size rather than the presence of an effect.

Conclusions

Out-of-hours discharge from an ICU is strongly associated with both in-hospital death and ICU readmission. These effects persisted across all definitions of "out of hours" and across healthcare systems in different geographical locations. Whether these increases in mortality and readmission result from patient differences, differences in care, or a combination remains unclear.

SUBMITTER: Vollam S

PROVIDER: S-EPMC6061448 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

Out-of-hours discharge from intensive care, in-hospital mortality and intensive care readmission rates: a systematic review and meta-analysis.

Vollam Sarah S Dutton Susan S Lamb Sallie S Petrinic Tatjana T Young J Duncan JD Watkinson Peter P

Intensive care medicine 20180625 7

<h4>Purpose</h4>Discharge from an intensive care unit (ICU) out of hours is common. We undertook a systematic review and meta-analysis to explore the association between time of discharge and mortality/ICU readmission.<h4>Methods</h4>We searched Medline, Embase, Web of Knowledge, CINAHL, the Cochrane Library and OpenGrey to June 2017. We included studies reporting in-hospital mortality and/or ICU readmission rates by ICU discharge "out-of-hours" and "in-hours". Inclusion was limited to patients ...[more]

PMID: 29938369

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Project description:ImportanceThe Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown.ObjectiveTo examine the correlation of paired trends in hospital 30-day readmission rates and hospital 30-day mortality rates after discharge.Design, setting, and participantsRetrospective study of Medicare fee-for-service beneficiaries aged 65 years or older hospitalized with HF, AMI, or pneumonia from January 1, 2008, through December 31, 2014.ExposureThirty-day risk-adjusted readmission rate (RARR).Main outcomes and measuresThirty-day RARRs and 30-day risk-adjusted mortality rates (RAMRs) after discharge were calculated for each condition in each month at each hospital in 2008 through 2014. Monthly trends in each hospital's 30-day RARRs and 30-day RAMRs after discharge were examined for each condition. The weighted Pearson correlation coefficient was calculated for hospitals' paired monthly trends in 30-day RARRs and 30-day RAMRs after discharge for each condition.ResultsIn 2008 through 2014, 2 962 554 hospitalizations for HF, 1 229 939 for AMI, and 2 544 530 for pneumonia were identified at 5016, 4772, and 5057 hospitals, respectively. In January 2008, mean hospital 30-day RARRs and 30-day RAMRs after discharge were 24.6% and 8.4% for HF, 19.3% and 7.6% for AMI, and 18.3% and 8.5% for pneumonia. Hospital 30-day RARRs declined in the aggregate across hospitals from 2008 through 2014; monthly changes in RARRs were -0.053% (95% CI, -0.055% to -0.051%) for HF, -0.044% (95% CI, -0.047% to -0.041%) for AMI, and -0.033% (95% CI, -0.035% to -0.031%) for pneumonia. In contrast, monthly aggregate changes across hospitals in hospital 30-day RAMRs after discharge varied by condition: HF, 0.008% (95% CI, 0.007% to 0.010%); AMI, -0.003% (95% CI, -0.005% to -0.001%); and pneumonia, 0.001% (95% CI, -0.001% to 0.003%). However, correlation coefficients in hospitals' paired monthly changes in 30-day RARRs and 30-day RAMRs after discharge were weakly positive: HF, 0.066 (95% CI, 0.036 to 0.096); AMI, 0.067 (95% CI, 0.027 to 0.106); and pneumonia, 0.108 (95% CI, 0.079 to 0.137). Findings were similar in secondary analyses, including with alternate definitions of hospital mortality.Conclusions and relevanceAmong Medicare fee-for-service beneficiaries hospitalized for heart failure, acute myocardial infarction, or pneumonia, reductions in hospital 30-day readmission rates were weakly but significantly correlated with reductions in hospital 30-day mortality rates after discharge. These findings do not support increasing postdischarge mortality related to reducing hospital readmissions.

Project description:Out-of-hours discharge from ICU to the ward is associated with increased in-hospital mortality and ICU readmission. Little is known about why this occurs. We map the discharge process and describe the consequences of out-of-hours discharge to inform practice changes to reduce the impact of discharge at night. This study was part of the REcovery FoLlowing intensive CarE Treatment mixed methods study. We defined out-of-hours discharge as 16:00 to 07:59 hours. We undertook 20 in-depth case record reviews where in-hospital death after ICU discharge had been judged "probably avoidable" in previous retrospective structured judgment reviews, and 20 where patients survived. We conducted semistructured interviews with 55 patients, family members, and staff with experience of ICU discharge processes. These, along with a stakeholder focus group, informed ICU discharge process mapping using the human factors-based functional analysis resonance method. Three U.K. National Health Service hospitals, chosen to represent different hospital settings. Patients discharged from ICU, their families, and staff involved in their care. None. Out-of-hours discharge was common. Patients and staff described out-of-hours discharge as unsafe due to a reduction in staffing and skill mix at night. Patients discharged out-of-hours were commonly discharged prematurely, had inadequate handover, were physiologically unstable, and did not have deterioration recognized or escalated appropriately. We identified five interdependent function keys to facilitating timely ICU discharge: multidisciplinary team decision for discharge, patient prepared for discharge, bed meeting, bed manager allocation of beds, and ward bed made available. We identified significant limitations in out-of-hours care provision following overnight discharge from ICU. Transfer to the ward before 16:00 should be facilitated where possible. Our work highlights changes to help make day time discharge more likely. Where discharge after 16:00 is unavoidable, support systems should be implemented to ensure the safety of patients discharged from ICU at night.

Project description:ImportanceCurrent guidelines recommend use of dexamethasone, 6 mg/d, up to 10 days or until discharge for patients hospitalized with COVID-19. Whether patients who received less than 10 days of corticosteroids during hospitalization for COVID-19 benefit from continuing treatment at discharge has not been determined.ObjectiveTo assess whether continuing dexamethasone treatment at discharge is associated with reduced all-cause readmissions or mortality postdischarge.Design, setting, and participantsA retrospective cohort study was conducted at 15 medical centers within Kaiser Permanente Southern California. The population included adults who received less than 10 days of dexamethasone, 6 mg/d, until discharge during hospitalization for COVID-19 and were discharged alive between May 1 and September 30, 2020.ExposuresContinued dexamethasone treatment at discharge.Main outcomes and measuresAll-cause readmissions or mortality within 14 days from discharge.ResultsA total of 1164 patients with a median age of 55 (IQR, 44-66) years were identified. Most patients were of Hispanic ethnicity (822 [70.6%]) and male (674 [57.9%]) and required oxygen support during hospitalization (1048 [90.0%]). Of the 1164 patients, 692 (59.5%) continued dexamethasone, 6 mg/d, at discharge. A balanced cohort was created using propensity score and inverse probability of treatment weighting. The adjusted odds ratio (OR) for readmissions or mortality within 14 days was 0.87 (95% CI, 0.58-1.30) for patients who continued dexamethasone therapy at discharge compared with those who did not. Similar results were produced by a sensitivity analysis that restricted the treatment group to those who received exactly 10 days of dexamethasone (OR, 0.89; 95% CI, 0.55-1.43) and by subgroup analyses stratified by the duration of dexamethasone treatment as an inpatient (1-3 days: OR, 0.71; 95% CI, 0.43-1.16; 4-9 days: OR, 1.01; 95% CI, 0.48-2.12), oxygen requirement at discharge (room air: OR, 0.91; 95% CI, 0.53-1.59; supplemental oxygen use: OR, 0.76; 95% CI, 0.42-1.37), and disease duration at discharge (≤10 days: OR, 0.81; 95% CI, 0.49-1.33; >10 days: OR, 0.94; 95% CI, 0.48-1.86).Conclusions and relevanceIn this cohort study of patients with COVID-19, continuing treatment with dexamethasone, 6 mg/d, at discharge was not associated with a reduction in 14-day all-cause readmission or mortality. This finding suggests that dexamethasone should not be routinely prescribed beyond discharge for individuals with COVID-19.

Project description:ImportanceShortcomings in the education of patients at hospital discharge are associated with higher risks for treatment failure and hospital readmission. Whether improving communication at discharge through specific interventions has an association with patient-relevant outcomes remains unclear.ObjectiveTo conduct a systematic review and meta-analysis on the association of communication interventions at hospital discharge with readmission rates and other patient-relevant outcomes.Data sourcesPubMed, EMBASE, PsycINFO, and CINAHL were systematically searched from the inception of each database to February 28, 2021.Study selectionRandomized clinical trials that randomized patients to receiving a discharge communication intervention or a control group were included.Data extraction and synthesisTwo independent reviewers extracted data on outcomes and trial and patient characteristics. Risk of bias was assessed using the Cochrane Risk of Bias Tool. Data were pooled using a random-effects model, and risk ratios (RRs) with corresponding 95% CIs are reported. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.Main outcomes and measuresThe primary outcome was hospital readmission, and secondary outcomes included adherence to treatment regimen, patient satisfaction, mortality, and emergency department reattendance 30 days after hospital discharge.ResultsWe included 60 randomized clinical trials with a total of 16 070 patients for the qualitative synthesis and 19 trials with a total of 3953 patients for the quantitative synthesis of the primary outcome. Of these, 11 trials had low risk of bias, 6 trials had high risk of bias, and 2 trials had unclear risk of bias. Communication interventions at discharge were significantly associated with lower readmission rates (179 of 1959 patients [9.1%] in intervention groups vs 270 of 1994 patients [13.5%] in control groups; RR, 0.69; 95% CI, 0.56-0.84), higher adherence to treatment regimen (1729 of 2009 patients [86.1%] in intervention groups vs 1599 of 2024 patients [79.0%] in control groups; RR, 1.24; 95% CI, 1.13-1.37), and higher patient satisfaction (1187 of 1949 patients [60.9%] in intervention groups vs 991 of 2002 patients [49.5%] in control groups; RR, 1.41; 95% CI, 1.20-1.66).Conclusions and relevanceThese findings suggest that communication interventions at discharge are significantly associated with fewer hospital readmissions, higher treatment adherence, and higher patient satisfaction and thus are important to facilitate the transition of care.

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Project description:ImportanceTo optimize patient outcomes and preserve critical acute care access during the COVID-19 pandemic, the Los Angeles County Department of Health Services developed the SAFE @ HOME O2 Expected Practice (expected practice), enabling ambulatory oxygen management for COVID-19.ObjectiveTo assess outcomes of patients with COVID-19 pneumonia discharged via the expected practice approach to home or quarantine housing with supplemental home oxygen.Design, setting, and participantsThis retrospective cohort study included 621 adult patients with COVID-19 pneumonia who were discharged from 2 large urban public hospitals caring primarily for patients receiving Medicaid from March 20 to August 19, 2020. Patients were included in the analysis cohort if they received emergency or inpatient care for COVID-19 and were discharged with home oxygen.InterventionsPatients receiving at least 3 L per minute of oxygen, stable without other indication for inpatient care, were discharged from either emergency or inpatient encounters with home oxygen equipment, educational resources, and nursing telephone follow-up within 12 to 18 hours of discharge. Nurses provided continued telephone follow up as indicated, always with physician back-up.Main outcomes and measuresAll-cause mortality and all-cause 30-day return admission.ResultsA total of 621 patients with COVID-19 pneumonia (404 male [65.1%] and 217 female [34.9%]) were discharged with home oxygen. Median age of these patients was 51 years (interquartile range, 45-61 years), with 149 (24.0%) discharged from the emergency department and 472 (76%) discharged from inpatient encounters. The all-cause mortality rate was 1.3% (95% CI, 0.6%-2.5%) and the 30-day return hospital admission rate was 8.5% (95% CI, 6.2%-10.7%) with a median follow-up time of 26 days (interquartile range, 15-55 days). No deaths occurred in the ambulatory setting.Conclusions and relevanceIn this cohort study, patients with COVID-19 pneumonia discharged on home oxygen had low rates of mortality and return admission within 30 days of discharge. Ambulatory management of COVID-19 with home oxygen has an acceptable safety profile, and the expected practice approach may help optimize outcomes, by ensuring right care in the right place at the right time and preserving access to acute care during the COVID-19 pandemic.

Project description:BackgroundReducing the 30-day unplanned hospital readmission rate is a goal for physicians and policymakers in order to improve quality of care. However, data on the readmission rate of critically ill patients in Japan and knowledge of the predictors associated with readmission are lacking. We investigated predictors associated with 30-day rehospitalization for medical and surgical adult patients separately.MethodsPatient data from 502 acute care hospitals with intensive care unit (ICU) facilities in Japan were retrospectively extracted from the Japanese Diagnosis Procedure Combination (DPC) database between April 2012 and February 2014. Factors associated with unplanned hospital readmission within 30 days of hospital discharge among medical and surgical ICU survivors were identified using multivariable logistic regression analysis.ResultsOf 486,651 ICU survivors, we identified 5583 unplanned hospital readmissions within 30 days of discharge following 147,423 medical hospitalizations (3.8% readmitted) and 11,142 unplanned readmissions after 339,228 surgical hospitalizations (3.3% readmitted). The majority of unplanned hospital readmissions, 60.9% of medical and 63.1% of surgical case readmissions, occurred within 15 days of discharge. For both medical and surgical patients, the Charlson comorbidity index score; category of primary diagnosis during the index admission (respiratory, gastrointestinal, and metabolic and renal); hospital length of stay; discharge to skilled nursing facilities; and having received a packed red blood cell transfusion, low-dose steroids, or renal replacement therapy were significantly associated with higher unplanned hospital readmission rates.ConclusionsFrom patient data extracted from a large Japanese national database, the 30-day unplanned hospital readmission rate after ICU stay was 3.4%. Further studies are required to improve readmission prediction models and to develop targeted interventions for high-risk patients.

Project description:IntroductionSeptic patients requiring intensive care unit (ICU) readmission are at high risk of mortality, but research focusing on the association of ICU readmission due to sepsis and mortality is limited. The aim of this study was to develop and validate a machine learning (ML) model for predicting in-hospital mortality in septic patients readmitted to the ICU using routinely available clinical data.MethodsThe data used in this study were obtained from the Medical Information Mart for Intensive Care (MIMIC-IV, v1.0) database, between 2008 and 2019. The study cohort included patients with sepsis requiring ICU readmission. The data were randomly split into a training (75%) data set and a validation (25%) data set. Nine popular ML models were developed to predict mortality in septic patients readmitted to the ICU. The model with the best accuracy and area under the curve (A.C.) in the validation cohort was defined as the optimal model and was selected for further prediction studies. The SHAPELY Additive explanations (SHAP) values and Local Interpretable Model-Agnostic Explanation (LIME) methods were used to improve the interpretability of the optimal model.ResultsA total of 1117 septic patients who had required ICU readmission during the study period were enrolled in the study. Of these participants, 434 (38.9%) were female, and the median (interquartile range [IQR]) age was 68.6 (58.4-79.2) years. The median (IQR) ICU interval duration was 2.60 (0.64-5.78) days. After feature selection, 31 of 47 clinical factors were ultimately chosen for use in model construction. Of the nine ML models tested, the best performance was achieved with the random forest (RF) model, with an A.C. of 0.81, an accuracy of 85% and a precision of 62% in the validation cohort. The SHAP summary analysis revealed that Glasgow Coma Scale score, urine output, blood urea nitrogen, lactate, platelet count and systolic blood pressure were the top six most important factors contributing to the RF model. Additionally, the LIME method demonstrated how the RF model works in terms of explaining risk of death prediction in septic patients requiring ICU readmission.ConclusionThe ML models reported here showed a good prognostic prediction ability in septic patients requiring ICU readmission. Of the features selected, the parameters related to organ perfusion made the largest contribution to outcome prediction during ICU readmission in septic patients.

Dataset Information

Out-of-hours discharge from intensive care, in-hospital mortality and intensive care readmission rates: a systematic review and meta-analysis.

Purpose

Methods

Results

Conclusions

Publications

Out-of-hours discharge from intensive care, in-hospital mortality and intensive care readmission rates: a systematic review and meta-analysis.

Similar Datasets

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