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Original Investigation |

Preventing 30-Day Hospital Readmissions:  A Systematic Review and Meta-analysis of Randomized Trials

Aaron L. Leppin, MD1; Michael R. Gionfriddo, PharmD1,2; Maya Kessler, MD1,3; Juan Pablo Brito, MBBS1,3,4; Frances S. Mair, MD5; Katie Gallacher, MBChB5; Zhen Wang, PhD1,4; Patricia J. Erwin, MLS6; Tanya Sylvester, BS7; Kasey Boehmer, BA1,8; Henry H. Ting, MD, MBA1; M. Hassan Murad, MD1,4; Nathan D. Shippee, PhD9; Victor M. Montori, MD1,3,4
[+] Author Affiliations
1Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, Minnesota
2Mayo Graduate School, Mayo Clinic, Rochester, Minnesota
3Department of Medicine, Mayo Clinic, Rochester, Minnesota
4Mayo Clinic Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota
5General Practice and Primary Care, Institute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland, United Kingdom
6Mayo Clinic Libraries, Mayo Clinic, Rochester, Minnesota
7medical student at St Louis University School of Medicine, St Louis, Missouri
8graduate student at University of Minnesota School of Public Health, Minneapolis
9Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis
JAMA Intern Med. 2014;174(7):1095-1107. doi:10.1001/jamainternmed.2014.1608.
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Importance  Reducing early (<30 days) hospital readmissions is a policy priority aimed at improving health care quality. The cumulative complexity model conceptualizes patient context. It predicts that highly supportive discharge interventions will enhance patient capacity to enact burdensome self-care and avoid readmissions.

Objective  To synthesize the evidence of the efficacy of interventions to reduce early hospital readmissions and identify intervention features—including their impact on treatment burden and on patients’ capacity to enact postdischarge self-care—that might explain their varying effects.

Data Sources  We searched PubMed, Ovid MEDLINE, Ovid EMBASE, EBSCO CINAHL, and Scopus (1990 until April 1, 2013), contacted experts, and reviewed bibliographies.

Study Selection  Randomized trials that assessed the effect of interventions on all-cause or unplanned readmissions within 30 days of discharge in adult patients hospitalized for a medical or surgical cause for more than 24 hours and discharged to home.

Data Extraction and Synthesis  Reviewer pairs extracted trial characteristics and used an activity-based coding strategy to characterize the interventions; fidelity was confirmed with authors. Blinded to trial outcomes, reviewers noted the extent to which interventions placed additional work on patients after discharge or supported their capacity for self-care in accordance with the cumulative complexity model.

Main Outcomes and Measures  Relative risk of all-cause or unplanned readmission with or without out-of-hospital deaths at 30 days postdischarge.

Results  In 42 trials, the tested interventions prevented early readmissions (pooled random-effects relative risk, 0.82 [95% CI, 0.73-0.91]; P < .001; I2 = 31%), a finding that was consistent across patient subgroups. Trials published before 2002 reported interventions that were 1.6 times more effective than those tested later (interaction P = .01). In exploratory subgroup analyses, interventions with many components (interaction P = .001), involving more individuals in care delivery (interaction P = .05), and supporting patient capacity for self-care (interaction P = .04) were 1.4, 1.3, and 1.3 times more effective than other interventions, respectively. A post hoc regression model showed incremental value in providing comprehensive, postdischarge support to patients and caregivers.

Conclusions and Relevance  Tested interventions are effective at reducing readmissions, but more effective interventions are complex and support patient capacity for self-care. Interventions tested more recently are less effective.

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Figure 1.
The Cumulative Complexity Model

Patient context is represented as a balance between workload and capacity. This balance must be optimized to ensure care effectiveness and improve outcomes. In turn, the outcomes achieved feed back to affect the workload-capacity balance.

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Figure 2.
Results of Primary Meta-analysis

Size of the data marker corresponds to the relative weight assigned in the pooled analysis using random-effects models. RR indicates relative risk.

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