Evidence-based Synthesis Program Department of Veterans Affairs

Health Services Research & Development Service

Investigators:

Principal Investigator: 

Devan Kansagara, M.D., M.C.R.

Co-Investigators:

Honora Englander, M.D. 

Amanda Salanitro, M.D., M.S., M.S.P.H.

David Kagen, M.D.

Cecelia Theobald, M.D.

Sunil Kripalani, M.D., M.Sc.

Research Associate:

Michele Freeman, M.P.H.

Prepared for:

Department of Veterans Affairs

Veterans Health Administration

Health Services Research & Development Service

Washington, DC 20420

Prepared by:

Evidence-based Synthesis Program (ESP) Center

Portland VA Medical Center

Portland, OR

Devan Kansagara, M.D., M.C.R., Director

Risk Prediction Models for Hospital 

Readmission:

A Systematic Review

October 2011
i

Risk Prediction Models for Hospital Readmission

A Systematic Review Evidence-based Synthesis Program

PREFACE

Health Services Research & Development Service’s (HSR&D’s) Evidence-based Synthesis 

Program (ESP) was established to provide timely and accurate syntheses of targeted healthcare 

topics of particular importance to Veterans Affairs (VA) managers and policymakers, as they 

work to improve the health and healthcare of Veterans. The ESP disseminates these reports 

throughout VA.

HSR&D provides funding for four ESP Centers and each Center has an active VA affiliation. The 

ESP Centers generate evidence syntheses on important clinical practice topics, and these reports 

help:

• develop clinical policies informed by evidence,

• guide the implementation of effective services to improve patient outcomes and to 

support VA clinical practice guidelines and performance measures, and 

• set the direction for future research to address gaps in clinical knowledge.

In 2009, the ESP Coordinating Center was created to expand the capacity of HSR&D Central 

Office and the four ESP sites by developing and maintaining program processes. In addition, 

the Center established a Steering Committee comprised of HSR&D field-based investigators, 

VA Patient Care Services, Office of Quality and Performance, and Veterans Integrated Service 

Networks (VISN) Clinical Management Officers. The Steering Committee provides program 

oversight, guides strategic planning, coordinates dissemination activities, and develops 

collaborations with VA leadership to identify new ESP topics of importance to Veterans and the 

VA healthcare system.

Comments on this evidence report are welcome and can be sent to Nicole Floyd, ESP 

Coordinating Center Program Manager, at nicole.floyd@va.gov.

Recommended citation: Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, 

Freeman M and Kripalani S. Risk Prediction Models for Hospital Readmission: A Systematic 

Review. VA-ESP Project #05-225; 2011.

This report is based on research conducted by the Evidence-based Synthesis 

Program (ESP) Center located at the Portland VA Medical Center, Portland, OR 

funded by the Department of Veterans Affairs, Veterans Health Administration, 

Office of Research and Development, Health Services Research and Development. 

The findings and conclusions in this document are those of the author(s) who are 

responsible for its contents; the findings and conclusions do not necessarily represent 

the views of the Department of Veterans Affairs or the United States government. 

Therefore, no statement in this article should be construed as an official position of 

the Department of Veterans Affairs. No investigators have any affiliations or financial 

involvement (e.g., employment, consultancies, honoraria, stock ownership or options, 

expert testimony, grants or patents received or pending, or royalties) that conflict with 

material presented in the report.
1

Risk Prediction Models for Hospital Readmission

A Systematic Review Evidence-based Synthesis Program

EXECUTIVE SUMMARY

CONTEXT

Predicting hospital readmission risk is of great interest to identify which patients would benefit 

most from care transition interventions, as well as to risk-standardize readmission rates for 

purposes of hospital comparison.

OBJECTIVE

To summarize validated readmission risk prediction models, describe their performance, and 

assess suitability for clinical or administrative use.

METHODS

Data Sources: MEDLINE, CINAHL, and Cochrane Library through March 2011, EMBASE 

through August 2011, and hand search of reference lists.

Study Selection: Dual review to identify English language studies of prediction models tested 

with medical patients, with both derivation and validation cohorts. 

Data extraction: Data were extracted on the population, setting, sample size, follow-up interval, 

readmission rate, model discrimination and calibration, type of data used, and timing of data 

collection.

RESULTS

Of 7,843 citations reviewed, 30 studies of 26 unique models met criteria. The most common 

outcome used was 30-day readmission; only one model specifically addressed preventable 

readmissions. Fourteen models relying on retrospective administrative data could be potentially 

used for standardization of readmission risk and hospital comparisons; of these, nine were tested 

in large US populations and had poor discriminative ability (c-statistics 0.55 – 0.65). Seven 

models could potentially be used to identify high-risk patients for intervention early during a 

hospitalization (c-statistics 0.56 – 0.72), and five could be used at hospital discharge (c-statistics 

0.68 – 0.83). Six studies compared different models in the same population and two of these 

found that functional and social variables improved model discrimination. Though most models 

incorporated medical comorbidity and prior utilization variables, few examined variables 

associated with overall health and function, illness severity, or social determinants of health.

CONCLUSIONS

Most current readmission risk prediction models, whether designed for comparative or clinical 

purposes, perform poorly. Though in certain settings such models may prove useful, efforts to 

improve their performance are needed as use becomes more widespread.