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JAMA. 2011 Oct 19;306(15):1688-98. doi: 10.1001/jama.2011.1515.

Risk prediction models for hospital readmission: a systematic review.

Author information

  • 1Department of General Internal Medicine, Portland Veterans Affairs Medical Center, Mailcode RD71, 3710 SW US Veterans Hospital Rd, Portland, OR 97239, USA. kansagar@ohsu.edu

Abstract

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-adjust readmission rates for the purposes of hospital comparison.

OBJECTIVE:

To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use.

DATA SOURCES AND STUDY SELECTION:

The databases of MEDLINE, CINAHL, and the Cochrane Library were searched from inception through March 2011, the EMBASE database was searched through August 2011, and hand searches were performed of the retrieved reference lists. Dual review was conducted to identify studies published in the English language of prediction models tested with medical patients in 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.

DATA SYNTHESIS:

Of 7843 citations reviewed, 30 studies of 26 unique models met the inclusion criteria. The most common outcome used was 30-day readmission; only 1 model specifically addressed preventable readmissions. Fourteen models that relied on retrospective administrative data could be potentially used to risk-adjust readmission rates for hospital comparison; of these, 9 were tested in large US populations and had poor discriminative ability (c statistic range: 0.55-0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c statistic range: 0.56-0.72), and 5 could be used at hospital discharge (c statistic range: 0.68-0.83). Six studies compared different models in the same population and 2 of these found that functional and social variables improved model discrimination. Although most models incorporated variables for medical comorbidity and use of prior medical services, few examined variables associated with overall health and function, illness severity, or social determinants of health.

CONCLUSIONS:

Most current readmission risk prediction models that were designed for either comparative or clinical purposes perform poorly. Although in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.

PMID:
22009101
[PubMed - indexed for MEDLINE]
PMCID:
PMC3603349
Free PMC Article

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