A Systematic Review: Risk Prediction Models for Hospital Readmission

Predicting hospital readmission rates is of great interest, both to identify which patients would benefit most from care transition interventions and to standardize readmission risk rates for purposes of comparisons of hospital quality. This systematic review was performed to synthesize the available literature on validated readmission risk prediction models, describe their performance, and assess their suitability for clinical or administrative use. Investigators at the Evidence-based Synthesis Program Center, Portland, OR, conducted a review of the literature from database inception through March 2011.

After reviewing nearly 8,000 citations, 30 studies (23 were based on U.S. healthcare data; 4 used VA data) of 26 unique models met the inclusion criteria. Studies that focused on psychiatric, surgical, and pediatric populations were excluded because factors contributing to readmission risk might be very different in these patient groups. For each of the 30 studies used in this review, investigators assessed: population characteristics, setting, number of patients in the derivation and validation cohorts, outcomes from use of medical services, readmission rate, range of readmission rates according to predicted risk, and ability of models to discriminate between patients who were subsequently readmitted and those who were not. Fourteen models used retrospective administrative data that could potentially be used for hospital comparisons. The remainder incorporated more detailed clinical and administrative data collected from chart review, primary data collection, or the electronic health record. The most common outcome used was 30-day readmission.

Findings of this systematic review include:

• Most current readmission risk prediction models that were designed for either comparing hospital performance or clinical purposes have poor predictive ability. Although in certain settings such models may prove useful, better approaches are needed to assess hospital performance in discharging patients, as well as to identify patients at greater risk of preventable readmission.
• Only one model explicitly defined and examined potentially preventable readmissions (as opposed to all-cause readmissions) as an outcome measure.
• Most models incorporated variables for medical comorbidity and use of prior medical services, but few examined variables associated with overall health and function, illness severity, or social determinants of health. The varying performance of predictive models in different populations suggests that the best choice of a model may depend on the setting and population in which it is being used.
• Even though the overall predictive ability of the clinical models was poor, investigators found that high- and low-risk scores were associated with a clinically meaningful gradient of readmission rates. Thus, even limited ability to identify a proportion of patients at highest risk for readmission could increase the cost-effectiveness of hospital interventions aimed at improving the discharge process and post-hospital follow-up.

Policy implications

• There is no single risk prediction standard that should be incorporated, but there is an opportunity to improve standardized collection of clinically informative variables -- such as social support, substance abuse, and access to care -- that are not otherwise routinely captured and used for decision-making.
• Risk prediction models designed for measuring quality are probably not well-suited for clinical use, while models designed for clinical use may be difficult to use for quality measurement purposes.
• The potential preventability of most readmissions is unknown. Additional metrics to assess the quality of care before and after discharge, such as patient satisfaction, should be considered.

Suggestions for Future Research
Additional research is needed to assess the true preventability of readmissions in US health systems. Given the broad variety of factors that may contribute to preventable readmissions, models that include factors obtained through medical record review or patient report may be valuable. Innovations to collect broader variable types for inclusion in administrative data sets should be considered. Future studies should assess the relative contributions of different types of patient data (for example, psychosocial factors) to readmission risk prediction by comparing the performance of models with and without these variables in a given population. These models should ideally be based on population-specific conceptual frameworks of risk.

This report is a product of VA/HSR&D's Quality Enhancement Research Initiative's (QUERI) Evidence-Based Synthesis Program (ESP), which was established to provide timely and accurate synthesis of targeted healthcare topics of particular importance to VA managers and policymakers - and to disseminate these reports throughout VA.

References
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.

Kansagara D, Englander H, Salanitro A, et al. Risk Prediction Models for Hospital Readmission: A Systematic Review. JAMA October 19, 2011;306(15):1688-98.

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