Background: Risk prediction models can assist in identifying individuals at risk of adverse events and also the judicious allocation of scare resources. Our objective was to describe risk prediction models for the rehospitalisation of individuals with chronic heart failure (CHF) and identify the elements contributing to these models.
Methods: The electronic data bases MEDLINE, PsychINFO, Ovid Evidence-Based Medicine Reviews and Scopus (1950-2010), were searched for studies that describe models to predict all-cause hospital readmission for individuals with CHF. Search terms included: patient readmission; risk; chronic heart failure, congestive heart failure and heart failure. We excluded non-English studies, pediatric studies, and publications without original data.
Results: Only 1 additional model was identified since the review undertaken by Ross and colleagues in 2008. All models were derived from data sets collected in the United States and patients were followed from 60 days to 18 months. The only common predictors of re-hospitalisation in the models identified by Ross and colleagues were a history of diabetes mellitus and a history of prior hospitalisation. The additional model extends its scope to include the non clinical factors of social instability and socioeconomic status as predictors of rehospitalisation.
Conclusions: In spite of the burden of hospitalisation in CHF, there are limited tools to assist clinicians in assessing risk. Developing risk prediction models, based on patient, provider and system characteristics may assist in identifying individuals in the community at greatest risk and in need of targeted interventions to improve outcomes.
Copyright Â© 2011. Published by Elsevier Ltd.