Population risk prediction models for incident heart failure: a systematic review.

BACKGROUND: The prevalence of heart failure is expected to significantly rise unless high-risk patients are effectively screened and appropriate, cost-effective prevention interventions are implemented. METHODS AND RESULTS: We performed a systematic review to evaluate the prediction characteristics of the published heart failure risk prediction models as of August 2014 using MEDLINE and EMBASE databases. Eligible studies reported the development, validation, or impact assessment of a model. Two investigators performed independent review to extract data on study design and characteristics, risk predictors, discrimination, calibration, and reclassification ability of models, as well as validation and impact analysis. We included 13 publications reporting on 28 heart failure risk prediction models. Models had acceptable-to-good discriminatory ability (c-statistics, >0.70) in the derivation sample. Calibration was less commonly assessed, but was acceptable when it was. Only 2 models were externally validated more than once, displaying modest-to-acceptable discrimination (c-statistics, 0.61-0.79). When assessed, novel blood and imaging markers modestly improved risk prediction. One model assessed the prediction properties in race-based subgroups, whereas 2 models evaluated sex-based subgroups. Impact analysis found none of the models recommended for use in any clinical practice guideline. CONCLUSIONS: Incident heart failure risk prediction remains at an early stage. The discrimination ability of current models is acceptable in derivation data sets but most models have not been externally validated. It remains unclear which models are cost-effective and best suit population screening needs. The effects of models on clinical and preventative care requires further study.

Duke Scholars

Author Stephen Greene Medicine, Cardiology