• Kazem Rahimi, Derrick Bennett, Nathalie Conrad, Timothy M Williams, Joyee Basu, Jeremy Dwight, Mark Woodward, Anushka Patel, John McMurray, and Stephen MacMahon.
• George Institute for Global Health, University of Oxford, Oxford, United Kingdom; Division of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom; Department of Cardiology, Oxford University Hospitals NHS Trust, Oxford, United Kingdom. Electronic address: kazem.rahimi@georgeinstitute.ox.ac.uk.
• JACC Heart Fail. 2014 Oct 1; 2 (5): 440-6.

ObjectivesThis study sought to review the literature for risk prediction models in patients with heart failure and to identify the most consistently reported independent predictors of risk across models.BackgroundRisk assessment provides information about patient prognosis, guides decision making about the type and intensity of care, and enables better understanding of provider performance.MethodsMEDLINE and EMBASE were searched from January 1995 to March 2013, followed by hand searches of the retrieved reference lists. Studies were eligible if they reported at least 1 multivariable model for risk prediction of death, hospitalization, or both in patients with heart failure and reported model performance. We ranked reported individual risk predictors by their strength of association with the outcome and assessed the association of model performance with study characteristics.ResultsSixty-four main models and 50 modifications from 48 studies met the inclusion criteria. Of the 64 main models, 43 models predicted death, 10 hospitalization, and 11 death or hospitalization. The discriminatory ability of the models for prediction of death appeared to be higher than that for prediction of death or hospitalization or prediction of hospitalization alone (p = 0.0003). A wide variation between studies in clinical settings, population characteristics, sample size, and variables used for model development was observed, but these features were not significantly associated with the discriminatory performance of the models. A few strong predictors emerged for prediction of death; the most consistently reported predictors were age, renal function, blood pressure, blood sodium level, left ventricular ejection fraction, sex, brain natriuretic peptide level, New York Heart Association functional class, diabetes, weight or body mass index, and exercise capacity.ConclusionsThere are several clinically useful and well-validated death prediction models in patients with heart failure. Although the studies differed in many respects, the models largely included a few common markers of risk.Copyright © 2014 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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