This 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.
Background
Risk assessment provides information about patient prognosis, guides decision making about the type and intensity of care, and enables better understanding of provider performance.
Methods
MEDLINE 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.
Results
Sixty-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.
Conclusions
There 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.
Key Words
death
heart failure
hospitalization
multivariable model
risk prediction
systematic review
Abbreviations and Acronyms
BNP
brain natriuretic peptide
LMIC
low- and middle-income country
NT-proBNP
N-terminal pro–B-type brain natriuretic peptide
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Supported by the National Institute for Health Research Oxford Biomedical Research Centre Programme. The work of the George Institute for Global Health is supported by the Oxford Martin School. Dr. Rahimi holds a National Institute for Health Research Career Development Fellowship. Ms. Conrad is an employee of IBM. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.