TY - JOUR
T1 - Risk prediction in patients with heart failure
T2 - A systematic review and analysis
AU - Rahimi, Kazem
AU - Bennett, Derrick
AU - Conrad, Nathalie
AU - Williams, Timothy M.
AU - Basu, Joyee
AU - Dwight, Jeremy
AU - Woodward, Mark
AU - Patel, Anushka
AU - McMurray, John
AU - MacMahon, Stephen
N1 - Funding Information:
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.
Publisher Copyright:
© 2014 American College of Cardiology Foundation.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - Objectives: 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 themodels 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 heartfailure. Although the studies differed in many respects, the models largely included a few common markers ofrisk.
AB - Objectives: 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 themodels 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 heartfailure. Although the studies differed in many respects, the models largely included a few common markers ofrisk.
KW - Death
KW - Heart failure
KW - Hospitalization
KW - Multivariable model
KW - Risk prediction
KW - Systematic review
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U2 - 10.1016/j.jchf.2014.04.008
DO - 10.1016/j.jchf.2014.04.008
M3 - Article
C2 - 25194291
AN - SCOPUS:84908071827
SN - 2213-1779
VL - 2
SP - 440
EP - 446
JO - JACC: Heart Failure
JF - JACC: Heart Failure
IS - 5
ER -