Performance of the American Heart Association's PREVENT risk score for cardiovascular risk prediction in a multiethnic population

Roy O. Mathew, Department of Medicine, Division of Nephrology, Loma Linda VA Healthcare system, Loma Linda, CA, USA. Roy.mathew@va.gov.
Sadiya S. Khan, Department of Medicine, Division of Cardiology, Northwestern Medicine Feinberg School of Medicine, Chicago, IL, USA.
Katherine R. Tuttle, Providence Medical Research Center, Providence Inland Northwest Health, Spokane, WA, USA.
Jennifer E. Ho, Department of Medicine, Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Dmitry Abramov, Division of Cardiology, Loma Linda University Medical Center, Loma Linda, CA, USA.
Sripal Bangalore, Division of Cardiovascular Medicine, NYU Grossman School of Medicine, New York, NY, USA.
Mandeep S. Sidhu, Department of Medicine, Division of Cardiology, Albany Medical College, Albany, NY, USA.
Chiadi E. Ndumele, Division of Cardiovascular Medicine, Johns Hopkins Medicine, Baltimore, MD, USA.
Tiffany M. Powell-Wiley, Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research National Heart Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Ian J. Neeland, Harrington Heart and Vascular Institute, University Hospitals Cleveland and Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Josef Coresh, Departments of Population Health and Medicine, NYU Grossman School of Medicine, New York, NY, USA.
Mitchell S. Elkind, American Heart Association, Dallas, TX, USA.
Janani Rangaswami, Department of Medicine, Division of Nephrology, Washington DC VA Medical Center, Washington, DC, USA.

Document Type

Journal Article

Publication Date

7-4-2025

Journal

Nature medicine

DOI

10.1038/s41591-025-03789-2

Abstract

The Predicting Risk of Cardiovascular EVENTS (PREVENT) equations, created and endorsed by the American Heart Association, provide cardiovascular risk estimates for the general population, but have not yet been tested in multiethnic populations. In the present study, in a large nationwide multiethnic sample of US veterans, the utility of PREVENT to predict the risk of total cardiovascular disease (CVD: fatal and nonfatal myocardial infarction, stroke or heart failure; PREVENT-CVD), atherosclerotic cardiovascular disease (ASCVD: fatal and nonfatal myocardial infarction or stroke; PREVENT ASCVD) and heart failure was evaluated. We assessed the discrimination and calibration performance of ASCVD prediction with PREVENT ASCVD compared with a previous risk-prediction score, pooled cohort equations (PCEs). Among 2,500,291 veterans aged 30-79 years (93.9% men and 6.1% women), 407,342 total CVD events occurred over a median (interquartile range (IQR)) follow-up of 5.8 (IQR = 3.1-8.3) years. The Concordance index (C-index) (95% confidence interval (CI)) for PREVENT-CVD was 0.65 (95% CI = 0.65-0.65) in the overall sample and was similar across different race and ethnic groups (Asian, Native Hawaiian or Pacific Islander, 0.70 (95% CI = 0.69-0.71); Hispanic, 0.70 (95% CI = 0.69-0.70); non-Hispanic Black. 0.68 (95% CI = 0.68-0.69) and non-Hispanic White, 0.65 (95% CI = 0.64-0.65)). C-indices were similar between PREVENT ASCVD and PCEs and ranged from 0.61 to 0.63. Calibration slopes for PREVENT-CVD and -ASCVD in the overall sample were 1.09 (s.e. = 0.04) and 1.15 (s.e. = 0.04), respectively. In contrast, PCEs demonstrated overprediction for ASCVD with a calibration slope of 0.51 (s.e. = 0.06). Calibration slopes for PREVENT and PCEs were similar across race and ethnic groups. Among US veterans, the PREVENT equations accurately estimated CVD and ASCVD risk with some variability across race and ethnicity groups and outperformed PCEs for ASCVD risk prediction.

Department

Medicine

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