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. 2025 Jul 22;86(3):181-192.
doi: 10.1016/j.jacc.2025.04.066.

AHA PREVENT Equations and Cardiovascular Disease Risk in Diverse Health Care Populations

So Mi Jemma Cho  1 , Michael Levin  2 , Robert Chen  3 , Ryan Samarakoon  4 , Renae Judy  5 , Katherine Hartmann  6 , Darina Postupaka  7 , Victoria Viscosi  7 , Eric Farber-Eger  4 , Akl C Fahed  8 , Michael C Honigberg  8 , Chirag J Patel  9 , Whitney Hornsby  7 , Quinn S Wells  4 , Scott M Damrauer  10 , Ron Do  11 , Pradeep Natarajan  12
Affiliations
Free article

AHA PREVENT Equations and Cardiovascular Disease Risk in Diverse Health Care Populations

So Mi Jemma Cho et al. J Am Coll Cardiol. .
Free article

Abstract

Background: The American Heart Association's Predicting Risk of cardiovascular Events (PREVENT) aims to improve cardiovascular risk prediction. Whether PREVENT suitably predicts 10-year incident atherosclerotic cardiovascular disease (ASCVD) in health care populations is unknown.

Objectives: This study sought to evaluate the calibration and discrimination of the PREVENT equations across integrated U.S. health care systems.

Methods: We retrospectively evaluated electronic health records of 270,320 patients (Mass General Brigham [MGB]: 136,654; Mount Sinai Health: 43,321; Penn Medicine: 56,889; Vanderbilt University Medical Center: 33,456) aged 30 to 79 years and without prior ASCVD from 2010 to 2014. We compared 10-year estimated ASCVD risk based on PREVENT and empirically observed first ASCVD event over a 10-year follow-up. Calibration was assessed based on Greenwood-Nam-d'Agostino test, discordance, and mean calibration. Discrimination was assessed with the use of the time-dependent Harrell's C-index.

Results: Based on PREVENT, the mean estimated 10-year ASCVD risk was 4.9% ± 4.7% in MGB (mean age 54 years, 42% female), 6.0% ± 5.6% in Mount Sinai (mean age 56 years, 54% female), 6.0% ± 5.2% in Penn (mean age 58 years, 55% female), and 4.8% ± 1.3% in Vanderbilt (mean age 60 years, 51% female). Although PREVENT underestimated the observed incidence rate in MGB (discordance: -71.0%), Mount Sinai (discordance: -36.2%), and Vanderbilt (discordance: -40.0%), it more closely mirrored the empirical rate in Penn (+1.3%). Overall, PREVENT yielded moderate discrimination C-index in MGB (0.70 [95% CI: 0.70-0.70]), Mount Sinai (0.74 [95% CI: 0.73-0.75]), Penn (0.69 [95% CI: 0.68-0.70]), and Vanderbilt (0.73 [95% CI: 0.72-0.74]). Nevertheless, calibration differed by sex, with greater underestimation among women in MGB (discordance: -80.7%) and Vanderbilt (discordance: -55.5%) but among men in Mount Sinai (discordance: -40.7%). The race and ethnicity-stratified predictive performance varied across health care systems. Compared with the pooled cohort equations, PREVENT demonstrated better overall calibration in Penn (+2.5% vs +93.6%) but worse in MGB (-70.0% vs -41.6%) and Mount Sinai (-36.4% vs 4.8%), notwithstanding comparable discrimination. The PREVENT predictive performance further differed with better discrimination among patients without diabetes mellitus or antihypertensives.

Conclusions: The PREVENT model moderately discriminated ASCVD incidence across 4 geographically distinct academic health care systems in the United States. However, calibration metrics varied widely across health care systems, sociodemographics, and underlying cardiometabolic comorbidities.

Keywords: cardiovascular disease; epidemiology; primary prevention; risk prediction.

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Conflict of interest statement

Funding Support and Author Disclosures Dr Cho has received funding from the National Heart, Lung, and Blood Institute (NHLBI; K099HL177340). Dr Levin has received support from the Doris Duke Foundation (2023-0224) and Department of Veterans Affairs Biomedical Research and Development Award IK2-BX006551. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government. Dr Hartmann receives funding from NHLBI (5-T32-EB-004311-20). Mr Chen is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH; T32-GM007280). Dr Fahed receives funding from NHLBI (K08HL161448, R01HL164629); is co-founder of Goodpath; has served as scientific advisor to MyOme, HeartFlow, and Aditum Bio; and has received a research grant from Foresite Labs. Dr Honigberg is supported by NHLBI (K08HL166687) and the American Heart Association (940166, 24RGRSG1275749); has received consulting fees from Comanche Biopharma; has served on an advisory board for Miga Health; has done site principal investigator work for Novartis; and has received research support from Genentech, all unrelated to the present work. Dr Do is supported by the National Institute of General Medical Sciences of the NIH (R35-GM124836); is a scientific co-founder, consultant for, and equity holder in Pensieve Health (pending); and is a consultant for Variant Bio, all unrelated to this work. Dr Natarajan is supported by grants from NHLBI (R01HL127564) and Massachusetts General Hospital (Paul and Phyllis Fireman Endowed Chair in Vascular Medicine); has received research grants from Allelica, Amgen, Apple, Boston Scientific, Genentech/Roche, and Novartis; has received personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Creative Education Concepts, CRISPR Therapeutics, Eli Lilly & Co, Esperion Therapeutics, Foresite Labs, Genentech/Roche, GV, HeartFlow, Magnet Biomedicine, Merck, Novartis, TenSixteen Bio, and Tourmaline Bio; has equity in Bolt, Candela, Mercury, MyOme, Parameter Health, Preciseli, and TenSixteen Bio; and his wife is employed at Vertex Pharmaceuticals, all unrelated to the present work. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The other authors have no relationships relevant to the contents of this paper to disclose.

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