AHA: PREVENT Risk Calculator Estimates Risk for Cardiovascular Disease

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PREVENT equations developed and validated in adults aged 30 to 79 years without known CVD and included eGFR as a predictor

By Elana Gotkine HealthDay Reporter

MONDAY, Nov. 13, 2023 (HealthDay News) — The Predicting Risk of Cardiovascular Disease (CVD) Events (PREVENT) risk calculator estimates the risk for CVD, including heart failure, according to a methods paper and accompanying scientific statement published online Nov. 10 in Circulation to coincide with the American Heart Association Scientific Sessions 2023, held from Nov. 11 to 13 in Philadelphia.

Sadiya S. Khan, M.D., from the Northwestern University Feinberg School of Medicine in Chicago, and colleagues developed and validated the PREVENT equations among U.S. adults aged 30 to 79 years without known CVD. The derivation sample included individual-level participant data from 25 datasets with 3,281,919 participants; external validation was performed in 3,330,085 participants from 21 additional datasets. Predictors included traditional risk factors and estimated glomerular filtration rate (eGFR). The researchers identified 211,515 incident total CVD events during a mean follow-up of 4.8 years. In external validation for CVD, the median C-statistics were 0.794 and 0.757 in female and male participants, respectively. For men and women, the calibration curves were 1.03 and 0.94, respectively. Similar estimates for discrimination and calibration were seen for atherosclerotic CVD- and heart failure-specific models.

In a scientific statement, Khan and colleagues discuss the clinical implications of the PREVENT equations. The authors note that use of the equations allows 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure). The sex-specific risk equations include eGFR as a predictor and remove race from risk prediction estimates.

“The PREVENT equations are a critical first step toward including cardiovascular-kidney-metabolic health and social factors in risk prediction for CVD,” Khan said in a statement.

Several authors disclosed ties to the biopharmaceutical industry.

Methods Paper

Scientific Statement

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