The American Heart Association’s Predicting Risk of Cardiovascular Disease Events equations demonstrated significantly better accuracy in predicting 10-year cardiovascular mortality than the widely used Pooled Cohort Equations, according to a recent study.
The external validation of the American Heart Association's Predicting Risk of Cardiovascular Disease Events (PREVENT) cardiovascular disease (CVD) risk equations was conducted using a cohort representative of the U.S. population from the National Health and Nutrition Examination Survey. This study, published in JAMA Network Open, aimed to evaluate the prognostic capabilities, calibration, and discrimination of the PREVENT equations in predicting CVD-related mortality over a 10-year follow-up period.
The cohort comprised 172.9 million participants, with a mean age of 45.0 years, and 52.1% women. The PREVENT equations demonstrated predictive performance for fatal CVD events, with a C-statistic of 0.890, indicating excellent discrimination. The PREVENT equations performed better than the Pooled Cohort Equations (PCE), a previously established model, with improvements in both calibration and risk reclassification. A 1% increase in PREVENT risk estimates was associated with a 9% increase in CVD mortality risk (hazard ratio, 1.090; 95% confidence interval, 1.087-1.094).
The PREVENT equations showed a strong ability to differentiate between patients at higher and lower risk of cardiovascular mortality, as indicated by a C-statistic of 0.890. However, the model exhibited some calibration issues, as reflected by a slope of 1.13, meaning it slightly underpredicted actual risk. Compared to the PCE, the PREVENT model significantly improved risk classification, with a net reclassification index of 0.093, indicating better performance in predicting cardiovascular outcomes.
Full disclosures can be found in the published study.