Abstract Body (Do not enter title and authors here): Background: The American Heart Association published the new PREVENT equations for estimating atherosclerotic cardiovascular disease (ASCVD) risk.
Question: Do the PREVENT equations improve risk prediction for 10-year ASCVD compared with the pooled cohort equations (PCEs) in a large diverse population?
Aim: To assess ASCVD risk prediction performance of the two PREVENT equations [PREVENT Base, PREVENT Base plus optional predictors including urine albumin-to-creatinine ratio, glycated hemoglobin, and social deprivation index (PREVENT Full)] compared with the PCEs.
Methods: We included adults aged 40-75 years without a history of ASCVD or diabetes from Kaiser Permanente Southern California in 2009 and followed them through 2019. Outcome was incident ASCVD defined as myocardial infarction, fatal coronary heart disease, fatal and nonfatal stroke. We compared model discrimination (Harrell’s C), mean calibration (estimated as the ratio of predicted to observed event rates), and calibration curve among the overall population and stratified by sex and race/ethnicity.
Results: Of the 559,111 adults (mean age 54, 11% Black, 32% Hispanic), 10,695 developed an ASCVD event during a median follow-up of 10 years. Harrell’s C among the overall population was 0.741 (95% CI 0.736-0.745) for PREVENT Base, 0.743 (0.738-0.748) for PREVENT Full, and 0.741 (0.736-0.746) for the PCEs (Table). Compared with the PCEs, both PREVENT equations improved Harrell’s C in men but not in women, and in Black adults but not in other racial/ethnic groups. Both PREVENT equations were well-calibrated (mean calibration ranged 0.83-1.37; calibration slope ranged 0.71-1.28), while the PCEs overestimated 10-year ASCVD risk (mean calibration ranged 1.80-2.20; calibration slope ranged 0.34-0.45) (Table & Figure).
Conclusion: Compared with the PCEs, both PREVENT Base and Full equations improved calibration in predicting 10-year ASCVD risk, with minimal improvement in discrimination in men and Black adults only.