Predicting Risk of Cardiovascular Disease Events (PREVENT) Versus Pooled Cohort Equations for 10-Year Atherosclerotic...

Accurate estimation of atherosclerotic cardiovascular disease (ASCVD) risk underpins primary prevention strategies. The Pooled Cohort Equations (PCE), long used in U.S. guidelines, face criticism for risk overestimation and reliance on race-based coefficients. In 2023, the American Heart Association introduced the race-neutral PREVENT equations, which integrate metabolic, renal, and socioeconomic factors to improve calibration and clinical relevance. However, comparative evidence has not been systematically synthesized.

PubMed, EMBASE, Scopus, and Google Scholar were searched (January 2010-September 2025) for studies directly comparing PREVENT and PCE within the same adult cohorts. Eligible studies reported discrimination (AUC/C-statistic), calibration, or reclassification metrics. Random-effects models using restricted maximum likelihood pooled standardized mean differences. Heterogeneity, influence, and publication bias were assessed using I², leave-one-out analyses, and Egger's test.

Eight studies including 2,296,156 adults without baseline cardiovascular disease and 5-15 years of follow-up, were analyzed. No significant difference in discrimination was observed between PREVENT and PCE (pooled SMD 0.93; 95% CI -1.56 to 3.41; p=0.465). Heterogeneity was substantial (I²=100%), though leave-one-out analyses confirmed robustness. Subgroup analyses by ASCVD versus broader CVD outcomes showed consistent findings. Egger's test revealed no small-study effects (p=0.352). Across individual studies, Several studies reported differences in calibration and risk reclassification between PREVENT and PCE.

PREVENT and PCE exhibit similar discrimination for 10-year ASCVD risk despite marked heterogeneity. PREVENT may offer differences in calibration and risk reclassification across populations. Ongoing validation across diverse populations is warranted to confirm generalizability and long-term clinical utility across healthcare systems.