Journal of the American Heart Association · 2026
Comprehensive Evaluation of Atherosclerotic Cardiovascular Disease Risk Predictors Across the Pooled Cohort Equations, Predicting Risk of Cardiovascular Disease Events, and an Expanded Model.
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Zhang Yixin, Schaefer Ernst J, Ikezaki Hiroaki, Diffenderfer Margaret R, Lloyd-Jones Donald M, Hoogeveen Ron C, Ballantyne Christie M, DeFilippis Andrew P, Guan Weihua, Tsai Michael Y, Liu Ching-Ti
Аннотация
BACKGROUND: Ten-year atherosclerotic cardiovascular disease (ASCVD) risk prediction models include the pooled cohort equations (PCE) and the Predicting Risk of Cardiovascular Disease Events (PREVENT) models. We evaluated the relative contributions of predictors in these models, along with social determinants and emerging biomarkers.
METHODS: We pooled data from 13 108 participants (58.4% female, 22.6% Black participants, median age 61 years) across 3 prospective cohorts: ARIC (Atherosclerosis Risk in Communities), FOS (Framingham Offspring Study), and MESA (Multi-Ethnic Study of Atherosclerosis). Of these participants, 873 (6.7%) developed ASCVD within 10 years. Candidate predictors included variables from PCE and PREVENT-ASCVD, alongside small dense low-density lipoprotein cholesterol, hs-CRP (high-sensitivity C-reactive protein), lipoprotein(a), and education level. We fit Cox proportional hazards models and applied stepwise selection, elastic net, and random forest for variable selection. Selected predictors were integrated into an exploratory model, Expanded ASCVD Non-traditional Determinants (EXPAND), which was compared with PCE and PREVENT-ASCVD both as originally published and after refitting in our sample.
RESULTS: Most predictors shared by PCE and PREVENT-ASCVD were selected across methods; small dense low-density lipoprotein cholesterol, hs-CRP, and education were also selected, whereas self-reported race was not. Using published coefficients, PCE overestimated 10-year ASCVD risk, whereas PREVENT-ASCVD underestimated risk. Compared with PCE and PREVENT-ASCVD models refitted in our sample, EXPAND showed modest calibration advantages, particularly among Black men, and consistently achieved higher C-statistics.
CONCLUSIONS: In our sample, self-reported race did not improve ASCVD risk prediction, whereas small dense low-density lipoprotein cholesterol, hs-CRP, and education level added predictive value, suggesting potential utility in including additional biomarkers and social factors.
METHODS: We pooled data from 13 108 participants (58.4% female, 22.6% Black participants, median age 61 years) across 3 prospective cohorts: ARIC (Atherosclerosis Risk in Communities), FOS (Framingham Offspring Study), and MESA (Multi-Ethnic Study of Atherosclerosis). Of these participants, 873 (6.7%) developed ASCVD within 10 years. Candidate predictors included variables from PCE and PREVENT-ASCVD, alongside small dense low-density lipoprotein cholesterol, hs-CRP (high-sensitivity C-reactive protein), lipoprotein(a), and education level. We fit Cox proportional hazards models and applied stepwise selection, elastic net, and random forest for variable selection. Selected predictors were integrated into an exploratory model, Expanded ASCVD Non-traditional Determinants (EXPAND), which was compared with PCE and PREVENT-ASCVD both as originally published and after refitting in our sample.
RESULTS: Most predictors shared by PCE and PREVENT-ASCVD were selected across methods; small dense low-density lipoprotein cholesterol, hs-CRP, and education were also selected, whereas self-reported race was not. Using published coefficients, PCE overestimated 10-year ASCVD risk, whereas PREVENT-ASCVD underestimated risk. Compared with PCE and PREVENT-ASCVD models refitted in our sample, EXPAND showed modest calibration advantages, particularly among Black men, and consistently achieved higher C-statistics.
CONCLUSIONS: In our sample, self-reported race did not improve ASCVD risk prediction, whereas small dense low-density lipoprotein cholesterol, hs-CRP, and education level added predictive value, suggesting potential utility in including additional biomarkers and social factors.