Comprehensive Evaluation of Atherosclerotic Cardiovascular Disease Risk Predictors Across the Pooled Cohort Equations, Predicting Risk of Cardiovascular Disease Events, and an Expanded Model.
Y, Z., EJ, S., H, I., MR, D., DM, L.J., RC, H., CM, B., AP, D., W, G., & MY, T. (2026). Comprehensive Evaluation of Atherosclerotic Cardiovascular Disease Risk Predictors Across the Pooled Cohort Equations, Predicting Risk of Cardiovascular Disease Events, and an Expanded Model.. Journal of the American Heart Association. https://doi.org/10.1161/JAHA.125.049726
Y Z, EJ S, H I, MR D, DM LJ, RC H, et al. Comprehensive Evaluation of Atherosclerotic Cardiovascular Disease Risk Predictors Across the Pooled Cohort Equations, Predicting Risk of Cardiovascular Disease Events, and an Expanded Model.. Journal of the American Heart Association. 2026; doi: 10.1161/JAHA.125.049726
Y Z, EJ S, H I, et al. Comprehensive Evaluation of Atherosclerotic Cardiovascular Disease Risk Predictors Across the Pooled Cohort Equations, Predicting Risk of Cardiovascular Disease Events, and an Expanded Model.[J]. Journal of the American Heart Association. 2026. DOI: 10.1161/JAHA.125.049726.
@article{y2026,
author = {Zhang Y and Schaefer EJ and Ikezaki H and Diffenderfer MR and Lloyd-Jones DM and Hoogeveen RC and Ballantyne CM and DeFilippis AP and Guan W and Tsai MY},
title = {Comprehensive Evaluation of Atherosclerotic Cardiovascular Disease Risk Predictors Across the Pooled Cohort Equations, Predicting Risk of Cardiovascular Disease Events, and an Expanded Model.},
journal = {Journal of the American Heart Association},
year = {2026},
doi = {10.1161/JAHA.125.049726},
note = {PMID: 42535551},
}
TY - JOUR AU - Zhang Y AU - Schaefer EJ AU - Ikezaki H AU - Diffenderfer MR AU - Lloyd-Jones DM AU - Hoogeveen RC AU - Ballantyne CM AU - DeFilippis AP AU - Guan W AU - Tsai MY TI - Comprehensive Evaluation of Atherosclerotic Cardiovascular Disease Risk Predictors Across the Pooled Cohort Equations, Predicting Risk of Cardiovascular Disease Events, and an Expanded Model. T2 - Journal of the American Heart Association PY - 2026 DO - 10.1161/JAHA.125.049726 AN - PMID:42535551 ER -
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.