An expanded ASCVD risk model incorporating small dense LDL cholesterol, hs-CRP, and education level achieved higher C-statistics and better calibration than the PCE and PREVENT-ASCVD models.
Cohort (n=13,108)
Yes
Does the EXPAND model improve 10-year ASCVD risk prediction compared to PCE and PREVENT-ASCVD models in a pooled cohort?
Incorporating small dense LDL-C, hs-CRP, and education level into ASCVD risk prediction models improves performance and calibration compared to traditional models, while self-reported race does not add predictive value.
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.
Zhang et al. (2026) conducted a cohort in Atherosclerotic cardiovascular disease (n=13,108). Expanded ASCVD Non-traditional Determinants (EXPAND) model vs. PCE and PREVENT-ASCVD models was evaluated on 10-year ASCVD risk prediction. An expanded ASCVD risk model incorporating small dense LDL cholesterol, hs-CRP, and education level achieved higher C-statistics and better calibration than the PCE and PREVENT-ASCVD models.