Key result
ASCVD Pooled Cohort Equations outperform PREVENT and Globorisk for predicting 10-year CVD risk.
Why the study?
Widely used cardiovascular risk prediction models such as the ASCVD pooled cohort equations, Framingham Risk Score, Globorisk, and PREVENT have demonstrated variable performance across populations, motivating a comparison of their discrimination, calibration, and clinical utility in a contemporary UK cohort.
How do the ASCVD, Framingham Risk Score, Globorisk, and PREVENT models compare in discrimination, calibration, and clinical utility for predicting 10-year incident cardiovascular events in a contemporary UK primary prevention cohort?
Cohort (n=183,124)
Yes
How do the ASCVD, Framingham Risk Score, Globorisk, and PREVENT models compare in discrimination, calibration, and clinical utility for predicting 10-year incident cardiovascular events in a contemporary UK primary prevention cohort?
In a large contemporary UK Biobank cohort, the 2013 ASCVD Pooled Cohort Equations outperformed FRS, Globorisk, and the newer 2023 PREVENT equations in discrimination, calibration, and net clinical benefit for 10-year cardiovascular risk prediction, though all models performed poorly in adults over 59 years.
Answers are grounded in this paper and cite their sources.
ASCVD equations may retain utility in UK adults; challenges PREVENT superiority and leaves open local recalibration.
Background Cardiovascular disease (CVD) remains the leading cause of global mortality. Accurate and generalizable risk prediction models are essential for effective primary prevention. Widely used models such as the Atherosclerotic Cardiovascular Disease (ASCVD) pooled cohort equations, Framingham Risk Score (FRS), Globorisk, and the more recent Predicting Risk of cardiovascular disease EVENTs (PREVENT) model have demonstrated variable performance across populations. Objective To assess and compare the discrimination, calibration, and clinical utility of the ASCVD, FRS, Globorisk, and PREVENT models for predicting 10-year cardiovascular risk in a large contemporary UK cohort. Methods A total of 183,124 participants without prior cardiovascular disease were selected from the UK Biobank cohort and followed for up to 10 years for incident cardiovascular events. Risk scores were computed using baseline clinical variables as specified by each model: ASCVD (2013 ACC/AHA Pooled Cohort Equations, Goff et al.), FRS (2008 general cardiovascular risk model, D'Agostino et al.), Globorisk (UK-specific calibration, Hajifathalian et al., 2015), and PREVENT (2023 AHA equations, Khan et al.). Model performance was evaluated using receiver operating characteristic (ROC) analysis, area under the curve (AUC) with 95% confidence intervals, calibration analysis, sensitivity, specificity, positive and negative predictive values, Decision Curve Analysis (DCA), and Net Reclassification Improvement (NRI). Results Over the 10-year follow-up period, 10,560 incident cardiovascular events were recorded (5.8% event rate). ASCVD demonstrated the best overall discrimination (AUC = 0.717, 95% CI: 0.712–0.723), closely followed by FRS (AUC = 0.715, 95% CI: 0.709–0.721). PREVENT achieved intermediate performance (AUC = 0.668, 95% CI: 0.662–0.674), while Globorisk showed the lowest discrimination (AUC = 0.605, 95% CI: 0.599–0.611). ASCVD demonstrated the best calibration across risk strata. Decision Curve Analysis indicated that ASCVD provided the highest net clinical benefit across most decision thresholds. NRI analysis confirmed that no single model provided consistent reclassification benefit over others. Model performance declined across all models in older adults (> 59 years). Conclusion ASCVD and FRS demonstrate acceptable discrimination and calibration for 10-year CVD risk prediction in a contemporary UK cohort, with ASCVD showing comparatively superior clinical utility on Decision Curve Analysis. PREVENT, though designed with broader applicability, showed intermediate performance. Globorisk demonstrated the lowest discriminatory ability in this cohort. Performance declines in older adults across all models underscore the need for age-specific recalibration and population-tailored risk prediction frameworks.
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Nagarajan et al. (2026) conducted a cohort in Primary prevention of cardiovascular disease (n=183,124). 10-year cardiovascular risk prediction models (ASCVD Pooled Cohort Equations, Framingham Risk Score, Globorisk, and PREVENT) vs. Pairwise comparison across ASCVD, FRS, Globorisk, and PREVENT models was evaluated on 10-year incident cardiovascular events (fatal and non-fatal myocardial infarction and stroke) (AUC 0.717 for ASCVD; AUC 0.715 for FRS; AUC 0.668 for PREVENT; AUC 0.605 for Globorisk, 95% CI 0.712-0.723, p=<0.001). ASCVD Pooled Cohort Equations achieved the best discrimination (AUC 0.717, 95% CI 0.712-0.723) and calibration for 10-year CVD risk in the UK Biobank, outperforming PREVENT (AUC 0.668) and Globorisk (AUC 0.605).
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