BACKGROUND
The American Heart Association Predicting Risk of Cardiovascular Disease EVENT (PREVENT) equations were developed from observational research cohorts and electronic health record data and provide sex-specific risk estimates for cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF). External validation in large contemporary samples across multiple health systems in the United States is needed.
METHODS
We assembled a national electronic health record-based cohort of US adults with individual-level patient data pooled from a collective of 30 health systems (Truveta) to externally validate the outcome-specific 10-year PREVENT equations (PREVENT-CVD, PREVENT-ASCVD, and PREVENT-HF). We included patients aged 30 to 79 years without a history of prior CVD and with an ambulatory encounter in the electronic health record between 2013 and 2018. The outcomes were defined as total CVD (composite of ASCVD and HF), ASCVD, and HF through December 2024 using diagnosis codes. Model performance of the outcome-specific PREVENT base equations was assessed with the Harrell C statistic and calibration slope, stratified by sex.
RESULTS
Of the 680 864 adults included, the mean (SD) age was 55 (13) years, and 56% were female. Over a mean (SD) follow-up of 6.8 (2.3) years, there were 29 535 incident CVD events, 19 280 incident ASCVD events, and 16 824 incident HF events. The median (interquartile range) 10-year predicted risk of PREVENT-CVD among women was 3.6% (1.3%-8.8%), and among men was 5.8% (2.5%-11.7%). The C statistic (95% CI) was 0.788 (0.786-0.790), and the calibration slope (95% CI) was 0.98 (0.95-1.01) for PREVENT-CVD. PREVENT-ASCVD and PREVENT-HF demonstrated similar C statistics (0.774 [0.771-0.777] and 0.824 [0.820-0.828]) and calibration slopes (1.07 [1.04-1.10] and 1.01 [0.97-1.04]) for prediction of the 10-year risk of ASCVD and HF, respectively.
CONCLUSIONS
The PREVENT equations accurately and precisely estimate the 10-year risk of CVD, ASCVD, and HF in a large sample of US adults. These findings support the generalizability of the PREVENT equations to inform guideline-recommended risk assessment and preventive efforts.
Sadiya S. Khan, Y. Sang, Xiaoning Huang et al.· Circulation. Population heal...· 0 citations
Abstract Background Fairness evaluation is essential for trustworthy clinical risk prediction. However, existing fairness-oriented discrimination metrics either ignore cross-group comparisons or rely on exhaustive pairwise evaluations, making them difficult to interpret and impractical for model selection. Objective This study aimed to develop and evaluate novel fairness-oriented discrimination metrics for clinical risk prediction that address limitations of within-group and pairwise cross-group approaches. Methods We examined theoretical properties of existing U-statistic–based metrics, including concordance index (CI) and area under the receiver operating characteristic curve (AUC), when applied to subgroups. We highlighted the distinction between within-group discrimination (ranking within a subgroup) and group-level discrimination (ranking relative to the broader population). Building on this framework, we proposed group-level extensions of the CI and AUC that summarize subgroup-specific performance in a single interpretable measure. We then applied these metrics to the PREVENT (Predicting Risk of Cardiovascular Disease Events) equation, a recently developed model for atherosclerotic cardiovascular disease. Results The traditional subgroup-specific CI and AUC captured within-group but not group-level discrimination, obscuring inequities in clinical decision-making. Existing cross-group approaches (eg, the xCI and xAUC metrics) addressed this limitation but became computationally and interpretively burdensome with multiple subgroups due to pairwise comparisons. Our proposed metrics provided a streamlined alternative, yielding 1 summary statistic per subgroup while retaining sensitivity to cross-group ranking disparities. Applied to PREVENT, these metrics revealed differences in subgroup performance not apparent from within-group evaluations. Conclusions By distinguishing between within-group and group-level discrimination, our framework clarifies a common source of misinterpretation in fairness evaluation. The proposed group-level extensions of the CI and AUC provide practical, interpretable tools for evaluating fairness in clinical prediction models, enabling more transparent and equitable risk assessment.
Haoyuan Wang, Chuan Hong, Michael J. Pencina et al.· JMIR AI· 0 citations
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