Integrating epicardial fat-omics with CAC scores in sex-specific models improved MACE prediction in females and remained independently predictive after comorbidity adjustment (HR 2.96, p<0.001).
Observational (n=40,851)
Does integrating AI-derived epicardial 'fat-omics' with CAC score improve major adverse cardiac events (MACE) prediction compared to CAC score alone in individuals undergoing CTCS for primary prevention?
Integrating AI-derived epicardial fat-omics with standard CAC scoring improves cardiovascular risk prediction specifically in females.
Effect estimate: HR 2.96
p-value: p=<0.001
Background: The Agatston CAC score from CT-calcium scoring (CTCS) is a standard guideline recommended measure for cardiovascular risk assessment that quantifies calcified plaque burden. However, many studies have reported lower discrimination of CTCS in females. Embedded epicardial adipose tissue (EAT) features have previously been shown to improve prediction of calcium scoring, and we hypothesized that it may provide incremental prognostic information in females. In this study, we evaluate whether integrating epicardial "fat-omics" with CAC score improves major adverse cardiac events (MACE) prediction from CTCS in females and assess the added value of sex-specific modeling. Methods: 40,851 individuals undergoing CTCS for primary prevention (CLARIFY Registry, NCT04075162) were analyzed. MACE was defined as myocardial infarction, stroke, revascularization, or mortality. A validated deep learning model segmented EAT, and 211 "fat-omics" features were extracted. Cox models were trained using CAC score, CAC with fat-omics (EAT-CAC), and sex-specific EAT-CAC models trained. Performance was evaluated on a held-out test set (20 %) using C-index, calibration, and decision curves. Results: < 0.0001 vs. CAC). No significant changes were observed in males. EAT-CAC models demonstrated good calibration, improved net benefit, and remained independently predictive after comorbidity adjustment (HR = 2.96, p < 0.001). Conclusions: Sex-specific risk models incorporating epicardial fat-omics from CTCS improve risk prediction in females and equity in cardiovascular risk assessment.
Singh et al. (2025) conducted an observational in Primary prevention of cardiovascular disease (n=40,851). Sex-specific EAT-CAC models (epicardial fat-omics + CAC score) vs. CAC score alone was evaluated on Major adverse cardiac events (myocardial infarction, stroke, revascularization, or mortality) (HR 2.96, p=<0.001). Integrating epicardial fat-omics with CAC scores in sex-specific models improved MACE prediction in females and remained independently predictive after comorbidity adjustment (HR 2.96, p<0.001).