Sex-specific cardiovascular risk prediction using AI-derived epicardial adipose tissue measurements on CT calcium scoring exams.

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: The cohort had a mean age of 59.2 years, with 49.4 % females, and 1017 MACE events (2.49 %) over a mean follow-up of 1.7 years. On the test set ( Conclusions: Sex-specific risk models incorporating epicardial fat-omics from CTCS improve risk prediction in females and equity in cardiovascular risk assessment.