Deep Learning-Derived Pericardial Adipose Tissue by Electrocardiogram-Gated Cardiac Computed Tomography Predicts Cardiovascular Events Beyond Coronary Calcium Score

Pericardial adipose tissue (PAT) is an emerging imaging biomarker of cardiovascular disease (CVD) risk. We evaluated whether PAT volume improves prediction of cardiovascular events beyond Coronary Artery Calcium (CAC) score and the AHA Predicting Risk of Cardiovascular Disease Events (PREVENT TM ) equation. We analyzed data of 11,897 Olmsted County, MN, residents without known history of heart failure and atherosclerotic cardiovascular disease (ASCVD) events who underwent non-contrast, ECG-gated CAC scans. PAT volume (cm 3 ) and density (Hounsfield units) were measured using a validated automated deep learning (DL) segmentation model (Dice score 0.94). We stratified patients into groups based on PAT volume (cut-off: top quartile 319 cm 3 ) and CAC score (cut-off: 100 Agatston units). Cox proportional hazard models evaluated associations with the composite outcome of total CVD (myocardial infarction, ischemic stroke, heart failure, and cardiovascular death), while adjusting for cardiovascular risk factors, and CAC score across PREVENT TM risk categories. Over a median of 16.4 years, 9.6 % experienced total CVD events. Kaplan-Meier curves demonstrated progressively lower CVD-free survival across PAT and CAC groups (p < 0.001). PAT volume stratified risk across CAC categories, with higher volume conferring incremental risk even at low CAC (HR = 1.24, 95% CI:1.05–1.48), and enhanced risk stratification within borderline and intermediate PREVENT TM categories. PAT volume provides prognostic value for total CVD events beyond PREVENT TM and CAC scoring.