Prediction of major adverse cardiovascular events using comprehensive AI analysis of calcifications and fat depots in CT calcium score images

Abstract

Coronary calcium Agatston score and epicardial adipose tissue (EAT) volume, as measured from CT calcium score (CTCS) images, are known risk factors for major adverse cardiovascular events (MACE). Here, we present greatlyexpanded analysis using coronary artery calcification (CAC) features, which more thoroughly capture pathophysiology of atherosclerosis, and EAT features, including HU thought to reflect inflammation, a harbinger of atherosclerosis. MACEenriched dataset (2316 patients, 13.6% MACE) was divided into balanced training/testing (70/30). We employed manually segmented CACs and automatically segmented EAT using DeepFat. Calcium-omics and fat-omics features were crafted to capture pathophysiology. Elastic-net was employed for feature reduction, and Cox proportional hazards model was used to design novel calcium-fat-omics model. Baseline Agatston and EAT volume models yielded 2-year-AUC training/testing results of (72.7%/68.2%) and (60.7%/55.6%), respectively. Our novel comprehensive analyses with some readily available clinical features gave improved results: calcium-omics (82.6%/72.2%), fat-omics (76.7%/71.7%), and calcium-fat-omics (83.7%/73.6%). In Kaplan-Meier survival analysis, the calcium-fat-omics model greatly improved risk stratification as compared to the standard Agatston model with five-risk intervals, suggesting improvement for personalized medicine.