Artificial Intelligence Prediction of Cardiovascular Events Using Opportunistic Epicardial Adipose Tissue Assessments From Computed Tomography Calcium Score

Recent studies have used basic epicardial adipose tissue (EAT) assessments (eg, volume and mean Hounsfield unit [HU]) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACEs). The purpose of this study was to create novel, hand-crafted EAT features, “fat-omics,” to capture the pathophysiology of EAT and improve MACE prediction. We studied a cohort of 400 patients with low-dose cardiac computed tomography calcium score examinations. We purposefully used a MACE-enriched cohort (56% event rate) for feature engineering purposes. We divided the cohort into training/testing sets (80%/20%). We segmented EAT using a previously validated, deep-learning method with optional manual correction. We extracted 148 initial EAT features (eg, morphologic, spatial, and HU), dubbed fat-omics, and used Cox elastic-net for feature reduction and prediction of MACE. Bootstrap validation gave CIs. Traditional EAT features gave marginal prediction (EAT-volume/EAT-mean-HU/BMI gave C-indices 0.53/0.55/0.57, respectively). Significant improvement was obtained with the 15-feature fat-omics model (C-index = 0.69, test set). High-risk features included the volume-of-voxels-having-elevated-HU-[-50,-30-HU] and HU-negative-skewness, both of which assess high HU values in EAT, a property implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, with the high-risk group having an HR 2.4 times that of the low-risk group (P