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Recent studies have highlighted the significance of Epicardial Adipose Tissue (EAT) on the development of Heart Failure (HF). Rather than simple EAT volume, we predicted HF from pathophysiologically-inspired EAT features opportunistically extracted from low-cost (no-cost at our institution) CT Calcium Score (CTCS) images. We segmented EAT using our deep learning algorithm, DeepFat, and collected 42 hand-crafted features (fat-omics), such as volume, spatial, thickness, and HU value distribution, where HU is thought to be an indicator of inflammation. We included readily available clinical features (e.g., Age, sex, and BMI). We used a large database of HF-enriched patients (N=1,988, HF: 5.13%) and a Cox proportional hazards model with elastic-net feature reduction and evaluated with training and testing of 80%/20% respectively. High-risk features (e.g., mean EAT thickness, EAT mean HU, and smoking) were identified using univariate analyses. Fat-omics + clinical features predicted HF with c-index (training/testing) of (78.1/72.7), respectively, exceeding results for BMI alone, EAT volume, sac volume, and clinical features. Importantly, the combined model (fatomics + clinical features) gave better stratification of patients into low- and high-risk groups using Kaplan-Meier plots with an NRI=0.11 compared to the model using clinical features alone. A univariate model based on the Agatston score gave training/testing (62.7/62.9), indicating that the fat and clinical features from CTCS images are more effective at predicting HF than traditional calcium scoring. Our combined model (fat-omics + clinical features) also showcases that the location and intensity of the EAT buildup is also a significant factor in predicting risk of HF onset and can change the relative importance of clinical features such as smoking status and sex.
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