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 greatly-expanded 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. MACE-enriched 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 two-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.
Patients with Diabetes Mellitus (DM) have an increased risk of Major Adverse Cardiac Events (MACE), commonly stratified via the Agatston score. In this study, we investigated Coronary Artery Calcification (CAC) patterns in patients with DM, to understand its impact on cardiovascular health. By individually segmenting CACs from over 25,000 Computed Tomography Calcium Score (CTCS) images and integrating clinical data, we created a propensity-matched cohort to isolate the effect of DM on calcification patterns by controlling for confounding variables such as age, gender, BMI, and medication use. We hand-crafted a novel set of 67 calcium-omics imaging features capturing the distribution, shape, density, and more of individual CACs and aggregated CAC features per artery. Diabetic patients, compared to nondiabetic, exhibited significantly higher Agatston (p-value: ⪅0.0005) and larger volume scores (p-value: 0.0018). Interestingly, diabetic patients had fewer calcified arteries (p-value: ⪅0005) and lower density calcifications than nondiabetic patients. Although previous studies have reported that DM leads to higher Agatston scores, to our knowledge, no studies have reported that this is driven by high-volume, low-density calcifications present in only one to two arteries. These findings suggest a distinct phenotype indicative of the continuing development of new lesions in affected arteries, possibly contributing to the increased incidence of MACE. This exploration of diabetes-related CAC patterns enhanced our understanding of the mechanisms of atherosclerotic cardiovascular disease in DM, emphasizing the need for targeted interventions and redefined cardiovascular risk models for this vulnerable population.
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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