Abstract

Abstract Background Epicardial adipose tissue (EAT) volume and density has shown to correlate with standard markers of coronary artery disease (CAD) and may predict major adverse cardiovascular events (MACE). Purpose We aimed to evaluate the prognostic value of EAT volume and density measured by fully automated deep-learning software from non-contrast cardiac computed tomography (CT). Methods We assessed 2071 consecutive asymptomatic subjects (age 56±9 years, 59% male) from the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) trial with long-term follow-up after coronary artery calcium (CAC) measurement. EAT volume and mean density were quantified using automated deep-learning software from non-contrast cardiac CT. MACE was defined as myocardial infarction (MI), cardiac death, late (>90 days) revascularization and acute coronary syndrome (ACS). EAT volume and density were systematically compared to CAC score and atherosclerotic cardiovascular disease (ASCVD) risk score using Cox proportional hazards regression for MACE prediction. Results At 14±3 years, 217 subjects suffered MACE. In age-and-gender-adjusted multivariate analysis, ASCVD risk score, CAC (two-fold increase) and EAT volume (two-fold increase) were associated with increased risk of suffering MACE [Hazard Ratio (HR) (95% CI): 1.03 (1.01–1.04); 1.25 (1.19–1.30); and 1.36 (1.08–1.70) respectively, p<0.01 for all] (Figure); the corresponding Harrell's C-statistic was 0.76. The area-under-the curve from receiver-operator characteristic analysis for MACE prediction increased significantly from 0.69 to 0.77 (p<0.0001) when EAT volume and CAC were added to the current clinical standard (ASCVD, family history and obesity measures BMI and BSA). Both in men and women, increase in EAT volume was associated with increased risk of MACE, with HR 1.14 (1.06–1.22), p<0.001 in men vs. 1.15 (1.01–1.31), p=0.03 in women, for each 20 cubic centimeter increase in volume. EAT density (HU) was independently inversely associated with MACE [HR: 0.96 (0.93–0.99), p=0.01]. MACE Prediction Conclusions EAT volume and density measurements improve prediction of MACE in asymptomatic populations over the current clinical standard. Fully automated EAT volume and density quantification by deep-learning from non-contrast cardiac CT can provide additional prognostic value for the asymptomatic patient. Acknowledgement/Funding 1R01HL133616, Forschungsstiftung Medizin Universitätsklinikum Erlangen, grant from Dr Miriam and Sheldon G. Adelson Medical Research Foundation

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