European Journal of Cardiovascular Medicine

Study Design and Population Prospective observational study conducted in 92 patients undergoing CT coronary angiography. CT Coronary Angiography Protocol CT coronary angiography was performed using a 384-slice multidetector CT scanner. Epicardial Fat Volume Quantification Epicardial fat was quantified using semiautomated segmentation. The pericardium was manually traced from the right pulmonary artery to the diaphragm to determine a region of interest (ROI). Within the region of interest, fat was defined as pixels within a window of −200 to −40 Hounsfield units (HU) and a window center of −120 HU. Only voxels with HUs equivalent to fat within the pericardial sac were counted as epicardial adipose tissue (EAT). Statistical Analysis Statistical analysis was performed using SPSS version 26.0. Continuous variables were expressed as mean ± standard deviation. Categorical variables were presented as frequency and percentage. Comparisons between two groups were performed using independent Student’s t-test, while comparisons across multiple groups were analyzed using one-way analysis of variance (ANOVA) with post-hoc Tukey’s HSD test. Correlation analysis was performed using Spearman correlation coefficients. Multivariate regression analysis was used to identify independent predictors of EFV. A p-value <0.05 was considered statistically significant. Figure (1) CT coronary angiography of a 55-year-old male depicting zone of epicardial fat volume (pink) – short axis view Figure (2A) and (2B) – CT angiography of a 66-year-old male showing epicardial fat volume (Pink) - (A) Axial view (B) Long axis view Figure 3 - Axial coronary CT image of a patient showing plaques in LAD and LCX Figure 4 - Multiplanar reconstructed CT images with automated left ventricular (LV) functional analysis demonstrating endocardial (red contour) and epicardial (green contour) segmentation in standard long- and short-axis views. Figure 5: CT angiography VRT image showing the heart with normal coronary arteries and LAD (green). Figure 4 (A) and (B): Coronary artery calcium scoring chart with Agatson Score and percentile graph showing identifiable calcification. Figure (5) Bar graph demonstrating progressive increase in mean EFV across CAD-RADS categories. Figure (6) Bar graph: EFV stratified by BMI categories Table 1. Baseline Characteristics Parameter Value Age (years) 54.35 ± 10.82 Male sex 64 (69.6%) BMI (kg/m²) 27.61 ± 4.25 Hypertension 46 (50.0%) Diabetes mellitus 38 (41.3%) Smoking 34 (37.0%) Table 2: Distribution of CAD-RADS Categories CAD-RADS Score Number (%) CAD-RADS 0 14 (15.2%) CAD-RADS 1 13 (14.1%) CAD-RADS 2 18 (19.6%) CAD-RADS 3 19 (20.7%) CAD-RADS 4 17 (18.5%) CAD-RADS 5 11 (12.0%) Table 3. Mean EFV Across CAD-RADS Categories CAD-RADS Mean EFV (cm³) ± SD 0 72.43 ± 15.61 1 85.37 ± 18.12 2 101.78 ± 20.44 3 118.61 ± 22.92 4 138.23 ± 24.57 5 162.87 ± 28.39 Table 4. Correlation of EFV with clinical variables Variable Spearman ρ p-value BMI 0.60 <0.001 CAD-RADS score 0.65 <0.001 Coronary calcium score 0.58 <0.001 HDL cholesterol −0.34 0.002 Table 5. Comparison of EFV according to cardiovascular risk factors Variable Mean EFV (cm³) ± SD p-value Hypertension present 134.68 ± 26.15 <0.001 Hypertension absent 98.23 ± 21.42 Diabetes present 141.22 ± 27.36 <0.001 Diabetes absent 103.11 ± 22.75 Table 6: Multivariate Linear Regression Analysis for Predictors of EFV Variable β Coefficient 95% CI (β) Derived Odds Ratio (Exp β) p-value BMI 0.41 0.28 – 0.54 1.51 < 0.001* CAD-RADS score 0.38 0.25 – 0.51 1.46 < 0.001* Diabetes Mellitus 0.26 0.09 – 0.43 1.30 0.004* Age 0.19 0.03 – 0.35 1.21 0.018* Hypertension 0.14 0.01 – 0.27 1.15 0.041* Model R² = 0.64