Prognostic Value of Epicardial Fat Volume Quantification Related to Coronary Artery Calcium Score and Degree of Stenosis on Coronary CT Angiography.

Abstract

Objectives: Epicardial fatty tissue volume (EFV) is regarded as an important parameter in the evaluation of coronary artery disease (CAD). The aim of our study was to investigate the prognostic value of EFV measurements related to age, gender, coronary artery calcium score (CCS) and CAD severity through coronary computed tomography angiography (CCTA).

Methods: We retrospectively evaluated a total of consecutive 688 patients who were either asymptomatic but had a positive family history or had typical or atypical symptoms suggesting the presence of CAD. They all underwent CCTA examination with multiplanar reformat (MPR), maximal intensity projection (MIP), and myocardial three-dimensional (3D) volume rendering (VRT) images were obtained. We calculated CCS, coronary artery plaque stenosis degrees, the number of main coronary arteries involved and the EFVs for each patient. Finally, the relationship between the EFVs and all other parameters was analyzed by performing the Pearson and Spearman correlation analysis.

Results: We found a statistically significant difference between the genders of the patients where males presented higher EFVs than females (p=0.001, p<0.01). The correlation between the presence of CAD and the number of main vessels involved with EFVs was also statistically significantly higher in the analysis performed with the student t-test (p=0.001, p<0.01). There was a statistically significant but weak positive correlation between the ages of the patients (r=0.271, p=0.001, p<0.01), calculated total CCSs (r=0.149, p=0.001, p<0.01) and the degree of vessel stenosis determined based on coronary artery disease reporting and data system (CAD RADS) (r=0.347, p=0.001, p<0.01) and their EFV measurements.

Conclusion: We assume that the quantification of EFV performed by the CCTA technique is a potential novel method and hence, can guide clinicians in predicting the presence and severity of CAD.