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Year : 2020  |  Volume : 33  |  Issue : 1  |  Page : 303-308

Epicardial fat measured by multidetector computed tomography and coronary artery disease

1 Department of Cardiology, Faculty of Medicine, Menoufia University, Menoufia, Egypt
2 Department of Specialist in Cardiology, Nasser Institute, Cairo, Egypt

Correspondence Address:
Mohammad F Alnaggar
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/mmj.mmj_262_18

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Objective To assess the relationship between epicardial fat (EF) as well as pericoronary fat measured by multidetector computed tomography (CT) and coronary calcium score, and coronary artery disease (CAD). Background EF is associated with increased risk of cardiovascular events. CT is an accurate and highly reproducible method to measure EF and can be used as a noninvasive tool to assess the cardiovascular risk. Patients and methods The current study included 70 patients with suspected CAD (low-intermediate probability). All patients were subjected to 256-row multidetector CT coronary angiography scans and were divided into three groups: group 1: no atherosclerosis (20 patients), group 2: nonobstructive atherosclerosis (luminal narrowing <50% in diameter) (25 patients), and group 3: obstructive atherosclerosis (luminal narrowing ≥50%) (25 patients). EF thickness and the mean thickness of the pericoronary fat surrounding the three coronary arteries were measured by CT. Results Calcium score, the average EF thickness, and the average pericoronary fat thickness (PCFT) were significantly higher in group 3 compared with other groups (P < 0.001, 0.013, and <0.001, respectively). Receiver operating characteristic curve was used to define the best cutoff value of the thickness of both epicardial and pericoronary fat in predicting obstructive CAD (group 3), and it was more than or equal to 7.2 and 12.6 mm for epicardial and pericoronary fat, respectively. Conclusion There was a positive correlation between both epicardial and PCFT and the severity of coronary stenosis. Epicardial and PCFT can be used in predicting the severity of CAD.

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