Incremental value of epicardial fat volume to coronary artery calcium score and traditional risk factors for predicting myocardial ischemia in patients with suspected coronary artery disease

Abstract

BackgroundEpicardial fat volume (EFV) has been reported to be associated with coronary artery disease (CAD). CAD is the leading cause of myocardial ischemia and myocardial ischemia is closely related to major adverse cardiovascular events. We hypothesized that EFV could provide incremental value to traditional risk factors and coronary artery calcium score (CACS) in predicting myocardial ischemia in Chinese patients with suspected CAD. MethodsWe retrospectively studied 204 Chinese patients with suspected CAD who underwent single-photon emission computerized tomography-myocardial perfusion imaging (SPECT-MPI) combined with computed tomography (CT). Pericardial contours were manually defined, and EFV was automatically calculated. A reversible perfusion defect with summed difference score (SDS) ≥ 2 was defined as myocardial ischemia. ResultsThe myocardial ischemia group had higher EFV than normal MPI group (137.80 ± 34.95cm3 vs. 106.63 ± 29.10 cm3, P < .001). In multivariable logistic regression analysis, high EFV was significantly associated with myocardial ischemia [odds ratio (OR): 8.30, 95% CI: 3.72-18.49, P < .001]. Addition of EFV to CACS and traditional risk factors could predict myocardial ischemia more effectively, with larger AUC .82 (P < .001), positive net reclassification index .14 (P = .04) and integrated discrimination improvement .14 (P < .001). The bootstrap resampling method (times = 500) was used to internally validation and calculate the 95% confidence interval (CI) of the AUC (95% CI .75-.87). The calibration curve for the probability of myocardial ischemia demonstrated good agreement between prediction and observation. ConclusionsIn Chinese patients with suspected CAD, EFV was significantly associated with myocardial ischemia, and improved prediction of myocardial ischemia above traditional risk factors and CACS.