Yasunori Nagayama1, Naoki Nakamura2, Ryo Itatani2, Seitaro Oda3, Shinichiro Kusunoki2, Hideo Takahashi2, Takeshi Nakaura3, Daisuke Utsunomiya4, Yasuyuki Yamashita3. 1. Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan. y.nagayama1980@gmail.com. 2. Department of Radiology, Minamata City General Hospital and Medical Center, 1-2-1, Tenjin-cho, Minamata, 867-0041, Japan. 3. Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto, 860-8556, Japan. 4. Department of Diagnostic Radiology, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, 236-0004, Japan.
Abstract
OBJECTIVE: To investigate whether epicardial fat volume (EFV) quantified on ECG-nongated noncontrast CT (nongated-NCCT) could be used as a reliable and reproducible predictor for coronary artery disease (CAD). METHODS: One hundred seventeen subjects (65 men, mean age 66.6 ± 11.9 years) underwent coronary CT angiography (CCTA) and nongated-NCCT during a single session because of symptoms suggestive of CAD. Two observers independently quantified EFV on both images. Correlation between CCTA-EFV and nongated-NCCT-EFV was assessed using Pearson's correlation coefficient and Bland-Altman plots. Inter-observer agreement was analyzed using concordance correlation coefficients (CCC). Coronary risk factors including EFV were compared between CAD-positive (> 50% stenosis) and CAD-negative groups. The association between EFV and CAD was analyzed using multivariate logistic regression. ROC analysis was performed, and AUC was compared with DeLong's method. RESULTS: Seventy-four subjects were diagnosed with CAD. An excellent correlation was noted between CCTA-EFV and nongated-NCCT-EFV (r = 0.948, p < 0.001), despite the systematic difference between both measurements (mean bias, 1.26). Inter-observer agreement was nearly perfect (CCC, 0.988 and 0.985 for CCTA and nongated-NCCT, respectively, p < 0.001). Significant differences were noted between subjects with versus without CAD in age, hypertension, and EFV on both types of images (p ≤ 0.026). Multivariate analysis revealed that increased EFV on CCTA (odds ratio 1.185, p = 0.003) and nongated-NCCT (odds ratio 1.20, p = 0.015) was independently associated with CAD. There was no significant difference between CCTA-EFV and nongated-NCCT-EFV in AUC for the prediction of CAD (0.659 vs 0.665, p = 0.706). CONCLUSIONS: Despite the absence of ECG gating, EFV measured on NCCT may serve as a reproducible predictor for CAD with accuracy equivalent to EFV measured on CCTA. KEY POINTS: • Despite the absence of ECG gating, the EFV on NCCT provides nearly perfect inter-observer reproducibility and shows excellent correlation with measurements on gated CCTA. • EFV on nongated-NCCT may serve as an independent biomarker for predicting coronary artery disease with accuracy equivalent to that of EFV on gated CCTA.
OBJECTIVE: To investigate whether epicardial fat volume (EFV) quantified on ECG-nongated noncontrast CT (nongated-NCCT) could be used as a reliable and reproducible predictor for coronary artery disease (CAD). METHODS: One hundred seventeen subjects (65 men, mean age 66.6 ± 11.9 years) underwent coronary CT angiography (CCTA) and nongated-NCCT during a single session because of symptoms suggestive of CAD. Two observers independently quantified EFV on both images. Correlation between CCTA-EFV and nongated-NCCT-EFV was assessed using Pearson's correlation coefficient and Bland-Altman plots. Inter-observer agreement was analyzed using concordance correlation coefficients (CCC). Coronary risk factors including EFV were compared between CAD-positive (> 50% stenosis) and CAD-negative groups. The association between EFV and CAD was analyzed using multivariate logistic regression. ROC analysis was performed, and AUC was compared with DeLong's method. RESULTS: Seventy-four subjects were diagnosed with CAD. An excellent correlation was noted between CCTA-EFV and nongated-NCCT-EFV (r = 0.948, p < 0.001), despite the systematic difference between both measurements (mean bias, 1.26). Inter-observer agreement was nearly perfect (CCC, 0.988 and 0.985 for CCTA and nongated-NCCT, respectively, p < 0.001). Significant differences were noted between subjects with versus without CAD in age, hypertension, and EFV on both types of images (p ≤ 0.026). Multivariate analysis revealed that increased EFV on CCTA (odds ratio 1.185, p = 0.003) and nongated-NCCT (odds ratio 1.20, p = 0.015) was independently associated with CAD. There was no significant difference between CCTA-EFV and nongated-NCCT-EFV in AUC for the prediction of CAD (0.659 vs 0.665, p = 0.706). CONCLUSIONS: Despite the absence of ECG gating, EFV measured on NCCT may serve as a reproducible predictor for CAD with accuracy equivalent to EFV measured on CCTA. KEY POINTS: • Despite the absence of ECG gating, the EFV on NCCT provides nearly perfect inter-observer reproducibility and shows excellent correlation with measurements on gated CCTA. • EFV on nongated-NCCT may serve as an independent biomarker for predicting coronary artery disease with accuracy equivalent to that of EFV on gated CCTA.
Entities:
Keywords:
Body fat distribution; Coronary artery disease; Multidetector computed tomography; Pericardium; Predictive value of tests
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