Diagnostic value of using epicardial fat measurement on screening low-dose chest CT for the prediction of metabolic syndrome: A cross-validation study. Issue 7 (February 2019)

• Record Type: Journal Article
• Authors: Kim, Hyun Ji; Lee, Heon; Lee, Bora; Lee, Jae Wook; Shin, Kyung Eun; Suh, Jon; Park, Hyun Woo; Kim, Jeong A.
• Other Names: Tarantino. Giovanni section editor.
• Is Part Of: Medicine. Volume 98:Issue 7(2019)
• Publication Date: 2019-02
• Subjects: computed tomography -- epicardial fat -- low-dose computed tomography -- metabolic syndrome; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000014601
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: There has been a marked increase in the use of low-dose computed tomography (LDCT) for lung cancer screening. However, the potential of LDCT to predict metabolic syndrome (MetS) has not been well-documented in this risk-sharing population. We assessed the reliability of epicardial fat volume (EFV) and epicardial fat area (EFA) measurements on chest LDCT for prediction of MetS. A total of 130 (mean age, 50.2 ± 10.77 years) asymptomatic male who underwent nonelectrocardiography (ECG)-gated LDCT were divided into 2 groups for the main analysis (n = 75) and validation (n = 55). Each group was further divided into subgroups with or without MetS. EFV and EFA were calculated semiautomatically using commercially available software with manual assistance. The area under the curve (AUC) on receiver operating characteristic (ROC) analysis and cutoff values to predict MetS on LDCT were then calculated and validated. Female data were not available for analysis due to small sample size in this self-referred lung cancer screening program. In the analysis group, the mean EFV was 123.12 ± 42.29 and 67.30 ± 20.68 cm 3 for the MetS and non-MetS subgroups, respectively ( P < .001), and the mean EFA was 7.95 ± 3.10 and 4.04 ± 1.73 cm 2, respectively ( P < .001). Using 93.65 and 4.94 as the cutoffs for EFV and EFA, respectively, the sensitivity, specificity, positive and negative predictive values, and accuracy for predicting MetS were 84.2% and 84.2%, and 92.9% and 64.3% ( P < .001); 80% and 44.4% ( P = .01); 94.5% and 92.3%; and 90.7% and 69.3% ( P < .001), respectively. The AUC for EFV and EFA for predicting MetS was 0.909 and 0.808 (95% confidence interval, 0.819–1.000 and 0.702–0.914, respectively) ( P = .02). Using the same cutoff values in the analysis group, there was no significant difference in diagnostic performance using EFV and EFA between the analysis and validation sets. Although quantification of both EFA and EFV is feasible on non-ECG-gated LDCT, EFV may be used to reliably predict MetS with fairly high and better diagnostic performance in selected population.