Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter study

作者：Xue, Yi; Zheng, Min Wen; Hou, Yang; Zhou, Fan; Li, Jian Hua; Wang, Yi Ning; Liu, Chun Yu; Zhou, Chang Sheng; Zhang, Jia Yin; Yu, Meng Meng; Zhang, Bo; Zhang, Dai Min; Yi, Yan; Xu, Lei; Hu, Xiu Hua; Lu, Guang Ming; Tang, Chun Xiang; Zhang, Long Jiang^*

来源：European Radiology, 2022, 32(6): 3778-3789.

DOI：10.1007/s00330-021-08468-7

• Fractional flow reserve
• Computed tomography angiography
• Coronary artery disease
• Machine learning
• Diabetes mellitus

Objectives To examine the diagnostic accuracy of machine learning-based coronary CT angiography-derived fractional flow reserve (FFRCT) in diabetes mellitus (DM) patients. @@@ Methods In total, 484 patients with suspected or known coronary artery disease from 11 Chinese medical centers were retrospectively analyzed. All patients underwent CCTA, FFRCT, and invasive FFR. The patients were further grouped into mild (25 similar to 49 %), moderate (50 similar to 69 %), and severe (>= 70 %) according to CCTA stenosis degree and Agatston score < 400 and Agatston score >= 400 groups according to coronary artery calcium severity. Propensity score matching (PSM) was used to match DM (n = 112) and non-DM (n = 214) groups. Sensitivity, specificity, accuracy, and area under the curve (AUC) with 95 % confidence interval (CI) were calculated and compared. @@@ Results Sensitivity, specificity, accuracy, and AUC of FFRCT were 0.79, 0.96, 0.87, and 0.91 in DM patients and 0.82, 0.93, 0.89, and 0.89 in non-DM patients without significant difference (all p > 0.05) on a per-patient level. The accuracies of FFRCT had no significant difference among different coronary stenosis subgroups and between two coronary calcium subgroups (all p > 0.05) in the DM and non-DM groups. After PSM grouping, the accuracies of FFRCT were 0.88 in the DM group and 0.87 in the non-DM group without a statistical difference (p > 0.05). @@@ Conclusions DM has no negative impact on the diagnostic accuracy of machine learning-based FFRCT.

• 单位
  中国医学科学院北京协和医院; 上海交通大学; 浙江大学; 中国医科大学; 南京大学; 南方医科大学; 中国人民解放军第四军医大学; 6