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DOI 10.1016/j.cjca.2019.08.026

Медицинская библиотека · The Canadian journal of cardiology · 2019

Diagnostic Performance of Machine Learning Based CT-FFR in Detecting Ischemia in Myocardial Bridging and Concomitant Proximal Atherosclerotic Disease.

Zhou Fan, Wang Yi Ning, Schoepf U Joseph, Tesche Christian, Tang Chun Xiang, Zhou Chang Sheng, Xu Lei, Hou Yang, Zheng Min Wen, Yan Jing, Lu Meng Jie, Lu Guang Ming, Zhang Dai Min, Zhang Bo, Zhang Jia Yin, Zhang Long Jiang

СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

BACKGROUND

The diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in detecting ischemia in myocardial bridging (MB) has not been investigated to date.

METHODS

This retrospective multicentre study included 104 patients with left anterior descending MBs. MB was classified as either superficial or deep, short, or long, whereas all MB vessels were further divided into <50%, 50% to 69%, and ≥70% groups, according to proximal lumen stenosis on invasive coronary angiography. Diagnostic performance and receiver operating characteristics (ROC) of CT-FFR to detect lesion-specific ischemia was assessed on a per-vessel level, using invasive FFR as reference standard. Intraclass correlation coefficient (ICC) and Bland-Altman plots were used for agreement measurement.

RESULTS

Forty-eight MB vessels (46.2%) showed ischemia by invasive FFR (≤0.80). Sensitivity, specificity, and accuracy of CT-FFR to detect functional ischemia were 0.96 (0.85 to 0.99), 0.84 (0.71 to 0.92), and 0.89 (0.81 to 0.94), respectively, in all MB vessels. There were no differences in diagnostic performance between superficial and deep MB or between short and long MB (all P > 0.05). The accuracy of CT-FFR was 0.96 (0.85 to 0.99) in ≥70% stenosis, 0.82 (0.67 to 0.91) in 50% to 69% stenosis, and 0.89 (0.51 to 0.99) in <50% stenosis (P = 0.081). Bland-Altman analysis showed a slight mean difference between CT-FFR and invasive FFR of 0.014 (95% limit of agreement, -0.117 to 0.145). The ICC was 0.775 (95% confidence interval, 0.685-0.842, P < 0.001).

CONCLUSIONS

CT-FFR demonstrated high diagnostic performance for identifying functional ischemia in vessels with MB and concomitant proximal atherosclerotic disease when compared with invasive FFR. However, the clinical use of CT-FFR in patients with MB needs further study for stronger and more robust results.

Для цитирования: Zhou Fan, Wang Yi Ning, Schoepf U Joseph, Tesche Christian, Tang Chun Xiang, Zhou Chang Sheng, Xu Lei, Hou Yang, Zheng Min Wen, Yan Jing, Lu Meng Jie, Lu Guang Ming, Zhang Dai Min, Zhang Bo, Zhang Jia Yin, Zhang Long Jiang. Diagnostic Performance of Machine Learning Based CT-FFR in Detecting Ischemia in Myocardial Bridging and Concomitant Proximal Atherosclerotic Disease.. The Canadian journal of cardiology. 2019. DOI: 10.1016/j.cjca.2019.08.026.

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