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Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR

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Abstract

To explore the diagnostic performance of a machine-learning-based (ML-based) computed fractional flow reserve (cFFR) derived from coronary computed tomography angiography (CCTA) in identifying ischemia-causing lesions verified by invasive FFR in catheter coronary angiography (ICA). We retrospectively studied 117 intermediate coronary artery lesions [40–80% diameter stenosis (DS)] from 105 patients (mean age 62 years, 32 female) who had undergone invasive FFR. CCTA images were used to compute cFFR values on the workstation. DS and the myocardium jeopardy index (MJI) of coronary stenosis were also assessed with CCTA. The diagnostic performance of cFFR was evaluated, including its correlation with invasive FFR and its diagnostic accuracy. Then, its performance was compared to that of combined DS and MJI. Of the 117 lesions, 36 (30.8%) had invasive FFR ≤ 0.80; 22 cFFR were measured as true positives and 74 cFFR as true negatives. The average time of cFFR assessment was 18 ± 7 min. The cFFR correlated strongly to invasive FFR (Spearman’s coefficient 0.665, p < 0.01). When diagnosing invasive FFR ≤ 0.80, the accuracy of cFFR was 82% with an AUC of 0.864, which was significantly higher than that of DS (accuracy 75%, AUC 0.777, p = 0.013). The AUC of cFFR was not significantly different from that of combined DS and MJI (0.846, p = 0.743). cFFR ≤ 0.80 based on CCTA showed good diagnostic performance for detecting ischemia-producing lesions verified by invasive FFR. The short calculation time required renders cFFR promising for clinical use.

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Funding

This study was funded by the Zhejiang Provincial Natural Science Foundation of China (Grant Number: LY16H180001).

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Authors and Affiliations

  1. Department of Radiology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, 3 East Qingchun Road, Hangzhou, 310006, Zhejiang, People’s Republic of China

    Xiuhua Hu, Lu Han & Yujiao Du

  2. Biomedical Engineering, CT Collaboration of Siemens Healthineers, No. 278, Zhouzhu Road, Pudong New District, Shanghai, 201318, People’s Republic of China

    Minglei Yang

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  1. Xiuhua Hu
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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The requirement for written informed consent was waived because this was a retrospective observational study.

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Hu, X., Yang, M., Han, L. et al. Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR. Int J Cardiovasc Imaging 34, 1987–1996 (2018). https://doi.org/10.1007/s10554-018-1419-9

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