Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: A multi-center study and meta-analysis

• Published in: European journal of radiology Vol. 116; pp. 90 - 97
• Main Authors: Tang, Chun Xiang, Wang, Yi Ning, Zhou, Fan, Schoepf, U. Joseph, Assen, Marly van, Stroud, Robert E., Li, Jian Hua, Zhang, Xiao Lei, Lu, Meng Jie, Zhou, Chang Sheng, Zhang, Dai Min, Yi, Yan, Yan, Jing, Lu, Guang Ming, Xu, Lei, Zhang, Long Jiang
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.07.2019
• Subjects: Algorithms; Computational fluid dynamics; Computed Tomography Angiography - methods; Coronary Angiography - methods; Coronary artery disease; Coronary Artery Disease - complications; Coronary Artery Disease - diagnostic imaging; Coronary Artery Disease - physiopathology; Coronary CT angiography; Female; Fractional flow reserve; Fractional Flow Reserve, Myocardial - physiology; Humans; Machine Learning; Male; Meta analysis; Middle Aged; Multicenter study; Myocardial Ischemia - diagnostic imaging; Myocardial Ischemia - etiology; Myocardial Ischemia - physiopathology; Reproducibility of Results; Retrospective Studies; Sensitivity and Specificity; CFD; Computational fluid dynamics; cFFRML; Meta analysis; CI; DOR; LR; QUADAS-2; Multicenter study; CAD; Fractional flow reserve; Coronary artery disease; CCTA; FFR; NPV; PPV; Machine learning; FFRCT; SROC curve; Coronary CT angiography; CT-FFR
• ISSN: 0720-048X; 1872-7727; 1872-7727
• DOI: 10.1016/j.ejrad.2019.04.011

To evaluate the diagnostic performance of coronary computed tomography angiography derived fractional flow reserve (CT-FFR) with invasive fractional flow reserve (FFR) in patients with coronary artery disease" before "with invasive fractional flow reserve serving as the reference standard. CT-FFR values based on a machine learning algorithm (cFFRML) in 183 vessels of 136 patients from four centers were measured with invasive FFR as reference standard. The diagnostic performance from our multicenter study was combined into a meta-analysis following a literature search in Web of Science, PubMed, Cochrane library to identify studies comparing diagnostic performance of coronary computed tomography angiography (CCTA) and CT-FFR. Sensitivity, specificity, accuracy were analyzed on both per-vessel and per-patient basis for intermediate lesions and by algorithm. Our multicenter study demonstrated sensitivities, specificities, and accuracies of cFFRML and CCTA of 0.85, 0.94, 0.90, and 0.95, 0.28, 0.55 on a per-vessel basis, respectively. For our meta-analysis, pooled sensitivities, specificities, and accuracies of CT-FFR and CCTA were 0.85, 0.82, 0.82, and 0.85, 0.57, 0.65 with AUC of 0.86 (95%CI: 0.83˜0.89) and 0.83 (95%CI: 0.79˜0.86) on a per-vessel basis, respectively. The sensitivity, specificity and accuracy for intermediate lesions using cFFRML were 0.84, 0.92, and 0.89. No significant difference was found among different algorithms of CT-FFR (P < 0.001). This multicenter study with meta-analysis showed that CT-FFR had a high diagnostic accuracy in determining ischemia-specific lesions and intermediate lesions. There was no significant difference when comparing the combined diagnostic performance of different algorithms of CT-FFR with invasive FFR as the reference standard.