Comparison of invasively measured FFR with FFR derived from coronary CT angiography for detection of lesion-specific ischemia: Results from a PC-based prototype algorithm

• Published in: Journal of cardiovascular computed tomography Vol. 12; no. 2; pp. 101 - 107
• Main Authors: Röther, Jens, Moshage, Maximilian, Dey, Damini, Schwemmer, Chris, Tröbs, Monique, Blachutzik, Florian, Achenbach, Stephan, Schlundt, Christian, Marwan, Mohamed
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2018
• Subjects: Aged; Algorithms; Cardiac Catheterization - methods; cFFR; Computational fluid dynamics; Computed Tomography Angiography - methods; Coronary Angiography - methods; Coronary artery disease; Coronary Artery Disease - diagnosis; Coronary Artery Disease - diagnostic imaging; Coronary Artery Disease - physiopathology; Coronary computed tomography angiography; Coronary Stenosis - diagnosis; Coronary Stenosis - diagnostic imaging; Coronary Stenosis - physiopathology; Coronary Vessels - diagnostic imaging; Coronary Vessels - physiopathology; Female; Fractional flow reserve; Fractional Flow Reserve, Myocardial; Humans; Male; Microcomputers; Middle Aged; Predictive Value of Tests; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Retrospective Studies; Severity of Illness Index; cFFR; Computational fluid dynamics; Fractional flow reserve; Coronary artery disease; Coronary computed tomography angiography
• ISSN: 1934-5925; 1876-861X; 1876-861X
• DOI: 10.1016/j.jcct.2018.01.012

We evaluated the diagnostic accuracy of a novel prototype for on-site determination of CT-based FFR (cFFR) on a standard personal computer (PC) compared to invasively measured FFR in patients with suspected coronary artery disease. A total of 91 vessels in 71 patients (mean age 65 ± 9 years) in whom coronary CT angiography had been performed due to suspicion of coronary artery disease, and who subsequently underwent invasive coronary angiography with FFR measurement were analyzed. For both cFFR and FFR, a threshold of ≤0.80 was used to indicate a hemodynamically relevant stenosis. The mean time needed to calculate cFFR was 12.4 ± 3.4 min. A very close correlation between cFFR and FFR could be shown (r = 0.85; p < 0.0001) with Bland-Altman analysis showing moderate agreement between FFR and cFFR with mild systematic overestimation of FFR values in CT (mean difference 0.0049, 95% limits of agreement ±2SD −0.007 to 0.008). Compared to FFR, the sensitivity of cFFR to detect hemodynamically significant lesions was 91% (19/21, 95% CI: 70%–99%), specificity was 96% (67/70, 95% CI: 88%–99%), positive predictive value 86% (95% CI: 65%–97%) and negative predictive value was 97% (95% CI: 90%–100%) with an accuracy of 93%. cFFR obtained using an on-site algorithm implemented on a standard PC shows high diagnostic accuracy to detect lesions causing ischemia as compared to FFR. Importantly, the time needed for analysis is short which may be useful for improving clinical workflow.