Diagnostic Accuracy of Fast Computational Approaches to Derive Fractional Flow Reserve From Diagnostic Coronary Angiography: The International Multicenter FAVOR Pilot Study
Shengxian Tu, Jelmer Westra, Junqing Yang, Clemens von Birgelen, Angela Ferrara, Mariano Pellicano, Holger Nef, Matteo Tebaldi, Yoshinobu Murasato, Alexandra Lansky, Emanuele Barbato, Liefke C van der Heijden, Johan H C Reiber, Niels R Holm, William Wijns, FAVOR Pilot Trial Study Group, Shengxian Tu, Jelmer Westra, Junqing Yang, Clemens von Birgelen, Angela Ferrara, Mariano Pellicano, Holger Nef, Matteo Tebaldi, Yoshinobu Murasato, Alexandra Lansky, Emanuele Barbato, Liefke C van der Heijden, Johan H C Reiber, Niels R Holm, William Wijns, FAVOR Pilot Trial Study Group
Abstract
Objectives: The aim of this prospective multicenter study was to identify the optimal approach for simple and fast fractional flow reserve (FFR) computation from radiographic coronary angiography, called quantitative flow ratio (QFR).
Background: A novel, rapid computation of QFR pullbacks from 3-dimensional quantitative coronary angiography was developed recently.
Methods: QFR was derived from 3 flow models with: 1) fixed empiric hyperemic flow velocity (fixed-flow QFR [fQFR]); 2) modeled hyperemic flow velocity derived from angiography without drug-induced hyperemia (contrast-flow QFR [cQFR]); and 3) measured hyperemic flow velocity derived from angiography during adenosine-induced hyperemia (adenosine-flow QFR [aQFR]). Pressure wire-derived FFR, measured during maximal hyperemia, served as the reference. Separate independent core laboratories analyzed angiographic images and pressure tracings from 8 centers in 7 countries.
Results: The QFR and FFR from 84 vessels in 73 patients with intermediate coronary lesions were compared. Mean angiographic percent diameter stenosis (DS%) was 46.1 ± 8.9%; 27 vessels (32%) had FFR ≤ 0.80. Good agreement with FFR was observed for fQFR, cQFR, and aQFR, with mean differences of 0.003 ± 0.068 (p = 0.66), 0.001 ± 0.059 (p = 0.90), and -0.001 ± 0.065 (p = 0.90), respectively. The overall diagnostic accuracy for identifying an FFR of ≤0.80 was 80% (95% confidence interval [CI]: 71% to 89%), 86% (95% CI: 78% to 93%), and 87% (95% CI: 80% to 94%). The area under the receiver-operating characteristic curve was higher for cQFR than fQFR (difference: 0.04; 95% CI: 0.01 to 0.08; p < 0.01), but did not differ significantly between cQFR and aQFR (difference: 0.01; 95% CI: -0.04 to 0.06; p = 0.65). Compared with DS%, both cQFR and aQFR increased the area under the receiver-operating characteristic curve by 0.20 (p < 0.01) and 0.19 (p < 0.01). The positive likelihood ratio was 4.8, 8.4, and 8.9 for fQFR, cQFR, and aQFR, with negative likelihood ratio of 0.4, 0.3, and 0.2, respectively.
Conclusions: The QFR computation improved the diagnostic accuracy of 3-dimensional quantitative coronary angiography-based identification of stenosis significance. The favorable results of cQFR that does not require pharmacologic hyperemia induction bears the potential of a wider adoption of FFR-based lesion assessment through a reduction in procedure time, risk, and costs.
Keywords: cardiovascular physiology; fractional flow reserve; quantitative coronary angiography.
Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
Source: PubMed