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JACC Cardiovasc Interv. 2016 Oct 10;9(19):2024-2035. doi: 10.1016/j.jcin.2016.07.013.

Diagnostic Accuracy of Fast Computational Approaches to Derive Fractional Flow Reserve From Diagnostic Coronary Angiography: The International Multicenter FAVOR Pilot Study.

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Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. Electronic address:
Department of Cardiology, Aarhus University Hospital, Skejby, Denmark.
Department of Cardiology, Guangdong General Hospital, Guangzhou, China.
Department of Cardiology, Thoraxcentrum Twente, Medisch Spectrum Twente, and Health Technology and Services Research, MIRA Institute, University of Twente, Enschede, the Netherlands.
Cardiovascular Research Centre, OLV Hospital, Aalst, Belgium.
Cardiovascular Research Centre, OLV Hospital, Aalst, Belgium; Department of Advanced Biomedical Sciences, University of Naples, Federico II, Naples, Italy.
Department of Cardiology and Angiology, University of Giessen, Giessen, Germany.
Cardiovascular Institute, Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, Italy.
Department of Cardiology, Clinical Research Center, Kyushu Medical Center, Fukuoka, Japan.
Section of Cardiovascular Medicine, Yale University School of Medicine, New Haven, Connecticut.
Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
Cardiovascular Research Centre, OLV Hospital, Aalst, Belgium; The Lambe Institute for Translational Medicine and Curam, National University of Ireland, Galway, and Saolta University Healthcare Group, Galway, Ireland.



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).


A novel, rapid computation of QFR pullbacks from 3-dimensional quantitative coronary angiography was developed recently.


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.


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.


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.


cardiovascular physiology; fractional flow reserve; quantitative coronary angiography

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