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.

The study met its prespecified primary performance goal for the level of diagnostic accuracy of QFR in identifying hemodynamically significant coronary stenosis. (The FAVOR [Functional Diagnostic Accuracy of Quantitative Flow Ratio in Online Assessment of Coronary Stenosis] II China study]; NCT03191708).

Objectives We aimed to evaluate the diagnostic accuracy of computation of fractional flow reserve (FFR) from a single angiographic view in patients with intermediate coronary stenosis. Background Computation of quantitative flow ratio (QFR) from a single angiographic view might increase the feasibility of routine use of computational FFR. In addition, current QFR solutions assume a linear tapering of the reference vessel size, which might decrease the diagnostic accuracy in the presence of the physiologically significant bifurcation lesions. Methods An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of major epicardial coronary arteries including their side branches. A step‐down reference diameter function was reconstructed based on the Murray bifurcation fractal law and used for QFR computation. Validation of this Murray law‐based QFR (μQFR) was performed on the FAVOR II China study population. The μQFR was computed separately in two angiographic projections, starting with the one with optimal angiographic image quality. Hemodynamically significant coronary stenosis was defined by pressure wire‐derived FFR ≤0.80. Results The μQFR was successfully computed in all 330 vessels of 306 patients. There was excellent correlation (r = 0.90, p < .001) and agreement (mean difference = 0.00 ± 0.05, p = .378) between μQFR and FFR. The vessel‐level diagnostic accuracy for μQFR to identify hemodynamically significant stenosis was 93.0% (95% CI: 90.3 to 95.8%), with sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio of 87.5% (95% CI: 80.2 to 92.8%), 96.2% (95% CI: 92.6 to 98.3%), 92.9% (95% CI: 86.5 to 96.9%), 93.1% (95% CI: 88.9 to 96.1%), 23.0 (95% CI: 11.6 to 45.5), 0.13 (95% CI: 0.08 to 0.20), respectively. Use of suboptimal angiographic image view slightly decreased the diagnostic accuracy of μQFR (AUC = 0.97 versus 0.92, difference = 0.05, p < .001). Intra‐ and inter‐observer variability for μQFR computation was 0.00 ± 0.03, and 0.00 ± 0.03, respectively. Average analysis time for μQFR was 67 ± 22 s. Conclusions Computation of μQFR from a single angiographic view has high feasibility and excellent diagnostic accuracy in identifying hemodynamically significant coronary stenosis. The short analysis time and good reproducibility of μQFR bear potential of wider adoption of physiological assessment in the catheterization laboratory.

Computation of FFRQCA is a novel method that allows the assessment of the functional significance of intermediate stenosis. It may emerge as a safe, efficient, and cost-reducing tool for evaluation of coronary stenosis severity during diagnostic angiography.

Aims: A novel method for computation of fractional flow reserve (FFR) from optical coherence tomography (OCT) was developed recently. This study aimed to evaluate the diagnostic accuracy of a new OCTbased FFR (OFR) computational approach, using wire-based FFR as the reference standard.Methods and results: Patients who underwent both OCT and FFR prior to intervention were analysed.The lumen of the interrogated vessel and the ostia of the side branches were automatically delineated and used to compute OFR. Bifurcation fractal laws were applied to correct the change in reference lumen size due to the step-down phenomenon. OFR was compared with FFR, both using a cut-off value of 0.80 to define ischaemia. Computational analysis was performed in 125 vessels from 118 patients. Average FFR was 0.80±0.09. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for OFR to identify FFR ≤0.80 was 90% (95% CI: 84-95), 87% (95% CI: 77-94), 92% (95% CI: 82-97), 92% (95% CI: 82-97), and 88% (95% CI: 77-95), respectively. The AUC was higher for OFR than minimal lumen area (0.93 [95% CI: 0.87-0.97] versus 0.80 [95% CI: 0.72-0.86], p=0.002). Average OFR analysis time was 55±23 seconds for each OCT pullback. Intra-and inter-observer variability in OFR analysis was 0.00±0.02 and 0.00±0.03, respectively. FFR fractional flow reserve IVUS intravascular ultrasound MLA minimal lumen area OCT optical coherence tomography OFR optical coherence tomography-based fractional flow reserve QFR quantitative flow ratio