Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law.

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.

Resting distal to aortic pressure ratio and fractional flow reserve discordance affects the diagnostic performance of quantitative flow ratio: Results from an individual patient data meta-analysis.

OBJECTIVE: To evaluate the diagnostic performance of quantitative flow ratio (QFR) related to fractional flow reserve (FFR) and resting distal-to-aortic pressure ratio (resting Pd/Pa) concordance. BACKGROUND: QFR is a method for computation of FFR based on standard coronary angiography. It is unclear how QFR is performed in patients with discordance between FFR and resting pressure ratios (distal-to-aortic pressure ratio [Pd/Pa]). MATERIALS AND METHODS: The main comparison was the diagnostic performance of QFR with FFR as reference stratified by correspondence between FFR and resting Pd/Pa. Secondary outcome measures included distribution of clinical or procedural characteristics stratified by FFR and resting Pd/Pa correspondence. RESULTS: Four prospective studies matched the inclusion criteria. Analysis was performed on patient level data reaching a total of 759 patients and 887 vessels with paired FFR, QFR, and resting Pd/Pa. Median FFR was 0.85 (IQR: 0.77-0.90). Diagnostic accuracy of QFR with FFR as reference was higher if FFR corresponded to resting Pd/Pa: accuracy 90% (95% CI: 88-92) versus 72% (95% CI: 64-80), p < .001, and sAUC 0.95 (95% CI: 0.92-0.96) versus 0.73 (95% CI: 0.69-0.77), p < .001. Resting Pd/Pa and FFR discordance were related to age, sex, hypertension, and lesion severity. CONCLUSION: Diagnostic performance of QFR with FFR as reference is reduced for lesions with discordant FFR (≤0.80) and resting Pd/Pa (≤0.92) measurements.

Can the Wall Shear Stress Values of Left Internal Mammary Artery Grafts during the Perioperative Period Reflect the One-Year Patency?

PURPOSE: The left internal mammary artery (LIMA) is the preferred graft for coronary artery bypass grafting, but the reasoning for LIMA occlusion is unclear. We sought to examine whether the wall shear stress (WSS) values of LIMA grafts during the perioperative period reflected the 1-year patency by using combining computational fluid dynamics (CFD) and coronary computed tomography angiography (CCTA) images. METHODS: CCTA was performed in 233 patients with LIMA graft perioperatively and 1 year later from October 2014 to May 2017. LIMA occlusion was detected in six patients at the 1-year follow-up CCTA. Two patients were excluded due to poor imaging quality. The remaining four patients were enrolled as occlusive (OCC) group, and eight patients with patent LIMA were recruited as patent (PAT) group. The WSS values of LIMA during perioperative period were calculated. LIMA graft was artificially divided into three even segments, proximal (pLIMA), middle (mLIMA) and distal (dLIMA) segments. The independent samples t-test and the Student-Newman-Keuls test were used. RESULTS: The WSS values of dLIMA were significantly higher in the PAT group than in the OCC group (4.43 vs. 2.56, p < 0.05). The WSS values of dLIMA in the PAT group were significantly higher than pLIMA, which was absent in the OCC group. CONCLUSIONS: A higher WSS value of the distal segment of LIMA and a higher WSS value of the distal segment compared with the proximal segment of LIMA in the PAT were observed; this tendency might be helpful in predicting the 1-year patency of LIMA.

Diagnostic performance of quantitative flow ratio in prospectively enrolled patients: An individual patient-data meta-analysis.

OBJECTIVES: We aimed to provide robust performance estimates for quantitative flow ratio (QFR) in assessment of intermediary coronary lesions. BACKGROUND: Angiography-based functional lesion assessment by QFR may appear as a cost saving and safe approach to expand the use of physiology-guided percutaneous coronary interventions. QFR was proven feasible and showed good diagnostic performance in mid-sized off-line and on-line studies with fractional flow reserve (FFR) as reference standard. METHODS: We performed a collaborative individual patient-data meta-analysis of all available prospective studies with paired assessment of QFR and FFR using the CE-marked QFR application. The main outcome was agreement of QFR and FFR using a two-step analysis strategy with a multilevel mixed model accounting for study and center level variation. RESULTS: Of 16 studies identified, four studies had prospective enrollment and provided patient level data reaching a total of 819 patients and 969 vessels with paired FFR and QFR: FAVOR Pilot (n = 73); WIFI II (n = 170); FAVOR II China (n = 304) and FAVOR II Europe-Japan (n = 272). We found an overall agreement (mean difference 0.009 ± 0.068, I2 = 39.6) of QFR with FFR. The diagnostic performance was sensitivity 84% (95%CI: 77-90, I2 = 70.1), specificity 88% (95%CI: 84-91, I2 = 60.1); positive predictive value 80% (95%CI: 76-85, I2 = 33.4), and negative predictive value 95% (95%CI: 93-96, I2 = 75.9). CONCLUSIONS: Diagnostic performance of QFR was good with FFR as reference in this meta-analysis of high quality studies. QFR could provide an easy, safe, and cost-effective solution for functional evaluation of coronary artery stenosis.

Local Flow Patterns After Implantation of Bioresorbable Vascular Scaffold in Coronary Bifurcations　- Novel Findings by Computational Fluid Dynamics.

BACKGROUND: Development of methods for accurate reconstruction of bioresorbable scaffolds (BRS) and assessing local hemodynamics is crucial for investigation of vascular healing after BRS implantation.Methods and Results:Patients with BRS that crossed over in a coronary bifurcation were included for analysis. Reconstructions of the coronary lumen and BRS were performed by fusion of optical coherence tomography and coronary angiography generating a tree model (TM) and a hybrid model with BRS (TM-BRS). A virtual BRS model with thinner struts was created and all 3 models were analyzed using computational fluid dynamics to derive: (1) time-average shear stress (TASS), (2) TASS gradient (TASSG), which represents SS heterogeneity, and (3) fractional flow reserve (FFR). Reconstruction of the BRS was successful in all 10 patients. TASS and TASSG were both higher by TM-BRS than by TM in main vessels (difference 0.27±4.30 Pa and 10.18±27.28 Pa/mm, P<0.001), with a remarkable difference at side branch ostia (difference 13.51±17.40 Pa and 81.65±105.19 Pa/mm, P<0.001). With thinner struts, TASS was lower on the strut surface but higher at the inter-strut zones, whereas TASSG was lower in both regions (P<0.001 for all). Computational FFR was lower by TM-BRS than by TM for both main vessels and side branches (P<0.001). CONCLUSIONS: Neglecting BRS reconstruction leads to significantly lower SS and SS heterogeneity, which is most pronounced at side branch ostia. Thinner struts can marginally reduce SS heterogeneity.

Fast Computational Approaches to Derive Fractional Pressure Ratio in Patients with Extracranial or Intracranial Symptomatic Stenosis.

OBJECTIVE: We aimed to evaluate the performance of fast and straightforward Murray law-based quantitative flow ratio (µQFR) computation in cerebrovascular stenosis. METHODS: A total of 30 patients with symptomatic stenosis of 50%-70% luminal stenosis and underwent fractional pressure ratio (FPR) assessment at our hospital were included in the present study. µQFR was applied to the interrogated vessel. An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of cerebrovascular stenosis. We used invasive FPRs as a reference standard. Pearson's correlation coefficient (r) was used to assess the correlation strength between the µQFR and FPR, and Bland-Altman plots were used to evaluate the agreement between the µQFR and FPR. An analysis of the receiver operating characteristic was used to evaluate the performance of µQFR. RESULTS: Our results displayed a strong positive correlations (r = 0.92; P < 0.001) between the µQFR and pressure wire FPR. Excellent agreement was observed between the µQFR and FPR with a mean difference of 0.01 ± 0.08 (range, -0.16 to 0.14; P = 0.263). The overall accuracy for identifying an FPR of ≤0.7 was 92% (95% confidence interval [CI], 85%-100%). The area under the receiver operating characteristic curve was higher for the µQFR (0.92; 95% CI, 0.81-0.98) than for diameter stenosis (0.88; 95% CI, 0.75-0.95). The positive likelihood ratio was 3.9 for the µQFR with a negative likelihood ratio of 0. CONCLUSIONS: The µQFR computation has a strong correlation and agrees with the FPR calculated from the pressure wire. Therefore, the µQFR might provide an essential therapeutic aid for patients with symptomatic stenosis.

Quantitative Flow Ratio Based on Murray Fractal Law: Accuracy of Single Versus Two Angiographic Views.

Background: Murray bifurcation fractal law-based quantitative flow ratio (QFR), namely, µQFR, is a novel method for the fast computation of fractional flow reserve (FFR) from a single angiographic view. We aimed to compare the diagnostic accuracy of computational QFR based on single vs 2 angiographic views in patients with intermediate coronary stenosis. Methods: The algorithm of µQFR was extended to develop a Murray law-based 3-dimensional (3D) µQFR from 2 angiographic projections. Patients with both angiographic views acquired according to the protocol-specified recommended views in the FAVOR (Functional Diagnostic Accuracy of Quantitative Flow Ratio in Online Assessment of Coronary Stenosis) II China study were included. µQFR was computed separately from the first (µQFR1) and second (µQFR2) angiographic projections, whereas the 3D-µQFR was computed based on both projections, all blinded to FFR data. Hemodynamically significant coronary stenosis was defined by wire-based FFR of ≤0.80. Results: Altogether, 280 vessels from 262 patients had 2 protocol-specified recommended angiographic views; µQFR1, µQFR2, and 3D-µQFR were successfully computed in all these vessels. The mean FFR was 0.82 ± 0.12. The vessel-level diagnostic accuracy for µQFR1, µQFR2, and 3D-µQFR to identify hemodynamically significant stenosis was 92.1% (95% CI, 89.0%-95.3%), 92.5% (95% CI, 89.4%-95.6%), and 93.2% (95% CI, 90.3%-96.2%), respectively, with similar areas under the receiver operating characteristic curve for µQFR1 (0.96, P < .001), µQFR2 (0.95, P < .001), and 3D-µQFR (0.95, P < .001). µQFR1 and µQFR2 had excellent correlation (r = 0.95) and agreement (mean difference = 0.00 ± 0.03). Conclusions: Computation of µQFR from a single angiographic view had comparably good diagnostic performance as 2-view 3D-µQFR in identifying hemodynamically significant coronary stenosis.

Functional Assessment of Cerebral Artery Stenosis by Angiography-Based Quantitative Flow Ratio: A Pilot Study.

BACKGROUND: Increasing attention has been paid to the hemodynamic evaluation of cerebral arterial stenosis. We aimed to demonstrate the performance of angiography-based quantitative flow ratio (QFR) to assess hemodynamic alterations caused by luminal stenoses, using invasive fractional pressure ratios (FPRs) as a reference standard. METHODS: Between March 2013 and December 2019, 29 patients undergoing the pressure gradient measurement of cerebral atherosclerosis were retrospectively enrolled. Wire-based FPR was defined by the arterial pressure distal to the stenotic lesion (Pd) to proximal (Pa) pressure ratios (Pd/Pa). FPR < 0.70 or FPR < 0.75 was assumed as hemodynamically significant stenosis. The new method of computing QFR from a single angiographic view, i.e., the Murray law-based QFR, was applied to the interrogated vessel. An artificial intelligence algorithm was developed to realize the automatic delineation of vascular contour. RESULTS: Fractional pressure ratio and QFR were assessed in 38 vessels from 29 patients. Excellent correlation and agreement were observed between QFR and FPR [r = 0.879, P < 0.001; mean difference (bias): -0.006, 95% limits of agreement: -0.198 to 0.209, respectively). Intra-observer and inter-observer reliability in QFR were excellent (intra-class correlation coefficients, 0.996 and 0.973, respectively). For predicting FPR < 0.70, the area under the receiver-operating characteristic curves (AUC) of QFR was 0.946 (95% CI, 0.820 to 0.993%). The sensitivity and specificity of QFR < 0.70 for identifying FPR < 0.70 was 88.9% (95% CI, 65.3 to 98.6%) and 85.0% (95% CI, 62.1 to 96.8%). For predicting FPR < 0.75, QFR showed similar performance with an AUC equal to 0.926. CONCLUSION: Computational QFR from a single angiographic view achieved comparable results to the wire-based FPR. The excellent diagnostic performance and repeatability empower QFR with high feasibility in the functional assessment of cerebral arterial stenosis.

Reproducibility of quantitative flow ratio: An inter-core laboratory variability study.

BACKGROUND: Quantitative flow ratio (QFR) is a novel approach to derive fractional flow reserve (FFR) from coronary angiography. This study sought to evaluate the reproducibility of QFR when analyzed in independent core laboratories. METHODS: All interrogated vessels in the FAVOR II China Study were separately analyzed using the AngioPlus system (Pulse medical imaging technology, Shanghai) by two independent core laboratories, following the same standard operation procedures. The analysts were blinded to the FFR values and online QFR values. For each interrogated vessel, two identical angiographic image runs were used by two core laboratories for QFR computation. In both core laboratories QFR was successfully obtained in 330 of 332 vessels, in which FFR was available in 328 vessels. Thus, 328 vessels ended in the present statistical analysis. RESULTS: The mean difference in contrast-flow QFR between the two core laboratories was 0.004 ± 0.03 (p = 0.040), which was slightly smaller than that between the online analysis and the two core laboratories (0.01 ± 0.05, p < 0.001 and 0.01 ± 0.05, p = 0.038). The mean difference of QFR with re-spect to FFR were comparable between the two core laboratories (0.002 ± 0.06, p = 0.609, and 0.002 ± 0.06, p = 0.531). Receiver operating characteristic curve analysis showed that diagnostic accuracies of QFR analyzed by the two core laboratories were both excellent (area under the curve: 0.970 vs. 0.963, p = 0.142), when using FFR as the reference standard. CONCLUSIONS: The present study showed good inter-core laboratory reproducibility of QFR in assessing functionally-significant stenosis. It suggests that QFR analyses can be carried out in different core labo-ratories if, and only if, highly standardized conditions are maintained.

Accuracy of 3-dimensional and 2-dimensional quantitative coronary angiography for predicting physiological significance of coronary stenosis: a FAVOR II substudy.

BACKGROUND: Three-dimensional quantitative coronary angiography (3D-QCA) enables reconstruction of a coronary artery in 3D from two angiographic image projections. This study compared the diagnostic accuracy of 3D-QCA vs. 2-dimensional (2D) QCA in predicting physiologically significant coronary stenosis, using fractional flow reserve (FFR) as the reference standard. METHODS: All interrogated vessels in the FAVOR II China study and the FAVOR II Europe-Japan study were assessed by 2D-QCA and 3D-QCA according to standard operating procedures in core laboratories. QCA analysts were blinded to the corresponding FFR values. RESULTS: A total of 645 vessels from 576 patients with 3D-QCA, 2D-QCA, and FFR were analyzed. Using the conventional cut-off value of 50% for percent diameter stenosis (DS%), 3D-QCA was more accurate in predicting FFR ≤0.80 than 2D-QCA [accuracy 74.0% (95% CI: 69.9-77.7%) vs. 64.9% (95% CI: 61.3-68.7%), difference: 9.1%, P<0.001]. Sensitivity was higher by 3D-QCA compared with 2D-QCA [69.1% (95% CI: 63.0-75.1%) vs. 47.1% (95% CI: 40.5-53.6%), difference: 22.0%, P<0.001] and specificity was similar [76.5% (95% CI: 72.5-80.6%) vs. 74.4% (95% CI: 70.2-78.6%), difference: 2.1%, P=0.40]. Area under the receiver operating characteristic curve was significantly higher for 3D-QCA than for 2D-QCA [0.81 (95% CI: 0.77-0.84) vs. 0.66 (95% CI: 0.62-0.71), P<0.001]. CONCLUSIONS: 3D-QCA demonstrated better diagnostic performance in predicting physiologically significant coronary stenosis compared with 2D-QCA, when FFR was used as the reference standard.