Junqing Yang

Bio: Junqing Yang is an academic researcher from Guangdong General Hospital. The author has contributed to research in topics: Fractional flow reserve & Coronary artery disease. The author has an hindex of 11, co-authored 24 publications receiving 855 citations. Previous affiliations of Junqing Yang include Academy of Medical Sciences, United Kingdom.

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

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.

Fractional flow reserve calculation from 3-dimensional quantitative coronary angiography and TIMI frame count: a fast computer model to quantify the functional significance of moderately obstructed coronary arteries.

Abstract: Objectives This study sought to present a novel computer model for fast computation of myocardial fractional flow reserve (FFR) and to evaluate it in patients with intermediate coronary stenoses. Background FFR is an indispensable tool to identify individual coronary stenoses causing ischemia. Calculation of FFR from x-ray angiographic data may increase the utility of FFR assessment. Methods Consecutive patients with intermediate coronary stenoses undergoing pressure wire-based FFR measurements were analyzed by a core laboratory. Three-dimensional quantitative coronary angiography (QCA) was performed and the mean volumetric flow rate at hyperemia was calculated using TIMI (Thrombolysis In Myocardial Infarction) frame count combined with 3-dimensional QCA. Computational fluid dynamics was applied subsequently with a novel strategy for the computation of FFR. Diagnostic performance of the computed FFR (FFRQCA) was assessed using wire-based FFR as reference standard. Results Computation of FFRQCA was performed on 77 vessels in 68 patients. Average diameter stenosis was 46.6 ± 7.3%. FFRQCA correlated well with FFR (r = 0.81, p Conclusions 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.

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

Abstract: 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.

Clinical implications of three-vessel fractional flow reserve measurement in patients with coronary artery disease.

Abstract: Aims There are limited data on the clinical implications of total physiologic atherosclerotic burden assessed by invasive physiologic studies in patients with coronary artery disease. We investigated the prognostic implications of total physiologic atherosclerotic burden assessed by total sum of fractional flow reserve (FFR) in three vessels (3V-FFR). Methods and results A total of 1136 patients underwent FFR measurement in three vessels (3V FFR-FRIENDS study, NCT01621438). The patients were classified into high and low 3V-FFR groups according to the median value of 3V-FFR (2.72). The primary endpoint was major adverse cardiac events (MACE, a composite of cardiac death, myocardial infarction and ischaemia-driven revascularization) at 2 years. Mean angiographic percent diameter stenosis and FFR were 43.7 ± 19.3% and 0.90 ± 0.08, respectively. There was a negative correlation between 3V-FFR and estimated 2-year MACE rate (P < 0.001). The patients in low 3V-FFR group showed a higher risk of 2-year MACE than those in the high 3V-FFR group [(7.1% vs. 3.8%, hazard ratio (HR) 2.205, 95% confidence interval (CI) 1.201-4.048, P = 0.011]. The higher 2-year MACE rate was mainly driven by the higher rate of ischaemia-driven revascularization in the low 3V-FFR group (6.2% vs. 2.7%, HR 2.568, 95% CI 1.283-5.140, P = 0.008). In a multivariable adjusted model, low 3V-FFR was an independent predictor of MACE (HR 2.031, 95% CI 1.078-3.830, P = 0.029). Conclusion Patients with high total physiologic atherosclerotic burden assessed by 3V-FFR showed higher risk of 2-year clinical events than those with low total physiologic atherosclerotic burden. The difference was mainly driven by ischaemia-driven revascularization for both functionally significant and insignificant lesions at baseline. Three-vessel FFR might be used as a prognostic indicator in patients with coronary artery disease. Clinical trial registration 3V FFR-FRIENDS study (https://clinicaltrials.gov/ct2/show/NCT01621438, NCT01621438).

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

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.

A machine-learning approach for computation of fractional flow reserve from coronary computed tomography.

Abstract: Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P < 0.001), and no systematic bias was found in Bland-Altman analysis: mean difference was -0.00081 ± 0.0039. Invasive FFR ≤ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P < 0.001). Compared with the physics-based computation, average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR. Average execution time went down from 196.3 ± 78.5 s for the CFD model to ∼2.4 ± 0.44 s for the machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor.

Computational fluid dynamics modelling in cardiovascular medicine

Abstract: This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards 'digital patient' or 'virtual physiological human' representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.