Diagnostic performance of angiography-derived quantitative flow ratio: A systematic review and meta-analysis

Diagnostic performance of angiography-derived quantitative flow ratio: A systematic review and meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Diagnostic performance of angiography-derived quantitative flow ratio: A systematic review and meta-analysis Guo Huang, Mengyun Sui, Wenru Shang, Pu Ge, He Zhu, Sheng Han, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6395472/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Quantitative flow ratio (QFR) is a novel technology to assess the functional significance of coronary stenoses based on standard coronary angiography, which can be alternatives to invasive fractional flow reserve (FFR) assessment. However, the evidence is limited to single-center studies and small sample sizes. This study systematically determined the diagnostic performance of QFR to diagnose functionally significant stenosis with FFR as the reference standard. A systematic review and meta-analysis of studies assessing the diagnostic performance of angiography-derived QFR systems were performed. All relevant studies from 6 literature databases were searched and screened according to the inclusion and exclusion criteria. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−) and diagnostic odds ratio (DOR) along with their 95% confidence intervals (CIs), were calculated using DerSimonian Lair methodology. The summary receiver operating characteristic (SROC) curve and area under the curve were estimated. Meta-regression analysis was performed to identify potential source of heterogeneity. Fifty-seven studies comprising 13,215 patients and 16,125 vessels were included in the final analysis. At the vessel level, the pooled sensitivity and specificity of QFR for detecting a significant coronary stenosis were 0.826 (95% CI: 0.798–0.851) and 0.919 (95% CI: 0.902–0.933). Pooled LR + and LR − were 10.198 (95% CI: 8.469–12.281) and 0.189 (95% CI: 0.163–0.219) with a pooled DOR was 53.968 (95% CI:42.888–67.910). The SROC revealed an area under the curve (AUC) of 0.94 (95% CI: 0.91–0.96). The summary AUCs were 0.90 (95% CI: 0.87–0.92) for fixed-flow QFR (fQFR), 0.95 (95% CI: 0.92–0.96) for contrast-flow QFR (cQFR), 0.97 (95% CI: 0.95–0.98) for Murray law-based QFR (µQFR), and 0.91 (95% CI: 0.89–0.94) for non-specified QFR. The adjusted pooled DORs were as follows: 126.25 for uQFR, 45.49 for cQFR, 26.12 for adenosine-flow QFR (aQFR), 25.88 for fQFR, and 36.54 for non-specified QFR. The accuracy of angiography-derived QFR was strong to assess the functional significance of coronary stenoses with FFR as a reference. uQFR demonstrated the highest diagnostic performance among the five evaluated modes. Health sciences/Cardiology/Cardiac device therapy Health sciences/Cardiology/Interventional cardiology Quantitative flow ratio Fractional flow reserve Angiography Significant stenosis Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Coronary artery disease (CAD), primarily caused by atherosclerosis-induced narrowing or occlusion of the coronary arteries, is one of the most prevalent cardiovascular diseases globally. 1 In fact, CAD is the leading cause of death worldwide, accounting for 17% of all deaths. 2 Accurate assessment of CAD, particularly in cases of intermediate coronary artery stenosis, is crucial for evaluating myocardial ischemia and determining the appropriate next steps in treatment. Coronary angiography (CAG) is the established standard for identifying CAD. However, angiographic images often fall short in determining the functional significance of a stenosis. This limitation can result in unnecessary revascularizations or, conversely, the deferral of necessary interventions. 3 , 4 Pressure-derived Fractional flow reserve (FFR) is widely regarded as the gold standard for evaluating the functional significance of coronary lesions and detecting lesion-specific ischemia, it is strongly endorsed by clinical guidelines as a crucial tool for guiding revascularization decisions. 5 , 6 FFR is defined as the ratio of maximal myocardial blood flow in the presence of an epicardial stenosis to that in a disease-free vessel, reflecting the fraction of normal blood flow supplied by the affected vessel. 7 An FFR value ≤ 0.8 indicates a functionally significant stenosis, where revascularization has been shown to provide better outcomes compared to conservative treatment. 8 FFR is highly sensitive and specific in detecting myocardial ischemia 9 and studies consistently demonstrate that FFR-guided percutaneous coronary intervention (PCI) reduces the number of implanted stents and significantly improves clinical outcomes compared to angiography alone. 10 , 11 However, the clinical adoption of FFR remains limited due to high costs, prolonged procedure time, hyperemia-induced discomfort, and risks such as complications and coronary dissection. 12 Challenges also include difficulty navigating pressure wires, reliance on angiographic assessments, and insufficient reimbursement support. 13 , 14 To enhance access to functional lesion assessment during invasive coronary angiography, quantitative flow ratio (QFR), as a wire-free FFR rapid analysis system was recently developed. 15 This approach utilizes artificial intelligence to reconstruct 3-dimensional vascular models from coronary angiography images and simulate the calculation of FFR indicators based on thrombolysis in myocardial infarction (TIMI) frame counting. 16 Compared to FFR, QFR eliminates the need for invasive physiological measurements, pharmacological hyperemia, and additional cost, meanwhile, it offers a shorter computation time making it a more efficient alternative. 17 , 18 QFR can be derived using four distinct flow models: (1) fixed-flow QFR (fQFR), a fixed empiric hyperemic flow velocity (HFV), based on previous FFR studies; (2) adenosine-flow QFR (aQFR), measured HFV obtained from coronary angiography during adenosine-induced maximal hyperemia; (3) contrast-flow QFR (cQFR), modeled HFV derived from coronary angiography performed without pharmacologically induced hyperemia; 19 (4) Murray law-based QFR (µQFR), enabled automatic contour delineation and swift FFR simulation using a single CAG image from a specific angle. 20 QFR has been extensively investigated and has exhibited robust diagnostic features in European, Asian, and US- populations. 21 Moreover, recent studies have verified good correlation and agreement between QFR and FFR, highlighting its potential clinical utility. 22 , 23 Given the intensive ongoing research in this field and the continuous evolution of these systems, interventionalists require a comprehensive understanding of the diagnostic capabilities of two distinct technologies. However, previous meta-analyses may have been underpowered due to their limited size, and there exists no systematic comparison between various QFR modes and FFR in assessing diagnostic performance. 24 , 25 To address this gap and update information for a better understanding enhance our understanding of QFR computation, we conducted a systematic review and quantitative meta-analysis. Our objective was to update the available information by comparing the fQFR, aQFR, cQFR, and µQFR flow models with invasive FFR in evaluating the functional significance of coronary stenoses. METHODS Data sources and searches PubMed, EMBASE, the Cochrane Library, the China National Knowledge Infrastructure (CNKI), the Wanfang Data Knowledge Service Platform (WANFANG data), and the China Biomedicine Database (Sinomed) were systematically searched up until 10 November, 2024 for published studies in both English and Chinese, using the terms “quantitative flow ratio or QFR” and “fractional flow reserve or FFR”. Additionally, a manual reference check of literature was performed for eligible papers, two independent reviewers examined the references to exclude duplicate or overlapping data. The eligibility of the articles, the data extraction, and quality assessment were independently evaluated by two reviewers, with a third review consulted to resolve any disagreements. Articles containing original material were retrieved and assessed in detail, and the references cited within these publications were further reviewed to identify additional relevant studies. Study selection Inclusion criteria included the following: (1) the diagnostic performance of QFR was assessed using FFR as the standard reference, with the FFR threshold to diagnose coronary stenosis severity set at ≤ 0.80; (2) sufficient data must be provided in the full text to derive the number of true positives (TPs), false negatives (FPs), false positives (FPs), and true negatives (TNs), which construct the 2×2 contingency table. Exclusion criteria included the following: (1) case reports, abstracts, reviews, posters, comments, animal experiments or other non-original articles; (2) duplicate publications or studies with overlapping sample data; and (3) articles not published in Chinese or English. Data extraction and quality assessment The following information from the included studies was extracted: first author, publication year, study design, baseline characteristics of patients and lesions, clinical presentation, cutoffs of QFR and FFR, QFR measurements, and diagnostic performances. If a study compared multiple QFRs, each single QFR was analyzed separately. We prespecified the analyses according five subgroups: (1) fQFR vs. FFR; (2) aQFR vs. FFR; (3) cQFR vs. FFR; (4) µQFR vs. FFR; (5) non-specified QFR vs. FFR. Study quality was assessed by the second version of Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) scale. 26 Discrepancies between reviewers were judged by a third person. This systematic review and meta-analysis were registered in PROPERO (CRD 42023489289). The study was approved by the ISB of the School of Public Health, Fudan University (IRB# 2019- 07-0767). Statistical Analysis The main analysis was performed at the per-vessel level. All variables were presented as number and percentage (%), mean ± standard deviation (SD), or median [interquartile range (IQR)] as appropriate. On the basis of the results from the 2 × 2 tables, the pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−) and diagnostic odds ratio (DOR) along with their 95% confidence intervals (CIs), were calculated using DerSimonian Lair methodology, 27 to assess the diagnostic performance of QFR for diagnosing significant coronary stenosis. The DOR, calculated as LR+ / LR−, reflects the ability of a test to distinguish, in this case, functionally and non-functionally significant lesion. A higher DOR indicates better diagnostic performance of the system. The summary receiver operating characteristic (SROC) curve was also calculated, in which we drew all the points of sensitivity and 1-specificity and adjusted the weighted regression curve using Moses’ Model. 28 The area under the SROC curve (AUC) serves as a global measure of test performance: excellent detection (AUC range of 0.90–1.00), good detection (AUC range of 0.80–0.90), fair detection (AUC range of 0.70–0.80), poor detection (AUC range of 0.60–0.70), and failure (AUC range of 0.50–0.60). 29 Cochran’s Q test and the measured inconsistency (I 2 ) index were calculated to assess potential heterogeneity across studies. Studies with P 50% were defined as significantly heterogeneous. Data with heterogeneity were pooled using a DerSimonian-Laird random-effects model, 27 whereas the Mantel-Haenszel fixed-effects model was adopted if there was no significant heterogeneity. Meta-regression analysis was performed to identify potential source of heterogeneity. A multilevel linear regression model (method = REML, weight = 1/variance of odds) was performed at per-vessel level for exploring the effects of the QFR modes on the pooled DOR while controlling for the study other effects [number of vessels (less or more than 200), year of publication (before or after 2020), country (Asian or others), study design (prospective or retrospective) and research type (single-center or multicenter). Using the above model, the adjusted pooled DORs of QFR modes were calculated and graphed. In addition, Deek’s funnel plot asymmetry test was employed to investigate publication bias and P < 0.05 indicated a significant asymmetry. All p values were two-tailed, with statistical significance set at p < 0.05. Analyses were performed using Review Manager 5.3, Stata/SE 12.0 and SAS 9.4. RESULTS Literature search A total of 765 records were initially detected with used terms. After removal of duplicates and screening by title and abstract, 128 full articles received a complete review. Of those, 57 QFR studies met the inclusion criteria and were used for the qualitative and quantitative meta-analysis (Fig. 1 ). 18 , 20 , 22 , 23 , 30 – 82 Study characteristics A total of 57 studies evaluated QFR, classified as follows: 11 studies on fQFR (2,403 patients, 2,958 vessels), 33 studies on cQFR (6,009 patients, 7,830 vessels), 2 studies on aQFR (90 patients, 99 vessels), 9 studies on uQFR (3,108 patients, 3,378 vessels), and 8 studies on non-specified QFR (1,605 patients, 1,860 vessels). Notably, some studies included more than one QFR mode. 22 , 30 – 34 , 37 , 38 , 73 The details of included studies, such as year of publication, country, study design, research type, standard reference, FFR cutoff, and QFR cutoff, are described in Table 1 . Baseline characteristics of patients, including demographics, clinical symptoms, cardiovascular risk factors, and cardiovascular history, are summarized in the Supplementary material online, Table S1 and Tables S2. The characteristics of target vessels are detailed in the Supplementary material online, Table S3. Individual study estimates of per-vessel diagnostic accuracy of QFR to identify the functional significance of coronary stenoses are presented in the Supplementary material online, Table S4. The accuracy ranged from 61.54–98.3%, sensitivity ranged from 40.00–100.00%, the specificity ranged from 27–100.00%, and the AUC ranged from 0.821 to 0.987. Table 1 Study Characteristics. Characteristics No. Percent (%) Characteristics No. Percent (%) Year of publication Study design 2016 1 1.75 Prospective 20 35.09 2017 3 5.26 Retrospective 37 64.91 2018 8 14.04 Total 57 100.00 2019 4 7.02 Research type 0.00 2020 5 8.77 Single-center 41 71.93 2021 7 12.28 Multicenter 16 28.07 2022 10 17.54 Total 57 100.00 2023 12 21.05 Standard reference 0.00 Total 57 100.00 FFR 57 100.00 Country Total 57 100.00 China 20 35.09 FFR cutoff 0.00 Netherlands 7 12.28 ≤ 0.08 57 100.00 Japan 7 12.28 Total 57 100.00 Poland 4 7.02 QFR cutoff 0.00 Spain 4 7.02 ≤ 0.08 57 100.00 Germany 3 5.26 Total 57 100.00 Italy 3 5.26 Others 9 15.79 Total 57 100.00 Pooled diagnostic performance The pooled diagnostic performance for each QFR modes are summarized in Table 2 . At the vessel level, the pooled sensitivity and specificity of QFR for detecting a significant coronary stenosis were 0.826 (95% CI: 0.798–0.851) and 0.919 (95% CI: 0.902–0.933) (Fig. 2 ), respectively. Pooled LR + and LR– were 10.198 (95% CI: 8.469–12.281) and 0.189 (95% CI: 0.163–0.219) (Supplementary Material online, Figure S1 ) with a pooled DOR was 53.968 (95% CI:42.888–67.910) (Supplementary Material online, Figure S2 ). The SROC revealed an area under the curve (AUC) of 0.94 (95% CI: 0.91–0.96) (Fig. 3 ). Table 2 Meta-analysis of QFR. Mode Pooled sensitivity Pooled specificity Pooled LR་ Pooled LR− Pooled DOR AUC QFR 0.826 (0.798–0.851) 0.919 (0.902–0.933) 10.198 (8.469–12.281) 0.189 (0.163–0.219) 53.968 (42.888–67.910) 0.94 (0.91–0.96) fQFR 0.775 (0.685–0.845) 0.886 (0.817–0.931) 6.797 (4.440–10.407) 0.254 (0.185–0.349) 26.766 (17.645–40.603) 0.90 (0.87–0.92) cQFR 0.854 (0.814–0.887) 0.908 (0.882–0.930) 9.334 (7.310–11.919) 0.160 (0.126–0.204) 58.191 (42.801–79.116) 0.95 (0.92–0.96) uQFR 0.829 (0.775–0.873) 0.967 (0.952–0.977) 25.078 (16.810–37.412) 0.177 (0.132–0.237) 142.051 (76.295–264.480) 0.97 (0.95–0.98) non–specified QFR 0.790 (0.735–0.837) 0.883 (0.855–0.906) 6.745 (5.517–8.247) 0.238 (0.188–0.300) 28.396 (20.795–38.775) 0.91 (0.89–0.94) For individual modes, fQFR exhibited a sensitivity of 0.775 (95% CI: 0.685–0.845) and specificity of 0.886 (95% CI: 0.817–0.931), cQFR had a sensitivity of 0.854 (95% CI: 0.814–0.887) and specificity of 0.908 (95% CI: 0.882–0.930), and uQFR demonstrated a sensitivity of 0.829 (95% CI: 0.775–0.873) and specificity of 0.967 (95% CI: 0.952–0.977). For studies that did not specify the mode of QFR, the sensitivity was 0.790 (95% CI: 0.735–0.837) and the specificity was 0.883 (95% CI: 0.855–0.906) (Table 2 ). However, data on the diagnostic accuracy of aQFR compared with FFR were limited to only two studies. Owing to variable reporting and substantial heterogeneity in results across these studies, a full Meta-analysis was not feasible. Meta-analyses of DORs and AUCs were also conducted for specific QFR modes. The DORs were as follows: 26.766 (95% CI: 17.645–40.603) for fQFR, 58.191 (95% CI: 42.801–79.116) for cQFR, 142.051 (95% CI: 76.295–264.480) for uQFR, and 28.396 (95% CI: 20.795–38.775) for non-specified QFR. The summary AUCs were 0.90 (95% CI: 0.87–0.92) for fQFR, 0.95 (95% CI: 0.92–0.96) for cQFR, 0.97 (95% CI: 0.95–0.98) for uQFR, and 0.91 (95% CI: 0.89–0.94) for non-specified QFR (Table 2 ). Meta-regression analysis The results revealed significant differences in the log values of pooled DORs among the QFR modes. While the log (pooled DOR) for the cQFR mode showed no significant differences compared to other QFR modes, it was notably lower than that of uQFR and higher than that of fQFR (Table 3 ). The adjusted pooled DORs, calculated using the specified modes, were as follows: 126.25 for uQFR, 45.49 for cQFR, 26.12 for aQFR, 25.88 for fQFR, and 36.54 for non-specified QFR. Importantly, all the adjusted pooled DORs were significantly higher than 1 (Fig. 4 ). Table 3 Meta-regression of the log (pooled DOR) of QFR. Variable Estimate (95% CI) Standard error P value Intercept 4.5176 (3.8247, 5.2104) 0.3477 < 0.0001 Mode (control = cQFR) fQFR (1: yes, 0: no) -0.5639 (-1.1147, -0.0131) 0.2764 0.0449 aQFR (1: yes, 0: no) -0.5547 (-2.2753, 1.1660) 0.8634 0.5226 uQFR (1: yes, 0: no) 1.0209 (0.3377, 1.7042) 0.3428 0.0039 non-specified QFR (1: yes, 0: no) -0.2191 (-0.8893, 0.4512) 0.3363 0.5168 No. of vessels (1: ≥200, 0:<200) -0.1979 (-0.6380, 0.2423) 0.2208 0.3732 Publication year after 2020 (1: yes, 0: no) -0.4868 (-0.9944, 0.0208) 0.2547 0.0599 Asian countries (1: yes, 0: no) -0.2181 (-0.6670, 0.2309) 0.2253 0.3362 Design (1: prospective, 0: retrospective) -0.2241 (-0.7402, 0.2919) 0.2589 0.3895 Single-center (1: yes, 0: no) -0.2095 (-0.6769, 0.2579) 0.2345 0.3746 Study quality and publication bias The quality of QFR studies was summarized in the Supplementary material online, Figure S3. Nearly all studies demonstrated a low risk of bias in the index test and reference standard. Nevertheless, 12% (7/57) of the studies had a high risk of bias in patient selection, primarily due to the lack of consecutive inclusion of patients. Additionally, six studies exhibited a high risk of bias in flow and timing, as not all samples were included in the final analysis. No significant publication bias was detected according to Deek’s funnel plot asymmetry test, with a bias coefficient of 1.855 ( P = 0.679). DISCUSSION The diagnostic accuracy of QFR has been extensively investigated, with 57 studies included in this review, encompassing a total of 13,215 patients and 16,125 vessels. Our meta-analysis confirms that QFR demonstrates strong diagnostic performance, with a pooled AUC of 0.94 (95% CI: 0.91–0.96). Additionally, the high pooled sensitivity [0.826 (95% CI: 0.798–0.851)] and specificity [0.919 (95% CI: 0.902–0.933)] further validate the reliability of this novel tool in assessing coronary stenosis. The strong pooled LR+ [10.198 (95% CI 8.469–12.281)] and low pooled LR- [0.189 (95% CI 0.163–0.219)] provide compelling diagnostic evidence of the usefulness of QFR in the clinical setting. Moreover, the high pooled DOR suggests that a positive QFR lesion is 54 times more likely to correspond to a functionally significant lesion (measured FFR ≤ 0.80) compared to a non-functionally significant lesion. QFR stands as a pioneering wire- and adenosine-free FFR rapid analysis system, offering a multitude of advantages: (1) non-invasive procedure: by leveraging angiographic images, QFR eliminates the necessity for invasive procedures, thereby minimizing patient risk and discomfort. (2) accuracy and reliability: QFR harness advanced artificial intelligence algorithms to enhance the precision and automation of image processing and blood flow simulation. 83 This reduces the influence of human error and subjective judgment, ultimately improving the accuracy and reliability of computational hemodynamic assessments. (3) clinical effectiveness: QFR has undergone rigorous validation in numerous clinical trials, demonstrating its consistency and correlation with traditional wire-based methods. Furthermore, its guidance during PCI treatment has facilitated better patient selection, leading to improved patient outcomes. 65 , 84 With a positive primary endpoint in FAVOR III China, demonstrating increasing benefits up to 2 years, 85 alongside procedural advantages such as lower costs compared to FFR, the path is paved for broader adoption of functional lesion evaluation. (4) rapid computational analysis: 18 , 42 the technology's capacity to swiftly process static images streamlines the diagnostic workflow, significantly reducing the time required in clinical practice. These attributes position QFR as a superior alternative in the realm of functional lesion evaluation. More specifically, the summary AUCs for fQFR, cQFR, uQFR and non-specified QFR were 0.90 (95%CI 0.87–0.92), 0.95 (95%CI 0.92–0.96), 0.97 (95%CI 0.95–0.98), and 0.91 (95%CI 0.89–0.94), respectively. These findings underscore the strong diagnostic performance of QFR across its various modes. Our meta-regression revealed that the log value of the pooled DOR for cQFR was nearly double that of fQFR. Similar outcomes were presented in smaller prospective observational studies by Tu et al., 30 van Rosendael et al., 31 and Echavarría-Pinto et al., 37 supporting the robustness of these observations. They demonstrated that the QFR computation, based on a patient-specific contrast-flow model derived from coronary angiography without pharmacologic hyperemia induction, achieved superior diagnostic accuracy compared to the fixed-flow approach. The cQFR employs frame count analysis from regular (non-hyperaemic) angiographic projections to simulate hyperemic flow velocity. Conversely, the fQFR applies a fixed empiric hyperemic flow velocity derived from prior FFR studies, thereby eliminating the requirement for TIMI frame counting and disregarding the impact of coronary microvasculature circulation. Consequently, the diagnostic accuracy of fQFR was found to be inferior to that of cQFR in the present study. This study further revealed that µQFR exhibited superior diagnostic accuracy through the utilization of QFR computations based on a single angiographic view, thereby eliminating the necessity for 3D angiographic reconstruction. Several factors may account for this outcome. Firstly, the µQFR computation precisely delineated the lumen contours of side branches and reconstructed a step-down reference diameter function for the hypothetical healthy vessel, as opposed to assuming a linear tapering of the reference vessel size. This methodology captured the inherent fractal physiology of bifurcations, facilitating a more accurate quantification of lesion severity, which is a pivotal determinant of FFR. Secondly, the µQFR computation leveraged angiographic views with optimal image quality. Adequate exposure of the stenotic segment enhanced the precision in quantifying the geometry of the vessel under investigation. Notably, µQFR based on a single angiographic view was reported to possess technical advantages, including easier operation, shorter analysis time, and enhanced reproducibility. 18 , 20 , 86 Limitations Several limitations of our study merit consideration. Firstly, despite substantial evidence supporting the diagnostic accuracy of QFR assessment, the impact of specific patient or lesion characteristics (such as a history of recent myocardial infarction or coronary artery bypass grafting, multi-vessel disease, or the quality of angiographic images) on the diagnostic accuracy or clinical effectiveness of QFR remains largely unknown. Notably, the quality of angiographic material is of foremost importance for accurate QFR computation. Hence, further research is warranted to investigate parameters for automatically assessing the quality of angiographic acquisitions intended for QFR analysis. Secondly, the absence of individual patient-level data hindered deeper analysis to identify predictors of QFR accuracy. Lastly, despite the negative meta-regression of population characteristics, significant heterogeneity remained in our study. Consequently, well-designed, large-scale, multicenter prospective clinical trials are essential to better understand the role of FFR and QFR-guided assessments in complex clinical scenarios. Conclusions In conclusion, our analysis confirms the impressive diagnostic performance of QFR in detecting functional ischemia of coronary arteries, with pressure-wire measured FFR serving as the reference standard. Notably, uQFR demonstrated the highest diagnostic performance among the evaluated modes. QFR might be considered a reliable and useful alternative to pressure wire-based FFR due to its simplicity and non-invasive modality. However, this superiority should be interpreted with caution, given the observed heterogeneity, the lack of automated quality assessment for angiographic acquisitions, and the complexities inherent in clinical practice. Therefore, further randomized trials are warranted to unveil the value of a QFR-based strategy in patients requiring functional evaluation. Declarations RESOURCE AVAILABILITY Data availability Data provided in manuscript or supplementary information file. CLINICAL TRIAL NUMBER Clinical trial number: not applicable. FUNDING DECLARATION This study was funded by the National Natural Science Foundation of China (grant number 82273899). AUTHOR CONTRIBUTIONS G.H., M.S., W.S., P.G. and H.Z. participated in the design, data search, data analysis, and manuscript writing. S.H., and L.S. participated in the data supervising and manuscript review. DECLARATION OF INTERESTS The authors declare no competing interests. Reporting Checklist The authors have completed the PRISMA reporting checklist. Available at References Tsao, C. W. et al. Heart disease and stroke statistics-2022 update: a report from the American Heart Association. Circulation 145 , e153–e639. https://10.1161/CIR.0000000000001052 (2022). Tonino, P. A. et al. Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl. J. 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Accuracy of second generation quantitative flow ratio in diagnosing coronary stenosis South China . J. Cardiovasc. Dis. 29 , 19–25. https://10.3969/j.issn.1007-9688.2023.01.04 (2023). Zuo, W. et al. Sex differences in Murray law-based quantitative flow ratio among patients with intermediate coronary lesions. J. Am. Heart Assoc. 12 , e029330. https://10.1161/JAHA.123.029330 (2023). Zuo, W. et al. Impact of calcification on Murray law-based quantitative flow ratio for physiological assessment of intermediate coronary stenoses. Cardiol. J. 31 , 205–214. https://10.5603/CJ.a2023.0045 (2024). Yang, J. et al. The effects of cardiac structure, valvular regurgitation, and left ventricular diastolic dysfunction on the diagnostic accuracy of Murray law-based quantitative flow ratio. Front. Cardiovasc. Med. 10 , 1134623. https://10.3389/fcvm.2023.1134623 (2023). Lai, D., Huang, Y., Chen, M., Lai, Z. & Wei, C. Accuracy of Murray law based quantitative flow ratio in diagnosing patients with cardiac structural abnormalities and left ventricular diastolic dysfunction. South. China J. Cardiovasc. Dis. 30 , 368–374379. https://10.3969/j.issn.1007-9688.2024.04.05 (2024). Yuta, F. et al. Diagnostic accuracy of Murray law-based quantitative flow ratio in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement. Heart Vessels . 39 , 735–745. https://10.1007/s00380-024-02387-5 (2024). Xi, Y., Huang, M., Huang, Y., Qiu, Q. & Tan, W. Quantitative flow ratio in the evaluation of myocardial ischemia in patients with left ventricular diastolic dysfunction. South. China J. Cardiovasc. Dis. 27 , 80–84. https://10.3969/j.issn.1007-9688.2021.01.18 (2021). Gan, P. et al. Evaluation value of quantitative flow ratio on coronary hemodynamics. South. China J. Cardiovasc. Dis. 27 , 243–247253. https://10.3969/j.issn.1007-9688.2021.03.01 (2021). Peper, J. et al. Diagnostic performance and clinical implications for enhancing a hybrid quantitative flow ratio-FFR revascularization decision-making strategy. Sci. Rep. 11 , 6425. https://10.1038/s41598-021-85933-9 (2021). Zhang, J. et al. Angiographic lesion morphology provides incremental value to generalize quantitative flow ratio for predicting myocardial ischemia. Front. Cardiovasc. Med. 9 , 872498. https://10.3389/fcvm.2022.872498 (2022). Liu, X. & Gao, C. Research on influencing factors of diagnostic mismatch between quantitative flow ratio and fractional flow reserve Chinese . J. Cardiovasc. Res. 21 , 397–402. https://10.3969/j.issn.1672-5301.2023.05.003 (2023). Dong, T. et al. Exploring the predictors of the discrepancy between quantitative flow ratio and fractional flow reserve measurements. Anatol. J. Cardiol. 27 , 390–397. https://10.14744/AnatolJCardiol.2023.2622 (2023). Yuasa, S. et al. Angiography-derived functional assessment of left main coronary stenoses. Catheter Cardiovasc. Interv . 101 , 1045–1052. https://10.1002/ccd.30633 (2023). Zhang, R. et al. Diagnostic concordance and influencing factors of quantitative flow fraction and fractional flow reserve. Chin J Intervent Cardiol. ;32:481–488. (2024). https://10.3969/j. issn. 1004–8812. 2024. 09. 001. Han, W. et al. Diagnostic performance of the quantitative flow ratio and CT-FFR for coronary lesion-specific ischemia. Sci. Rep. 14 , 16969. https://10.1038/s41598-024-68212-1 (2024). Song, L. et al. Quantitative flow ratio-guided strategy versus angiography-guided strategy for percutaneous coronary intervention: Rationale and design of the FAVOR III China trial. Am. Heart J. 223 , 72–80. https://10.1016/j.ahj.2020.02.015 (2020). Biscaglia, S. et al. QFR-based virtual PCI or conventional angiography to guide PCI: The AQVA Trial. JACC Cardiovasc. Interv . 16 , 783–794. https://10.1016/j.jcin.2022.10.054 (2023). Song, L. et al. 2-Year Outcomes of Angiographic Quantitative Flow Ratio-Guided Coronary Interventions. J. Am. Coll. Cardiol. 80 , 2089–2101. https://10.1016/j.jacc.2022.09.007 (2022). Huang, Y. et al. Morphometric assessment for functional evaluation of coronary stenosis with optical coherence tomography and the optical flow ratio in a vessel with single stenosis. J. Clin. Med. 11 , 5198. https://10.3390/jcm11175198 (2022). Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx PRISMA2020Checklist.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6395472","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463761354,"identity":"8ab51d77-9764-478d-8eb7-f2a565739ac3","order_by":0,"name":"Guo 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14:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6395472/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6395472/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83814593,"identity":"9ecf2a10-b8ff-4dbc-b936-f85bd11d44b2","added_by":"auto","created_at":"2025-06-03 07:29:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86901,"visible":true,"origin":"","legend":"\u003cp\u003eLiterature search of eligible studies.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6395472/v1/939f13e46a9823692cd76aca.jpg"},{"id":83813308,"identity":"18e0d5de-00d5-429a-af2f-236bcb2c64b9","added_by":"auto","created_at":"2025-06-03 07:21:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":194253,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots for sensitivity and specificity of QFR.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6395472/v1/d3613bba9140a5e623e3f5d4.jpg"},{"id":83813306,"identity":"bf6a3142-942d-4103-921a-217b362181d0","added_by":"auto","created_at":"2025-06-03 07:21:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33359,"visible":true,"origin":"","legend":"\u003cp\u003eSummary receiver operating characteristic curve for QFR.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6395472/v1/1483ad053d40a5ee0c316df6.jpg"},{"id":83813312,"identity":"c258c451-48ff-4341-a1b0-29b24472373b","added_by":"auto","created_at":"2025-06-03 07:21:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":111320,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted pooled diagnostic odds ratio of QFR.\u003c/p\u003e\n\u003cp\u003e“***” indicate that the adjusted pooled DOR for the mode was significantly higher than 1, with \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6395472/v1/1e0cae4490317885d472a700.jpg"},{"id":96055896,"identity":"14dcedae-4071-4cbc-86f5-b8188d7b5c55","added_by":"auto","created_at":"2025-11-17 07:40:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1371224,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395472/v1/41b11bf1-0f0b-4281-91ba-5de6e00168bb.pdf"},{"id":83813310,"identity":"83e26bdc-a2d8-4cd0-9678-e58a00df7e9f","added_by":"auto","created_at":"2025-06-03 07:21:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":183531,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6395472/v1/2022d46618ec4cff60c5cf63.docx"},{"id":83814594,"identity":"a4035f77-e4e0-4b2b-97f2-bb92a1a1efb5","added_by":"auto","created_at":"2025-06-03 07:29:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":347932,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMA2020Checklist.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395472/v1/d8f2d8e7412a16d9de6f68a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic performance of angiography-derived quantitative flow ratio: A systematic review and meta-analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCoronary artery disease (CAD), primarily caused by atherosclerosis-induced narrowing or occlusion of the coronary arteries, is one of the most prevalent cardiovascular diseases globally.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In fact, CAD is the leading cause of death worldwide, accounting for 17% of all deaths.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Accurate assessment of CAD, particularly in cases of intermediate coronary artery stenosis, is crucial for evaluating myocardial ischemia and determining the appropriate next steps in treatment. Coronary angiography (CAG) is the established standard for identifying CAD. However, angiographic images often fall short in determining the functional significance of a stenosis. This limitation can result in unnecessary revascularizations or, conversely, the deferral of necessary interventions.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePressure-derived Fractional flow reserve (FFR) is widely regarded as the gold standard for evaluating the functional significance of coronary lesions and detecting lesion-specific ischemia, it is strongly endorsed by clinical guidelines as a crucial tool for guiding revascularization decisions.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e FFR is defined as the ratio of maximal myocardial blood flow in the presence of an epicardial stenosis to that in a disease-free vessel, reflecting the fraction of normal blood flow supplied by the affected vessel.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e An FFR value\u0026thinsp;\u0026le;\u0026thinsp;0.8 indicates a functionally significant stenosis, where revascularization has been shown to provide better outcomes compared to conservative treatment.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e FFR is highly sensitive and specific in detecting myocardial ischemia\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and studies consistently demonstrate that FFR-guided percutaneous coronary intervention (PCI) reduces the number of implanted stents and significantly improves clinical outcomes compared to angiography alone.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e However, the clinical adoption of FFR remains limited due to high costs, prolonged procedure time, hyperemia-induced discomfort, and risks such as complications and coronary dissection.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Challenges also include difficulty navigating pressure wires, reliance on angiographic assessments, and insufficient reimbursement support.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo enhance access to functional lesion assessment during invasive coronary angiography, quantitative flow ratio (QFR), as a wire-free FFR rapid analysis system was recently developed.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e This approach utilizes artificial intelligence to reconstruct 3-dimensional vascular models from coronary angiography images and simulate the calculation of FFR indicators based on thrombolysis in myocardial infarction (TIMI) frame counting.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Compared to FFR, QFR eliminates the need for invasive physiological measurements, pharmacological hyperemia, and additional cost, meanwhile, it offers a shorter computation time making it a more efficient alternative.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e QFR can be derived using four distinct flow models: (1) fixed-flow QFR (fQFR), a fixed empiric hyperemic flow velocity (HFV), based on previous FFR studies; (2) adenosine-flow QFR (aQFR), measured HFV obtained from coronary angiography during adenosine-induced maximal hyperemia; (3) contrast-flow QFR (cQFR), modeled HFV derived from coronary angiography performed without pharmacologically induced hyperemia;\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e (4) Murray law-based QFR (\u0026micro;QFR), enabled automatic contour delineation and swift FFR simulation using a single CAG image from a specific angle.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e QFR has been extensively investigated and has exhibited robust diagnostic features in European, Asian, and US- populations.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Moreover, recent studies have verified good correlation and agreement between QFR and FFR, highlighting its potential clinical utility.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGiven the intensive ongoing research in this field and the continuous evolution of these systems, interventionalists require a comprehensive understanding of the diagnostic capabilities of two distinct technologies. However, previous meta-analyses may have been underpowered due to their limited size, and there exists no systematic comparison between various QFR modes and FFR in assessing diagnostic performance.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e To address this gap and update information for a better understanding enhance our understanding of QFR computation, we conducted a systematic review and quantitative meta-analysis. Our objective was to update the available information by comparing the fQFR, aQFR, cQFR, and \u0026micro;QFR flow models with invasive FFR in evaluating the functional significance of coronary stenoses.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources and searches\u003c/h2\u003e \u003cp\u003ePubMed, EMBASE, the Cochrane Library, the China National Knowledge Infrastructure (CNKI), the Wanfang Data Knowledge Service Platform (WANFANG data), and the China Biomedicine Database (Sinomed) were systematically searched up until 10 November, 2024 for published studies in both English and Chinese, using the terms \u0026ldquo;quantitative flow ratio or QFR\u0026rdquo; and \u0026ldquo;fractional flow reserve or FFR\u0026rdquo;.\u003c/p\u003e \u003cp\u003eAdditionally, a manual reference check of literature was performed for eligible papers, two independent reviewers examined the references to exclude duplicate or overlapping data. The eligibility of the articles, the data extraction, and quality assessment were independently evaluated by two reviewers, with a third review consulted to resolve any disagreements. Articles containing original material were retrieved and assessed in detail, and the references cited within these publications were further reviewed to identify additional relevant studies.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy selection\u003c/h3\u003e\n\u003cp\u003eInclusion criteria included the following: (1) the diagnostic performance of QFR was assessed using FFR as the standard reference, with the FFR threshold to diagnose coronary stenosis severity set at \u0026le;\u0026thinsp;0.80; (2) sufficient data must be provided in the full text to derive the number of true positives (TPs), false negatives (FPs), false positives (FPs), and true negatives (TNs), which construct the 2\u0026times;2 contingency table. Exclusion criteria included the following: (1) case reports, abstracts, reviews, posters, comments, animal experiments or other non-original articles; (2) duplicate publications or studies with overlapping sample data; and (3) articles not published in Chinese or English.\u003c/p\u003e\n\u003ch3\u003eData extraction and quality assessment\u003c/h3\u003e\n\u003cp\u003eThe following information from the included studies was extracted: first author, publication year, study design, baseline characteristics of patients and lesions, clinical presentation, cutoffs of QFR and FFR, QFR measurements, and diagnostic performances. If a study compared multiple QFRs, each single QFR was analyzed separately. We prespecified the analyses according five subgroups: (1) fQFR vs. FFR; (2) aQFR vs. FFR; (3) cQFR vs. FFR; (4) \u0026micro;QFR vs. FFR; (5) non-specified QFR vs. FFR.\u003c/p\u003e \u003cp\u003eStudy quality was assessed by the second version of Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) scale.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Discrepancies between reviewers were judged by a third person. This systematic review and meta-analysis were registered in PROPERO (CRD 42023489289). The study was approved by the ISB of the School of Public Health, Fudan University (IRB# 2019- 07-0767).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe main analysis was performed at the per-vessel level. All variables were presented as number and percentage (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), or median [interquartile range (IQR)] as appropriate. On the basis of the results from the 2 \u0026times; 2 tables, the pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR\u0026minus;) and diagnostic odds ratio (DOR) along with their 95% confidence intervals (CIs), were calculated using DerSimonian Lair methodology,\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e to assess the diagnostic performance of QFR for diagnosing significant coronary stenosis. The DOR, calculated as LR+ / LR\u0026minus;, reflects the ability of a test to distinguish, in this case, functionally and non-functionally significant lesion. A higher DOR indicates better diagnostic performance of the system. The summary receiver operating characteristic (SROC) curve was also calculated, in which we drew all the points of sensitivity and 1-specificity and adjusted the weighted regression curve using Moses\u0026rsquo; Model.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e The area under the SROC curve (AUC) serves as a global measure of test performance: excellent detection (AUC range of 0.90\u0026ndash;1.00), good detection (AUC range of 0.80\u0026ndash;0.90), fair detection (AUC range of 0.70\u0026ndash;0.80), poor detection (AUC range of 0.60\u0026ndash;0.70), and failure (AUC range of 0.50\u0026ndash;0.60).\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCochran\u0026rsquo;s Q test and the measured inconsistency (I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) index were calculated to assess potential heterogeneity across studies. Studies with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50% were defined as significantly heterogeneous. Data with heterogeneity were pooled using a DerSimonian-Laird random-effects model,\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e whereas the Mantel-Haenszel fixed-effects model was adopted if there was no significant heterogeneity. Meta-regression analysis was performed to identify potential source of heterogeneity. A multilevel linear regression model (method\u0026thinsp;=\u0026thinsp;REML, weight\u0026thinsp;=\u0026thinsp;1/variance of odds) was performed at per-vessel level for exploring the effects of the QFR modes on the pooled DOR while controlling for the study other effects [number of vessels (less or more than 200), year of publication (before or after 2020), country (Asian or others), study design (prospective or retrospective) and research type (single-center or multicenter). Using the above model, the adjusted pooled DORs of QFR modes were calculated and graphed.\u003c/p\u003e \u003cp\u003eIn addition, Deek\u0026rsquo;s funnel plot asymmetry test was employed to investigate publication bias and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a significant asymmetry. All \u003cem\u003ep\u003c/em\u003e values were two-tailed, with statistical significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Analyses were performed using Review Manager 5.3, Stata/SE 12.0 and SAS 9.4.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLiterature search\u003c/h2\u003e \u003cp\u003eA total of 765 records were initially detected with used terms. After removal of duplicates and screening by title and abstract, 128 full articles received a complete review. Of those, 57 QFR studies met the inclusion criteria and were used for the qualitative and quantitative meta-analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62 CR63 CR64 CR65 CR66 CR67 CR68 CR69 CR70 CR71 CR72 CR73 CR74 CR75 CR76 CR77 CR78 CR79 CR80 CR81\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy characteristics\u003c/h3\u003e\n\u003cp\u003eA total of 57 studies evaluated QFR, classified as follows: 11 studies on fQFR (2,403 patients, 2,958 vessels), 33 studies on cQFR (6,009 patients, 7,830 vessels), 2 studies on aQFR (90 patients, 99 vessels), 9 studies on uQFR (3,108 patients, 3,378 vessels), and 8 studies on non-specified QFR (1,605 patients, 1,860 vessels). Notably, some studies included more than one QFR mode.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe details of included studies, such as year of publication, country, study design, research type, standard reference, FFR cutoff, and QFR cutoff, are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Baseline characteristics of patients, including demographics, clinical symptoms, cardiovascular risk factors, and cardiovascular history, are summarized in the Supplementary material online, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Tables S2. The characteristics of target vessels are detailed in the Supplementary material online, Table S3. Individual study estimates of per-vessel diagnostic accuracy of QFR to identify the functional significance of coronary stenoses are presented in the Supplementary material online, Table S4. The accuracy ranged from 61.54\u0026ndash;98.3%, sensitivity ranged from 40.00\u0026ndash;100.00%, the specificity ranged from 27\u0026ndash;100.00%, and the AUC ranged from 0.821 to 0.987.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy Characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYear of publication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eProspective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eResearch type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSingle-center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMulticenter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eStandard reference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFFR cutoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eQFR cutoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePooled diagnostic performance\u003c/h3\u003e\n\u003cp\u003eThe pooled diagnostic performance for each QFR modes are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. At the vessel level, the pooled sensitivity and specificity of QFR for detecting a significant coronary stenosis were 0.826 (95% CI: 0.798\u0026ndash;0.851) and 0.919 (95% CI: 0.902\u0026ndash;0.933) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), respectively. Pooled LR\u0026thinsp;+\u0026thinsp;and LR\u0026ndash; were 10.198 (95% CI: 8.469\u0026ndash;12.281) and 0.189 (95% CI: 0.163\u0026ndash;0.219) (Supplementary Material online, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) with a pooled DOR was 53.968 (95% CI:42.888\u0026ndash;67.910) (Supplementary Material online, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The SROC revealed an area under the curve (AUC) of 0.94 (95% CI: 0.91\u0026ndash;0.96) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeta-analysis of QFR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePooled sensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePooled specificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePooled LR་\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePooled LR\u0026minus;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePooled DOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.826 (0.798\u0026ndash;0.851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.919 (0.902\u0026ndash;0.933)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.198 (8.469\u0026ndash;12.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.189 (0.163\u0026ndash;0.219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.968 (42.888\u0026ndash;67.910)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94 (0.91\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efQFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.775 (0.685\u0026ndash;0.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.886 (0.817\u0026ndash;0.931)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.797 (4.440\u0026ndash;10.407)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254 (0.185\u0026ndash;0.349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.766 (17.645\u0026ndash;40.603)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90 (0.87\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecQFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.854 (0.814\u0026ndash;0.887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.908 (0.882\u0026ndash;0.930)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.334 (7.310\u0026ndash;11.919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.160 (0.126\u0026ndash;0.204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.191 (42.801\u0026ndash;79.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95 (0.92\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euQFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.829 (0.775\u0026ndash;0.873)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.967 (0.952\u0026ndash;0.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.078 (16.810\u0026ndash;37.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177 (0.132\u0026ndash;0.237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e142.051 (76.295\u0026ndash;264.480)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97 (0.95\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon\u0026ndash;specified QFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.790 (0.735\u0026ndash;0.837)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883 (0.855\u0026ndash;0.906)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.745 (5.517\u0026ndash;8.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.238 (0.188\u0026ndash;0.300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.396 (20.795\u0026ndash;38.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.91 (0.89\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor individual modes, fQFR exhibited a sensitivity of 0.775 (95% CI: 0.685\u0026ndash;0.845) and specificity of 0.886 (95% CI: 0.817\u0026ndash;0.931), cQFR had a sensitivity of 0.854 (95% CI: 0.814\u0026ndash;0.887) and specificity of 0.908 (95% CI: 0.882\u0026ndash;0.930), and uQFR demonstrated a sensitivity of 0.829 (95% CI: 0.775\u0026ndash;0.873) and specificity of 0.967 (95% CI: 0.952\u0026ndash;0.977). For studies that did not specify the mode of QFR, the sensitivity was 0.790 (95% CI: 0.735\u0026ndash;0.837) and the specificity was 0.883 (95% CI: 0.855\u0026ndash;0.906) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, data on the diagnostic accuracy of aQFR compared with FFR were limited to only two studies. Owing to variable reporting and substantial heterogeneity in results across these studies, a full Meta-analysis was not feasible. Meta-analyses of DORs and AUCs were also conducted for specific QFR modes. The DORs were as follows: 26.766 (95% CI: 17.645\u0026ndash;40.603) for fQFR, 58.191 (95% CI: 42.801\u0026ndash;79.116) for cQFR, 142.051 (95% CI: 76.295\u0026ndash;264.480) for uQFR, and 28.396 (95% CI: 20.795\u0026ndash;38.775) for non-specified QFR. The summary AUCs were 0.90 (95% CI: 0.87\u0026ndash;0.92) for fQFR, 0.95 (95% CI: 0.92\u0026ndash;0.96) for cQFR, 0.97 (95% CI: 0.95\u0026ndash;0.98) for uQFR, and 0.91 (95% CI: 0.89\u0026ndash;0.94) for non-specified QFR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeta-regression analysis\u003c/h2\u003e \u003cp\u003eThe results revealed significant differences in the log values of pooled DORs among the QFR modes. While the log (pooled DOR) for the cQFR mode showed no significant differences compared to other QFR modes, it was notably lower than that of uQFR and higher than that of fQFR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The adjusted pooled DORs, calculated using the specified modes, were as follows: 126.25 for uQFR, 45.49 for cQFR, 26.12 for aQFR, 25.88 for fQFR, and 36.54 for non-specified QFR. Importantly, all the adjusted pooled DORs were significantly higher than 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeta-regression of the log (pooled DOR) of QFR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5176 (3.8247, 5.2104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMode (control\u0026thinsp;=\u0026thinsp;cQFR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efQFR (1: yes, 0: no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5639 (-1.1147, -0.0131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaQFR (1: yes, 0: no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5547 (-2.2753, 1.1660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003euQFR (1: yes, 0: no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0209 (0.3377, 1.7042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enon-specified QFR (1: yes, 0: no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2191 (-0.8893, 0.4512)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo. of vessels (1: \u0026ge;200, 0:\u0026lt;200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1979 (-0.6380, 0.2423)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePublication year after 2020 (1: yes, 0: no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.4868 (-0.9944, 0.0208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAsian countries (1: yes, 0: no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2181 (-0.6670, 0.2309)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDesign (1: prospective, 0: retrospective)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2241 (-0.7402, 0.2919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSingle-center (1: yes, 0: no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2095 (-0.6769, 0.2579)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy quality and publication bias\u003c/h2\u003e \u003cp\u003eThe quality of QFR studies was summarized in the Supplementary material online, Figure S3. Nearly all studies demonstrated a low risk of bias in the index test and reference standard. Nevertheless, 12% (7/57) of the studies had a high risk of bias in patient selection, primarily due to the lack of consecutive inclusion of patients. Additionally, six studies exhibited a high risk of bias in flow and timing, as not all samples were included in the final analysis. No significant publication bias was detected according to Deek\u0026rsquo;s funnel plot asymmetry test, with a bias coefficient of 1.855 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.679).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe diagnostic accuracy of QFR has been extensively investigated, with 57 studies included in this review, encompassing a total of 13,215 patients and 16,125 vessels. Our meta-analysis confirms that QFR demonstrates strong diagnostic performance, with a pooled AUC of 0.94 (95% CI: 0.91\u0026ndash;0.96). Additionally, the high pooled sensitivity [0.826 (95% CI: 0.798\u0026ndash;0.851)] and specificity [0.919 (95% CI: 0.902\u0026ndash;0.933)] further validate the reliability of this novel tool in assessing coronary stenosis. The strong pooled LR+ [10.198 (95% CI 8.469\u0026ndash;12.281)] and low pooled LR- [0.189 (95% CI 0.163\u0026ndash;0.219)] provide compelling diagnostic evidence of the usefulness of QFR in the clinical setting. Moreover, the high pooled DOR suggests that a positive QFR lesion is 54 times more likely to correspond to a functionally significant lesion (measured FFR\u0026thinsp;\u0026le;\u0026thinsp;0.80) compared to a non-functionally significant lesion.\u003c/p\u003e \u003cp\u003eQFR stands as a pioneering wire- and adenosine-free FFR rapid analysis system, offering a multitude of advantages: (1) non-invasive procedure: by leveraging angiographic images, QFR eliminates the necessity for invasive procedures, thereby minimizing patient risk and discomfort. (2) accuracy and reliability: QFR harness advanced artificial intelligence algorithms to enhance the precision and automation of image processing and blood flow simulation.\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e This reduces the influence of human error and subjective judgment, ultimately improving the accuracy and reliability of computational hemodynamic assessments. (3) clinical effectiveness: QFR has undergone rigorous validation in numerous clinical trials, demonstrating its consistency and correlation with traditional wire-based methods. Furthermore, its guidance during PCI treatment has facilitated better patient selection, leading to improved patient outcomes.\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e With a positive primary endpoint in FAVOR III China, demonstrating increasing benefits up to 2 years,\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e alongside procedural advantages such as lower costs compared to FFR, the path is paved for broader adoption of functional lesion evaluation. (4) rapid computational analysis:\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e the technology's capacity to swiftly process static images streamlines the diagnostic workflow, significantly reducing the time required in clinical practice. These attributes position QFR as a superior alternative in the realm of functional lesion evaluation.\u003c/p\u003e \u003cp\u003eMore specifically, the summary AUCs for fQFR, cQFR, uQFR and non-specified QFR were 0.90 (95%CI 0.87\u0026ndash;0.92), 0.95 (95%CI 0.92\u0026ndash;0.96), 0.97 (95%CI 0.95\u0026ndash;0.98), and 0.91 (95%CI 0.89\u0026ndash;0.94), respectively. These findings underscore the strong diagnostic performance of QFR across its various modes. Our meta-regression revealed that the log value of the pooled DOR for cQFR was nearly double that of fQFR. Similar outcomes were presented in smaller prospective observational studies by Tu et al.,\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e van Rosendael et al.,\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and Echavarr\u0026iacute;a-Pinto et al.,\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e supporting the robustness of these observations. They demonstrated that the QFR computation, based on a patient-specific contrast-flow model derived from coronary angiography without pharmacologic hyperemia induction, achieved superior diagnostic accuracy compared to the fixed-flow approach. The cQFR employs frame count analysis from regular (non-hyperaemic) angiographic projections to simulate hyperemic flow velocity. Conversely, the fQFR applies a fixed empiric hyperemic flow velocity derived from prior FFR studies, thereby eliminating the requirement for TIMI frame counting and disregarding the impact of coronary microvasculature circulation. Consequently, the diagnostic accuracy of fQFR was found to be inferior to that of cQFR in the present study.\u003c/p\u003e \u003cp\u003eThis study further revealed that \u0026micro;QFR exhibited superior diagnostic accuracy through the utilization of QFR computations based on a single angiographic view, thereby eliminating the necessity for 3D angiographic reconstruction. Several factors may account for this outcome. Firstly, the \u0026micro;QFR computation precisely delineated the lumen contours of side branches and reconstructed a step-down reference diameter function for the hypothetical healthy vessel, as opposed to assuming a linear tapering of the reference vessel size. This methodology captured the inherent fractal physiology of bifurcations, facilitating a more accurate quantification of lesion severity, which is a pivotal determinant of FFR. Secondly, the \u0026micro;QFR computation leveraged angiographic views with optimal image quality. Adequate exposure of the stenotic segment enhanced the precision in quantifying the geometry of the vessel under investigation. Notably, \u0026micro;QFR based on a single angiographic view was reported to possess technical advantages, including easier operation, shorter analysis time, and enhanced reproducibility.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of our study merit consideration. Firstly, despite substantial evidence supporting the diagnostic accuracy of QFR assessment, the impact of specific patient or lesion characteristics (such as a history of recent myocardial infarction or coronary artery bypass grafting, multi-vessel disease, or the quality of angiographic images) on the diagnostic accuracy or clinical effectiveness of QFR remains largely unknown. Notably, the quality of angiographic material is of foremost importance for accurate QFR computation. Hence, further research is warranted to investigate parameters for automatically assessing the quality of angiographic acquisitions intended for QFR analysis. Secondly, the absence of individual patient-level data hindered deeper analysis to identify predictors of QFR accuracy. Lastly, despite the negative meta-regression of population characteristics, significant heterogeneity remained in our study. Consequently, well-designed, large-scale, multicenter prospective clinical trials are essential to better understand the role of FFR and QFR-guided assessments in complex clinical scenarios.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our analysis confirms the impressive diagnostic performance of QFR in detecting functional ischemia of coronary arteries, with pressure-wire measured FFR serving as the reference standard. Notably, uQFR demonstrated the highest diagnostic performance among the evaluated modes. QFR might be considered a reliable and useful alternative to pressure wire-based FFR due to its simplicity and non-invasive modality. However, this superiority should be interpreted with caution, given the observed heterogeneity, the lack of automated quality assessment for angiographic acquisitions, and the complexities inherent in clinical practice. Therefore, further randomized trials are warranted to unveil the value of a QFR-based strategy in patients requiring functional evaluation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eRESOURCE AVAILABILITY\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eData provided in manuscript or supplementary information file.\u003c/p\u003e\n\u003cp\u003eCLINICAL TRIAL NUMBER\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eFUNDING DECLARATION\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (grant number 82273899).\u003c/p\u003e\n\u003cp\u003eAUTHOR CONTRIBUTIONS\u003c/p\u003e\n\u003cp\u003eG.H., M.S., W.S., P.G. and H.Z. participated in the design, data search, data analysis, and manuscript writing. S.H., and L.S. participated in the data supervising and manuscript review.\u003c/p\u003e\n\u003cp\u003eDECLARATION OF INTERESTS\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eReporting Checklist\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have completed the PRISMA reporting checklist. Available at \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTsao, C. W. et al. 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Morphometric assessment for functional evaluation of coronary stenosis with optical coherence tomography and the optical flow ratio in a vessel with single stenosis. \u003cem\u003eJ. Clin. Med.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 5198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://10.3390/jcm11175198\u003c/span\u003e\u003cspan address=\"https://10.3390/jcm11175198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Quantitative flow ratio, Fractional flow reserve, Angiography, Significant stenosis","lastPublishedDoi":"10.21203/rs.3.rs-6395472/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6395472/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eQuantitative flow ratio (QFR) is a novel technology to assess the functional significance of coronary stenoses based on standard coronary angiography, which can be alternatives to invasive fractional flow reserve (FFR) assessment. However, the evidence is limited to single-center studies and small sample sizes. This study systematically determined the diagnostic performance of QFR to diagnose functionally significant stenosis with FFR as the reference standard. A systematic review and meta-analysis of studies assessing the diagnostic performance of angiography-derived QFR systems were performed. All relevant studies from 6 literature databases were searched and screened according to the inclusion and exclusion criteria. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR\u0026minus;) and diagnostic odds ratio (DOR) along with their 95% confidence intervals (CIs), were calculated using DerSimonian Lair methodology. The summary receiver operating characteristic (SROC) curve and area under the curve were estimated. Meta-regression analysis was performed to identify potential source of heterogeneity. Fifty-seven studies comprising 13,215 patients and 16,125 vessels were included in the final analysis. At the vessel level, the pooled sensitivity and specificity of QFR for detecting a significant coronary stenosis were 0.826 (95% CI: 0.798\u0026ndash;0.851) and 0.919 (95% CI: 0.902\u0026ndash;0.933). Pooled LR\u0026thinsp;+\u0026thinsp;and LR\u0026thinsp;\u0026minus;\u0026thinsp;were 10.198 (95% CI: 8.469\u0026ndash;12.281) and 0.189 (95% CI: 0.163\u0026ndash;0.219) with a pooled DOR was 53.968 (95% CI:42.888\u0026ndash;67.910). The SROC revealed an area under the curve (AUC) of 0.94 (95% CI: 0.91\u0026ndash;0.96). The summary AUCs were 0.90 (95% CI: 0.87\u0026ndash;0.92) for fixed-flow QFR (fQFR), 0.95 (95% CI: 0.92\u0026ndash;0.96) for contrast-flow QFR (cQFR), 0.97 (95% CI: 0.95\u0026ndash;0.98) for Murray law-based QFR (\u0026micro;QFR), and 0.91 (95% CI: 0.89\u0026ndash;0.94) for non-specified QFR. The adjusted pooled DORs were as follows: 126.25 for uQFR, 45.49 for cQFR, 26.12 for adenosine-flow QFR (aQFR), 25.88 for fQFR, and 36.54 for non-specified QFR. The accuracy of angiography-derived QFR was strong to assess the functional significance of coronary stenoses with FFR as a reference. uQFR demonstrated the highest diagnostic performance among the five evaluated modes.\u003c/p\u003e","manuscriptTitle":"Diagnostic performance of angiography-derived quantitative flow ratio: A systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 07:21:37","doi":"10.21203/rs.3.rs-6395472/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc55bd1d-8d08-416a-b0d3-a44375b73cd9","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49244730,"name":"Health sciences/Cardiology/Cardiac device therapy"},{"id":49244731,"name":"Health sciences/Cardiology/Interventional cardiology"}],"tags":[],"updatedAt":"2025-11-17T07:39:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 07:21:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6395472","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6395472","identity":"rs-6395472","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}