Diagnostic Performance of Target-position Murray Law based Quantitative Flow Ratio (target-μFR) vs Vessel-μFR in Patients with stable Coronary Artery Disease

Diagnostic Performance of Target-position Murray Law based Quantitative Flow Ratio (target-μFR) vs Vessel-μFR in Patients with stable Coronary Artery Disease | 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 Research Article Diagnostic Performance of Target-position Murray Law based Quantitative Flow Ratio (target-μFR) vs Vessel-μFR in Patients with stable Coronary Artery Disease Wenhao Huang, Yajun Liu, Qianqian Wang, Hongfeng Jin, Yiming Tang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3844865/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Background: We aim to compare with the diagnostic performance of target-position quantitative flow ratio derived from Murray Law (target-μFR) and vessel quantitative flow ratio derived from Murray Law (vessel-μFR) using the fractional flow reserve (FFR) as reference standard. This study may provide more evidence for the novel clinical usage of target-μFR in the diagnosis of coronary artery disease. Methods: Six hundreds and fifty-six patients (685 lesions) with known or suspected coronary artery disease were screened for this retrospective analysis between January 2021 to March 2023. A total of 161 patients (190 lesions) underwent quantitative coronary angiography and FFR evaluations. Both of target-μFR and vessel-μFR were compared the diagnostic performance using the FFR≤0.80 as the reference standard. Results: Both target-μFR (R=0.90) and vessel-μFR (R=0.87) demonstrated a strong correlation with FFR, and both methods showed great agreement with FFR. The area under the receiver operating characteristic curve was 0.937 for target-μFR and 0.936 for vessel-μFR in predicting FFR≤0.80. FFR≤0.80 were predicted with high sensitivity (92.98%), specificity (91.01%) and the Youden index (0.840) using the cutoff value of 0.83 for target-μFR. A good diagnostic performance (sensitivity 86.44%, specificity 88.51% and Youden index 0.750) was also demonstrated by vessel-μFR which the cutoff value was 0.80. Conclusion: The target-μFR has the similar diagnostic performance with vessel-μFR. The accuracy of μFR does not seem to be affected by the selection of the measurement point. Both of the virtual model could be used as computations tools for diagnosing ischemia and to aid clinical decision-making. Coronary physiology Murray Law based Quantitative Flow Ratio Fractional Flow Reserve Coronary Artery Disease Diagnostic Performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The functional evaluation of coronary physiology plays a vital role in guiding diagnosis and treatment strategies in patients with known or suspected coronary artery diseases (CAD). The invasive fractional flow reserve (FFR) has the highest recommendation (class IA) for the evaluation of CAD in guidelines[1.2].However, the adoption of FFR in daily clinical practice has been limited because of the invasive of the procedure, requirement of pressure wire, and the administration of hyperemic agents [ 3 – 5 ]. The virtual FFR from coronary artery imaging may increase the utility of FFR assessment in clinical practice. Currently, more attention has been paid in these novel applications of virtual FFR derived from coronary artery imaging, such as quantitative flow ratio (QFR), intravascular ultrasound-derived fractional flow fraction (IVUS-FFR) and coronary computed tomography-derived fractional flow reserve (CT-FFR), without pressure wire and administration of hyperemic agents[ 6 – 8 ].QFR has improved the diagnostic performance for identifying hemodynamically significant coronary stenosis compared with the assessment of coronary lesion based on QCA[ 7 – 9 ]. Accumulating evidence has proved the high correlation of QFR with FFR in the diagnosing ischemia and to aid clinical decision-making[ 9 – 11 ]. Quantitative flow ratio derived from Murray Law (µFR) is a novel technique for fast computation of FFR from coronary agiography (CAG) and estimates the pressure drop due to coronary stenosis according to semiautomatic delineation of target vessels and FFR simulation from single-angle[ 12 ]. The accuracy of virtual FFR derived from coronary imaging depends importantly on the measurement location of the coronary artery tree. Previous study showed that lesion-CT-FFR (defined the value at approximately 2-3cm distal to the target lesion of the coronary artery) can reclassify positive patients defined by the vessel-derived CT-FFR or lowest CT-FFR (defined the value at the distal to the target lesion of the coronary artery), and that lesion CT-FFR has higher diagnostic performance than vessel CT-FFR[ 13 ]. The outcomes after 60-day follow up indicated that revascularization rates were significantly higher in lesion CT-FFR positive than those with vessel CT-FFR positive (53% vs 44%, P < 0.01), suggesting that clinicians should not only consider the vessel CT-FFR value when making clinical decisions[ 14 ]. Recently, more and more studies show that the lesion CT-FFR has higher diagnostic accuracy than vessel CT-FFR [ 13 – 15 ]. However, the measurement of the µFR was always stay at the vessel level (vessel-µFR, defined the value at the distal to the target lesion of the coronary artery) in most of current studies, while whether vessel-µFR is different from the target-position Murray Law based quantitative flow ratio (targer-µFR, defined the value at the position recorded during FFR evaluation procedure) has less been reported. As in the case of CT-FFR, we speculate that target-µFR may be more superior to the diagnostic performance than the vessel-µFR and may better guide catheterization laboratory revascularization strategies. In this study, we aim to compare with the diagnostic performance of target-µFR and vessel-µFR using the FFR as reference standard. This study may provide more evidence for the clinical usage of QFR in the diagnosis and treatment of coronary artery disease (CAD).. Methods Study population A retrospective, single-center, observational study was conducted from January 2021 to March 2023, including consecutive patients with CAD who underlying FFR assessment were eligible for enrollment. The inclusion criteria were as follows: (1) age ≥ 18 years; (2) patients with suspected or known CAD; and (3) at least one lesion with 30–80% diameter stenosis (DS%) based on visual estimation. The exclusion criteria were as follows: (1) angiographic evidence of thrombi-containing lesions; (2) severe valvular heart diseases; (3) left ventricle ejection fraction < 30%; (4) angiographic features limiting QFR computation (eg, left main or ostial right coronary artery ongoing ventricular arrhythmias, and significant and persistent tachycardia); (5) significant foreshortening or vessel overlapping; (6) previous coronary artery bypass grafting; (7) inadequate contrast flush; (8) deep catheter intubation into the lesion precluding complete visualization of stenosis; (9) severely calcified vessels; or (10) inconsistent image format. The study was conducted in compliance with the Declaration of Helsinki, and was approved by the Ethics Review Committee of Zhejiang Hospital. Individual informed consent was waived due to the retrospective nature of the study. QCA and measurement of physiological indices The radiographic system Allura Xper FD20/10 (PHILIPS Medical Systems, the Netherlands) was used for the angiographic imaging at a rate of 15 frames/s. The contrast medium was injected at a stable rate of approximately 4 mL/s using a pump. The QCA was performed using the Angiogram QCA software (Allura Xper FD20/10; PHILIPS Medical Systems, The Netherlands). A coronary pressure wire (St. Jude Medical, St. Paul, Minnesota, USA) was used to calculate FFR with the pressure sensor positioned at 2–3 cm distal to the target lesion of the coronary artery. Before placement, the pressure wire was calibrated and equalized, and intravenous adenosine triphosphate concentration was 150–180 g/kg/min to induce maximum hyperemia of the coronary microvascular system. Simultaneously, the distal and proximal coronary artery pressures at the pressure sensor (Pd) and coronary ostium (Pa) were recorded. The pressure sensor was then pulled back to the proximal end to assess or correct pressure drift. The FFR was determined by dividing Pd by Pa. Further analysis was performed at the core laboratory using all ICA and FFR data. Thus, a standardized radiographic system, pressure wire, and software were used, and strict protocols were followed for data collection and analysis to ensure accuracy and reliability.[ 6 ] µFR measurement In this research, the µFR with simultaneous QCA analysis independently used the calculated software (AngioPlus Core, Pulse Medical Imaging Technology Co., Ltd., Shanghai, China). The calculation of µFR, which semiautomatic delineation of vessel contours and FFR simulation from single-angle, was enable by this artificial intelligence-based algorithm. The key frame was first selected from an optimal CAG image which had a sharp lumen contour at the stenosis segment. Then the vessel contours were automatically delineated, and the reference lumen diameter was reconstructed according to Murray Law of blood flow distribution. The proximal and distal reference lumen diameter could be manually adjusted as needed. And an appropriate manual correction was allowed under certain circumstances, such as stenosis or individual contours with wrong automatic recognition. Finally, both µFR of main and side-branches coronary has been calculated. The details of the vessel-µFR (value at the distal to the target lesion of the coronary artery) computation method were reported in previous studies[ 9 – 11 ]. Based on previous studies, we use the vessel-µFR ≤ 0.80 as cutoff value for predicting ischemia myocardial in our research[ 16 – 18 ]. Then the target-µFR values were obtain at the position recorded during FFR evaluation procedure (the position was located approximately 2-3cm distal to the target lesion of the coronary artery). Both of vessel-µFR and target-µFR were calculated independently by two senior physicians who were unaware of the clinical data. If the two physicians fell into a disagreement, a third, more senior associate chief physician or chief physician would re-calculate and determine it[ 19 ]. (Fig. 1) Figure.1 Example of the same vessel of the same patients, target-µFR (A) and vessel-µFR (B) was analyzed separately Statistical analysis Continuous and binary variables were presented as mean ± standard deviation (SD) and percentages, respectively. Pearson’s or Spearman’s correlation coefficients were used to quantify the correlations. Bland–Altman plots were used to assess the agreements, which displayed the differences between each pair of measurements versus their mean values with reference lines for the mean difference of all paired measurements. The agreement limits were defined as the mean ± 1.96 SD of the absolute difference. To predict functionally significant stenosis (defined as FFR ≤ 0.80), sensitivity, specificity, and the Youden index (defined as [sensitivity/100] + [specificity/100] − 1) were calculated for cutoff values of target-µFR. To assess the area under the curve (AUC) of target-µFR and vessel-µFR, receiver operating characteristic (ROC) curve analysis was performed. All statistical analyses were performed using MedCalc (version 19.0, MedCalc Software, Ostend, Belgium), and P < 0.05 was defined as statistically significant.[ 6 ] Results Baseline clinical and lesion characteristics During the research period, six hundreds and fifty-six patients (685 lesions) with known or suspected coronary artery disease were screened for this retrospective analysis between January 2021 to March 2023. A total of 161 patients (190 lesions) underwent quantitative coronary angiography (QCA) and FFR evaluations in our catheter lab. For the excluded patients, five patients (12 lesions) had the lesion in left main coronary or ostial right coronary artery, seven patients (12 lesions) had significant foreshortening or vessel overlapping, four patients (9 lesions) had severely calcified vessels, four patients (5 lesions) had inadequate contrast flush, and four patients (10 lesions) had inconsistent image format. In the fnal analysis, 137 patients (146 vessels) were included in this study (Fig. 2). The mean age was 64.5±10.5 years, and 95 (69.3%) were male. Approximately 44.5% of patients had the history of smoking. The mean left ventricular ejection fraction was 65.8±7.58%. The baseline clinical patient characteristics are listed in Table1. The target vessels included 92 (63.0%) left anterior descending arteries(LAD), 18 (12.3%) left circumfex arteries(LCX), and 36 (24.7%) right coronary arteries(RCA). The mean values of target-μFR, vessel-μFR, and FFR were 0.83±0.101, 0.83±0.098, and 0.82±0.098, respectively. The baseline lesion characteristics are listed in Table 2. Figure.2 Participant fowchart of the study. FFR: fractional fow reserve, CAG: coronary angiography, QCA: quantitative coronary angiography, target-μFR: target-position Murray Law based quantitative flow ratio and vessel-μFR: vessel quantitative flow ratio derived from Murray Law. TABLE1 Baseline Clinical Patient characteristics Values are`Mean±SD or n (%). BMI: Body mass index, CAD: coronary artery disease, CABG: coronary artery bypass grafting PCI: percutaneous coronary intervention, MI: myocardial infarction. TABLE2 Baseline Lesion Characteristics Values are`Mean±SD, Mean (P25,P75) or n (%). LAD: lanterior descending coronary artery, LCX: left circumflex artery, RCA: right coronary artery. QCA: quantitative coronary angiography. Target-μFR: target-position Murray Law based quantitative flow ratio. vessel-μFR: Vessel quantitative flow ratio derived from Murray Law. Comparison of the correlations and agreements among target- μ FR, vessel- μ FR, and invasive-FFR Figure. 3 illustrates a visual representation of target-μFR, vessel-μFR, and FFR measurements. Figure. 4 shows the correlation and agreement among these measurements. The results demonstrate that both target-μFR and vessel-μFR are highly correlation with FFR, with R of 0.87 (P<0.001) and 0.90 (P<0.0001), respectively. A great agreement is demonstrated by both target-μFR and vessel-μFR with FFR, with similar mean diferences of 0.02±0.045 and 0.01±0.050 respectively. (Fig. 4). Figure.3 A representative example of target-μFR, vessel-μFR, and FFR measurements. (A)Wire-based Fractional flow reserve (FFR) = 0.77. (B) Vessel quantitative flow ratio derived from Murray Law (vessel-μFR) = 0.75. (C) Target-μFR: target-position Murray Law based quantitative flow ratio (target-μFR) = 0.81. Figure. 4 Correlations and agreements among target-μFR, vessel-μFR, and FFR. (A) Correlation between target-μFR and FFR. (B) Agreement between vessel-μFR and FFR. The mean value of target-μFR - FFR = 0.02, the upper limit of agreement = 0.10, and the lower limit of agreement =-0.07. (C) Correlation between vessel-μFR and FFR. (D) Agreement between vessel-μFR and FFR. Mean value of vessel-μFR - FFR = 0.01, the upper limit of agreement = 0.11, and the lower limit of agreement = –0.09. Diagnostic performance of target- μFR adn vessel- μFR for predicting invasive-FFR ≤ 0.80 Figure. 5 presents the receiver operating characteristic (ROC) curves for target-μFRand vessel-μFR for FFR≤0.80 predictions. The AUC for target-μFR was similar to that for vessel (0.0937 vs. 0.0936). Figure. 6 illustrates the sensitivity, specifcity, and Youden index for cutoff values of lesion in predicting FFR≤0.80. The optimal cutoff value for target-μFR to predict FFR≤0.80 was 0.83. This demonstrated the superior diagnostic ability of target-μFR and vessel-μFR in identifying whether stenoses can lead to ischemia. Figure. 5 The receiver operating characteristic curves of target-μFR and vessel-μFR in detecting FFR≤0.80. Figure. 6 Sensitivity, specificity and Youden index for different cutoff values of leison-QFR to predict FFR≤0.80. The optimal target-μFR cutoff vlaue for predicting FFR≤0.80 was 0.83. Diagnostic performance of target- μFR ≤ 0.83 and vessel- μFR ≤ 0.80 for predicting FFR ≤ 0.80 The diagnostic performance of target-μFR≤0.83 and vessel-μFR≤0.80 for predicting FFR≤0.80 is presented in Table 3. Among all 146 lesions, using FFR as the reference standard, target-μFR had 53 true positives (TP), 81 true negatives (TN), 4 false positive (FP), and 8 false negative (FN), while vessel-μFR had 46 TP, 83 TN, 2 FP, and 15 FN. target-μFR had a false discovery rate of 13.11% (positive predict value 86.89% ) and a false omission rate of 14.71% (negative predict value 95.29%) compared with FFR. This indicates that 7.02% of stenosis were misclassified using target-μFR compared to FFR. On the other hand, the vessel-μFR also showed a relatively good diagnostic performance, 13.6% were misclassified using vessel-μFR compared to FFR. The diagnostic accuracy of target-μFR≤0.83 for predicting FFR≤0.80 was 92.98% (95% confidence interval [CI]: 83.00% to 98.06%), while that of vessel-μFR was 86.40% (95% CI: 75.00% to 94.00% ). A good diagnostic performance indicates that an accurate assessment of coronary stenosis is feasible. What’s more, the target-μFR’s diagnostic performance (Youden index 0.840) was slightly better than the traditional QFR calculation (vessel-μFR, Youden index 0.750). Table 3 Diagnostic performance of t arget-μFR and vessel-μFR for predicting FFR≤0.80 Value are n(95%confidence interval). Abbreviations as in Table 2. Discussion In this study, we compared the diagnostic performance between target-µFR and vessel-µFR using the FFR ≤ 0.80 as reference standard. The primary study findings are summarized as follows: 1) the µFR is a novel physiological assessment methods which estimates the pressure drop due to coronary stenosis according to semiautomatic delineation of target vessels and FFR simulation from single-angle. 2) both target-µFR and vessel-µFR demonstrated high correlations and great agreements with FFR; 3) the ability of target-µFR defining hemodynamic significant of coronary stenosis was similar to vessel-µFR; 4) diagnostic performance of target-µFR was slightly better than that of vessel-µFR;5) the selection of the measurement location has less influence on the accuracy of µFR. Based on these findings, it could improve the calculation algorithm for enhancing the time-efficient and showed the potential in the coronary imagine and virtual physiological evaluation of CAD. The ability of µFR highlights that by integration of the imaging information in order to enable a comprehensive assessment of the CAD. DEFER, FAME, FAME II and FAME III establish FFR as the "gold standard" of coronary physiology for assessing coronary artery stenosis, treatment plan formulation and evaluation of treatment effect[ 20 – 23 ]. However, as an invasive method, the application of FFR requires expensive equipment and has potentially procedure-related complications, such as non-fatal myocardial infarction, cerebrovascular accident, and has been limited in clinical practice because of the invasive of the procedure, requirement of pressure wire, the administration of hyperemic agents and so on[ 3 – 5 , 24 ]. To solve these limitations and reduce the complications, the QFR had been developed which is a virtual FFR technique derived from coronary angiography. And QFR derived from Murray Law as a novel angiographic-based method could enable fast computation of FFR, which provides an avenue for determining the most appropriate therapy for the intermediate lesions. A large number of clinical studies have confirmed the accuracy of QFR in assessing coronary artery function. In FAVOR Pilot Study, the fix-flow QFR (fQFR), contrast-flow QFR (cQFR) and adenosine-flow QFR (aQFR) was compared with the gold standard FFR for evaluate the capability of those QFR in predicting coronary stenosis. The results confirm that fQFR, cQFR and aQFR had shown the great agreement and diagnostic performance (accuracy 80%, 86%, and 87%) for predicting ischemia myocardial[ 9 ]. Then, in the FAVOR II China, the sensitivity and specificity in identifying hemodynamically significant stenosis were evidently higher for QFR than for QCA (94.6% vs. 62.5%; 91.7% vs. 58.1%). The FAVOR II China also revealed that vessel-level QFR had a high diagnostic accuracy of 93.3%[ 10 ]. In a large study of FAVOR II E/J, the good diagnostic performance of QFR assessed the degree of coronary stenosis (accuracy 86.3%, specificity 86.9%, sensitivity 86.5%, AUC 0.92) and evaluated the calculation time of QFR and FFR. Furthermore, the time to complete QFR (5min) was significantly shorter than the time to complete FFR (7min) [ 25 ]. The FAVOR II China and FAVOR II E/J had proved that the diagnostic accuracy of QFR at both the patients and vessels level was better than QCA in the assessment of the relevance of functional stenoses.In a meta-analysis of 16 high quality researches comparing FFR and QFR, QFR has demonstrated good positive and significant negative predictive values in confirming the relevance of the ischemia myocardial on the basis of the FFR cut-off ≤ 0.80[ 18 ].Subsequently, Wienemann etal further verified the diagnostic performance of cQFR (sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 82%, 95%, 97%, 92% and 91%, respectively). The area under the curve shown for cQFR is greater than that for fQFR and RFR (0.938 vs. 0.891,0.869, P < 0.01). The good diagnostic performance of cQFR was maintained in different clinical subpopulations (including gender, aortic stenosis and atrial fibrillation, etc.) and different anatomical subpopulations (including focal and non-focal lesions, etc.)[ 26 ]. In a head-to-head study, QFR showed good agreement with QFR compared with SPECT and PET. Meanwhile, the accuracy of QFR was 88%, 82% for SPECT and 78% for PET[ 27 ]. What’s more, the QFR derived from CAG is the only functional angiography system for which the value in clinical practice has been assessed in a randomised clinical trial. The patients, who enrolled in FAVOR III China, with at least one 50–90% coronary stenosis underwent QFR-guide strategies (if QFR ≤ 0.80) or angiographic-based strategies. After 1-year of follow up, patients randomised to the QFR-guide strategies demonstrated better outcomes driven by fewer myocardial infarctions and ischemia-driven revascularisations[ 28 ]. Based on those studies, QFR has demonstrated good diagnostic accuracy in detecting myocardial ischemia. Meanwhile, QFR could more quickly and conveniently calculate virtual FFR after CAG without any intervention operations, further providing clinical support for revascularization strategies. But none of the QFR mentioned above studies is a lesion-specific QFR. In previous studies, they demonstrated that the choice of virtual FFR measurement locations is particular importance when identifying ischemic lesions or guiding treatment strategies[ 13 – 15 ]. Series studies demonstrated that lesion-specific FFR, such as lesion-specific CT-FFR, can reclassify positive patients defined by the vessel-derived FFR value, and that lesion-specific FFR has higher diagnostic performance than vessel-derived FFR[ 29 – 32 ]. The possiable causes of the above phenomennon were as follows: 1) the virtual FFR measurement at far distal segments may overeatimate coronary ischemia, 2) these differences between vessel territories in pressure gradients for segments 1–2 cm distal to the stenosis versus far distal segments relate to the larger territory of perfused myocardium, 3) the virtual FFR were only assessed in the main coronary arteries, which may have disregarded the impact of collateral stenosis on myocardial ischemia[ 6 , 14 ]. Then, Kołtowski Ł et al analysised the diagnostic performance of index QFR, vessel QFR (assessemnt for entire segmented vessel) and lesion QFR (assessment for the target lesion) to identify the best measurement location for optimal accuracy of QFR. Thie researche demostrated the index QFR value which obtained at the pressure transducer position (R 0.85, accuracy 85.4%, AUC 0.94) had better diagnostic performance than vessel QFR (R 0.78, accuracy 78.5%, AUC 0.90) and lesion QFR (R 0.70, accuracy 76.1%, AUC 0.82)[ 33 ]. Of note, the µFR had been showed gteat agreement and correlation with standard three-dimensional quantitative flow ratio (R 0.996)[ 34 ]. All the above results are comparable to those shown in our analysis. In previous studies, the µFR demonstrated powerful and superior diagnostic performance for lesion-specific ischemia compared with angiography alone regardless of coronary calcification[ 35 ]. Hence, this paper explores whether target-µFR could further improve the diagnostic ability of myocardial ischemic by comparing the diagnostic performance of target-µFR and vessel-µFR. The diagnostic performance (accuracy 86.40% ,sensitivity 86.44%, specificity 88.51%, and Youden index 0.750) and AUC (0.936) of vessel-µFR were similar to those reported in previous studies. Then, those indexes of target-µFR (accuracy 92.98%,sensitivity 92.98%, specificity 91.01%, Youden index 0.840, and AUC 0.937) were slightly better than vessel-µFR and previous research. At the same time, we found the calculation time of target-µFR (4-5min) and vessel-µFR (4-5min) was also similar to the QFR reported in previous papers. Becaose of that, the µFR could assess the degree of coronary stenosis which is a time-efficient and accurate method, the visualized anatomic geometry of the coronary artery can provide guidance for subsequent therapeutic regimens. By the way, the µFR calcualted the pressure loss by the frictional loss along the lesion entrance and stenotic segment as well as the inertial loss stemming from the sudden expansion of the flow as it emerged from the stenosis were calculated, based on the stenosis geometry and the hyperemic flow rate. What’s more, based on the U-Net architecture, Murray Law and artificial intelligence, µFR automatically outlines the lumen of the target vessels and their collaterals through artificial intelligence[ 12 ]. All in all, we suggest the accuracy of µFR have almost less influence by the selection of the remote measurement locations. At the same time, µFR behaved similarly well in sexes and has great diagnostic performance, indicating its potential as a reliable wireless tool for identifying functional ischemia[ 36 ]. However, our study had several limitations. First, it was a single-center and retrospective study with a small sample size. This might have introduced selection bias even though consecutive patients were included. The limited number of enrolled patients due to the low adoption rate of patients undergoing FFR in clinical practice also affected the statistical efficiency of the study. Secondly, not all the vessels were interrogated for the enrolled patients. The vessels with diameter stenosis 90% were not assessed because performing physiological assessments in such lesions was unnecessary. Thirdly this is a retrospective analysis in which one-third of the data were excluded because the QFR assessment was not applicable, the study should more likely be viewed as a hypothesis-generating study, and further prospective studies would provide more evidence. The availability of QFR can be improved by requiring careful attention to the projection angle and location of the target lesions in coronary angiography; however, the extent to which this can be improved remains to be assessed. Forth, target-µFR and vessel-µFR computation require automatic reconstruction of 3D anatomical models of coronary vessels, and further studies should be consider that analyze the impact of anatomical features on diagnostic accuracy in target vascular lesions. Fifth, there may be inter-operator differences and previous PCI operation effect in the target-µFR and vessel-µFR calculation process, so further evidences from larger studies are needed. Sixth, the selection of the measurement location depends on the location recorded during the FFR evaluation procedure. Influenced by the real world, some of the measurement locations cannot be accurately positioned ar rhe 2-3cm distal to the target vessels. Hence, further large-sample. Multicenter, prospective, randomized sthudies are needed to further confirm the feasibility of target-µFR in clinical practice. Conclusion The target-µFR has the similar diagnostic performance with vessel-µFR. The accuracy of µFR does not seem to be affected by the selection of the measurement point. Both of the virtual model could be used as computations tools for diagnosing ischemia and to aid clinical decision-making. Declarations Ethical approval This study was approved by the Ethics Review Committee of Zhejiang Hospital and conducted according to the principles of the Declaration of Helsinki. Since this clinical study was a retrospective analysis of the information from previous cases, without direct contact with the subjects and subject privacy protection was ensured, the risk borne by the subjects was not greater than the minimum risk. The Ethics Review Committee of Zhejiang Hospital agreed to exempt informed consent after review. Consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflict of interest The authors have no conflicts of interest to declare that are relevant to the content of this article. Funding The present study was supported by the Major medical and health science and technology plan of Zhejiang Province (grant No. WKJ-ZJ-1913), the Natural Science Foundation of Zhejiang Province (grant No. LY21H020002),and the Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province(grant no. 2023ZL223). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions Conception and design of the work: all authors; acquisition and assembly of data: all authors; analysis, computation and interpretation of data: Wenhao Huang, Qianqian Wang and Yajun Liu; drafting the work: Wenhao Huang and Lin Yang; revising it critically for important intellectual content: Changqing Du and Lijiang Tang; Final approval of the manuscript: all authors. Acknowledgements We appreciate very much for all the healthcare staff and engineers who assisted in the current study. References Writing Committee Members, Lawton JS, Tamis-Holland JE, et al. 2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2022;79(2):197-215. Neumann FJ, Sousa-Uva M, Ahlsson A, et al. 2018 ESC/EACTS Guidelines on myocardial revascularization. Eur Heart J. 2019;40(2):87–165 Pijls NH, De Bruyne B, Peels K, et al. Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. N Engl J Med. 1996;334(26):1703–1708. Zimmermann FM, Ferrara A, Johnson NP, et al. Deferral vs. performance of percutaneous coronary intervention of functionally non-significant coronary stenosis: 15-year follow-up of the DEFER trial. Eur Heart J. 2015;36(45):3182–3188. . N. Pijls, Bernard De, Mamdouh El, et al. Fractional Flow Reserve: The Ideal Parameter for Evaluation of Coronary, Myocardial, and Collateral Blood Flow by Pressure Measurements at PTCA. Journal of Interventional Cardiology, 1993, 6(4) : 331–344. Huang W, Zhang J, Yang L, et al. Accuracy of intravascular ultrasound-derived virtual fractional flow reserve (FFR) and FFR derived from computed tomography for functional assessment of coronary artery disease. Biomed Eng Online. 2023;22(1):64. Cortés C, Carrasco-Moraleja M, Aparisi A, et al. Quantitative flow ratio-Meta-analysis and systematic review. Catheter Cardiovasc Interv. 2021;97(5):807-814. Tomaniak M, Serruys PW. Combining anatomy and physiology: New angiography-based and computed tomography coronary angiography-derived fractional flow reserve indices. Cardiol J. 2020;27(3):225–229. Tu S, Westra J, Yang J, et al. Diagnostic Accuracy of Fast Computational Approaches to Derive Fractional Flow Reserve From Diagnostic Coronary Angiography: The International Multicenter FAVOR Pilot Study. JACC Cardiovasc Interv. 2016;9(19):2024-2035. Xu B, Tu S, Qiao S, et al. Diagnostic Accuracy of Angiography-Based Quantitative Flow Ratio Measurements for Online Assessment of Coronary Stenosis. J Am Coll Cardiol. 2017;70(25):3077-3087. Westra J, Andersen BK, Campo G, et al. Diagnostic Performance of In-Procedure Angiography-Derived Quantitative Flow Reserve Compared to Pressure-Derived Fractional Flow Reserve: The FAVOR II Europe-Japan Study. J Am Heart Assoc. 2018;7(14):e009603. Tu S, Ding D, Chang Y, Li C, et al. Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law. Catheter Cardiovasc Interv. 2021;97 Suppl 2:1040-1047. Kueh SH, Mooney J, Ohana M, et al. Fractional flow reserve derived from coronary computed tomography angiography reclassification rate using value distal to lesion compared to lowest value[J]. J Cardiovasc Comput Tomogr, 2017,11(6):462–467. .Omori H, Hara M, Sobue Y, et al. Determination of the optimal measurement point for fractional flow reserve derived from CTA using pressure wire assessment as reference[J]. AJR Am J Roentgenol,2021,216(6):1492–1499. Cami E, Tagami T, Raff G, et al. Assessment of lesion-specific ischemia using fractional flow reserve (FFR) profiles derived from coronary computed tomography angiography (FFRCT) and invasive pressure measurements (FFRINV): Importance of the site of measurement and implications for patient referral for invasive coronary angiography and percutaneous coronary intervention[J]. J Cardiovasc Comput Tomogr,2018,12(6):480–492. Guan C, Geng L, Zhang R, et al. Long-term prognostic value of dynamic function assessment of intermediate coronary lesion with computational physiology. Catheter Cardiovasc Interv. 2022;99 Suppl 1:1386-1394. Tanigaki T, Emori H, Kawase Y, et al. QFR Versus FFR Derived From Computed Tomography for Functional Assessment of Coronary Artery Stenosis. JACC Cardiovasc Interv. 2019;12(20):2050–2059. Westra J, Tu S, Campo G, et al. Diagnostic performance of quantitative flow ratio in prospectively enrolled patients: An individual patient-data meta-analysis. Catheter Cardiovasc Interv. 2019;94(5):693-701 Westra J, Sejr-Hansen M, Koltowski L, et al. Reproducibility of quantitative flow ratio: the QREP study. EuroIntervention. 2022;17(15):1252-1259. Zimmermann FM, Ferrara A, Johnson NP, et al. Deferral vs. performance of percutaneous coronary intervention of functionally non-significant coronary stenosis: 15-year follow-up of the DEFER trial. Eur Heart J. 2015;36(45):3182-3188. Tonino PA, Fearon WF, De Bruyne B, et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol. 2010;55(25):2816-2821. Fearon WF, Nishi T, De Bruyne B, et al. Clinical Outcomes and Cost-Effectiveness of Fractional Flow Reserve-Guided Percutaneous Coronary Intervention in Patients With Stable Coronary Artery Disease: Three-Year Follow-Up of the FAME 2 Trial (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation). Circulation. 2018;137(5):480-487. Fearon WF, Zimmermann FM, Ding VY, et al. Quality of Life After Fractional Flow Reserve-Guided PCI Compared With Coronary Bypass Surgery. Circulation. 2022;145(22):1655-1662. Zhuang B, Wang S, Zhao S, et al. Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis. Eur Radiol. 2020;30(2):712-725. Westra J, Andersen BK, Campo G, et al. Diagnostic Performance of In-Procedure Angiography-Derived Quantitative Flow Reserve Compared to Pressure-Derived Fractional Flow Reserve: The FAVOR II Europe-Japan Study. J Am Heart Assoc. 2018;7(14):e009603. Wienemann H, Ameskamp C, Mejía-Rentería H, et al. Diagnostic performance of quantitative flow ratio versus fractional flow reserve and resting full-cycle ratio in intermediate coronary lesions. Int J Cardiol. 2022;362:59-67. Van Diemen PA, Driessen RS, Kooistra RA, et al. Comparison Between the Performance of Quantitative Flow Ratio and Perfusion Imaging for Diagnosing Myocardial Ischemia. JACC Cardiovasc Imaging. 2020;13(9):1976-1985. Xu B, Tu S, Song L, et al. Angiographic quantitative flow ratio-guided coronary intervention (FAVOR III China): a multicentre, randomised, sham-controlled trial. Lancet. 2021;398(10317):2149-2159 Takagi H, Ishikawa Y, Orii M, et al. Optimized interpretation of fractional flow reserve derived from computed tomography: Comparison of three interpretation methods[J]. J Cardiovasc Comput Tomogr,2019,13(2):134-141. Lee JM, Choi G, Koo BK, et al. Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics[J]. JACC Cardiovasc Imaging, 2019,12(6):1032-1043. Takagi H, Leipsic JA, McNamara N, et al. Trans-lesional fractional flow reserve gradient as derived from coronary CT improves patient management: ADVANCE registry[J]. J Cardiovasc Comput Tomogr, 2022,16(1):19-26. Yan H, Gao Y, Zhao N, et al. Change in Computed Tomography-Derived Fractional Flow Reserve Across the Lesion Improve the Diagnostic Performance of Functional Coronary Stenosis[J]. Front Cardiovasc Med, 2022,8:788703. Kołtowski Ł, Zaleska M, Maksym J, et al. Quantitative flow ratio derived from diagnostic coronary angiography in assessment of patients with intermediate coronary stenosis: a wire-free fractional flow reserve study. Clin Res Cardiol. 2018;107(9):858-867. Cortés C, Liu L, Berdin SL, et al. Agreement between Murray law-based quantitative flow ratio (μQFR) and three-dimensional quantitative flow ratio (3D-QFR) in non-selected angiographic stenosis: A multicenter study. Cardiol J. 2022;29(3):388-395. Tu S, Ding D, Chang Y, Li C, et al. Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law. Catheter Cardiovasc Interv. 2021;97 Suppl 2:1040-1047. Zuo W, Sun R, Xu Y, et al. Impact of calcification on Murray law-based quantitative flow ratio for physiological assessment of intermediate coronary stenoses [published online ahead of print, 2023 Jul 4]. Cardiol J. 2023;10.5603/CJ.a2023.0045 Tables Tables 1 to 3 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files TABLE1BaselineClinicalPatientcharacteristics.docx TABLE2BaselineLesionCharacteristics.docx Table3Diagnosticperformanceoftarget.docx Cite Share Download PDF Status: Published Journal Publication published 02 May, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 06 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviews received at journal 18 May, 2024 Reviewers agreed at journal 08 May, 2024 Reviewers invited by journal 16 Apr, 2024 Editor assigned by journal 16 Apr, 2024 Editor invited by journal 09 Jan, 2024 Submission checks completed at journal 09 Jan, 2024 First submitted to journal 08 Jan, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3844865","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266033493,"identity":"2520fb0e-4d45-408b-8375-001a44af7e9a","order_by":0,"name":"Wenhao Huang","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Huang","suffix":""},{"id":266033494,"identity":"7861b806-14c3-4d53-8e74-432a136fbc4a","order_by":1,"name":"Yajun Liu","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yajun","middleName":"","lastName":"Liu","suffix":""},{"id":266033495,"identity":"ba9eadb3-78fc-4788-b68b-dbdd70711bea","order_by":2,"name":"Qianqian Wang","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Wang","suffix":""},{"id":266033496,"identity":"6598eef3-b2be-4923-8e33-9d6f7215f2f3","order_by":3,"name":"Hongfeng Jin","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongfeng","middleName":"","lastName":"Jin","suffix":""},{"id":266033497,"identity":"a99fbd79-8b04-41f7-b3d2-2e6e75a14884","order_by":4,"name":"Yiming Tang","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Tang","suffix":""},{"id":266033498,"identity":"6f116346-6177-4d23-bc2c-b184283b6fb4","order_by":5,"name":"Jiangting Wang","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiangting","middleName":"","lastName":"Wang","suffix":""},{"id":266033499,"identity":"220fd7b8-6df1-4504-be86-38aef209ac66","order_by":6,"name":"Xiaowei Liu","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaowei","middleName":"","lastName":"Liu","suffix":""},{"id":266033500,"identity":"b053e215-1746-4e59-bd10-e27c6740ff34","order_by":7,"name":"Yitao Guo","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yitao","middleName":"","lastName":"Guo","suffix":""},{"id":266033501,"identity":"e5c8c5fb-3f31-40e7-8060-32f1b8b8bf21","order_by":8,"name":"Chen Ye","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Ye","suffix":""},{"id":266033502,"identity":"f828a699-d8ec-4c56-a3dd-a28d4e7fc151","order_by":9,"name":"Lijiang Tang","email":"","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lijiang","middleName":"","lastName":"Tang","suffix":""},{"id":266033503,"identity":"81f2cb98-8925-4937-adf2-09255d6a5cb0","order_by":10,"name":"Changqing Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYDCCAwkMDB8qbHj42RtI0MI440yajGTPARK0MPO2HbYxuOFApA6+48kPb85gO8/DcIOB8cPHHCK0SJ55Zmzxgec2D+PsBmbJmduI0GJwI4dNcobEbR5mmQNszLzEapHmMTjHwyaRQJKWhAM8PERrAfnFcsaBZB4JnoPNxPkFFGI3Pv6zs7c/3nzww0ditICABIRibCBSPULLKBgFo2AUjAIcAAB04Dil1IFGSQAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Changqing","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2024-01-08 08:14:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3844865/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3844865/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-04757-x","type":"published","date":"2025-05-02T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49436277,"identity":"277405dd-1beb-4edd-9672-8869414a7ba3","added_by":"auto","created_at":"2024-01-10 20:07:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":865974,"visible":true,"origin":"","legend":"\u003cp\u003eExample of the same vessel of the same patients, target-μFR (A) and vessel-μFR (B) was analyzed separately\u003c/p\u003e","description":"","filename":"figure.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/440f410116b776f022e84ba0.png"},{"id":49436276,"identity":"cd70c2e7-36e4-4e7b-aa8b-5c540ff68aad","added_by":"auto","created_at":"2024-01-10 20:07:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125663,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant fowchart of the study. FFR: fractional fow reserve, CAG: coronary angiography, QCA: quantitative coronary angiography, target-μFR: target-position Murray Law based quantitative flow ratio and vessel-μFR: vessel quantitative flow ratio derived from Murray Law.\u003c/p\u003e","description":"","filename":"figure.2Participantflowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/3c0633e578d7e93d6d865090.png"},{"id":49436279,"identity":"0a19340d-0ba2-4af8-afac-206946978216","added_by":"auto","created_at":"2024-01-10 20:07:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1392769,"visible":true,"origin":"","legend":"\u003cp\u003eA representative example of target-μFR, vessel-μFR, and FFR measurements. (A)Wire-based Fractional flow reserve (FFR) = 0.77. (B) Vessel quantitative flow ratio derived from Murray Law (vessel-μFR) = 0.75. (C) Target-μFR: target-position Murray Law based quantitative flow ratio (target-μFR) = 0.81.\u003c/p\u003e","description":"","filename":"figure.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/b6621a410a45a423b3b7e2e8.png"},{"id":49436282,"identity":"da026626-9531-447a-9479-33fd699ed7b9","added_by":"auto","created_at":"2024-01-10 20:07:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":140353,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations and agreements among target-μFR, vessel-μFR, and FFR. (A) Correlation between target-μFR and FFR. (B) Agreement between vessel-μFR and FFR. The mean value of target-μFR - FFR = 0.02, the upper limit of agreement = 0.10, and the lower limit of agreement =-0.07. (C) Correlation between vessel-μFR and FFR. (D) Agreement between vessel-μFR and FFR. Mean value of vessel-μFR - FFR = 0.01, the upper limit of agreement = 0.11, and the lower limit of agreement = –0.09.\u003c/p\u003e","description":"","filename":"figure.4correlationsandagreements.png","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/3ebdaf4c30f013f652537ea1.png"},{"id":49436281,"identity":"c44920ed-f42c-470a-bcf4-871ace04bb70","added_by":"auto","created_at":"2024-01-10 20:07:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44866,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic curves of target-μFR and vessel-μFR in detecting FFR≤0.80.\u003c/p\u003e","description":"","filename":"figure.5ROC.png","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/c55a10ffa3383bf73f835ad7.png"},{"id":49436278,"identity":"b74bf23d-30e1-483b-aef2-41bd8489a613","added_by":"auto","created_at":"2024-01-10 20:07:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70393,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity, specificity and Youden index for different cutoff values of leison-QFR to predict FFR≤0.80. The optimal target-μFR cutoff vlaue for predicting FFR≤0.80 was 0.83.\u003c/p\u003e","description":"","filename":"figure.6CUTOFF.png","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/926e8a543063de6987d19997.png"},{"id":81987834,"identity":"2b01dfd0-ca3a-4b28-9591-7d8a2719be7b","added_by":"auto","created_at":"2025-05-05 16:06:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3578144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/7675b81f-6b5c-4451-9ba3-6658f7c547a7.pdf"},{"id":49437159,"identity":"80ee2409-e259-49d2-a39b-34ef96f7d2be","added_by":"auto","created_at":"2024-01-10 20:23:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12694,"visible":true,"origin":"","legend":"","description":"","filename":"TABLE1BaselineClinicalPatientcharacteristics.docx","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/0d49275e4e9b5f204f105df5.docx"},{"id":49436280,"identity":"b5345f7c-6d9d-4aec-958e-c3143a073c6a","added_by":"auto","created_at":"2024-01-10 20:07:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13446,"visible":true,"origin":"","legend":"","description":"","filename":"TABLE2BaselineLesionCharacteristics.docx","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/e49cbbba37a4d9d559670264.docx"},{"id":49436954,"identity":"eea818f9-bf26-457c-9f2a-25b9d01b4ddd","added_by":"auto","created_at":"2024-01-10 20:15:41","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11898,"visible":true,"origin":"","legend":"","description":"","filename":"Table3Diagnosticperformanceoftarget.docx","url":"https://assets-eu.researchsquare.com/files/rs-3844865/v1/8c94b7a4fe63559903f2c952.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic Performance of Target-position Murray Law based Quantitative Flow Ratio (target-μFR) vs Vessel-μFR in Patients with stable Coronary Artery Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe functional evaluation of coronary physiology plays a vital role in guiding diagnosis and treatment strategies in patients with known or suspected coronary artery diseases (CAD). The invasive fractional flow reserve (FFR) has the highest recommendation (class IA) for the evaluation of CAD in guidelines[1.2].However, the adoption of FFR in daily clinical practice has been limited because of the invasive of the procedure, requirement of pressure wire, and the administration of hyperemic agents [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The virtual FFR from coronary artery imaging may increase the utility of FFR assessment in clinical practice. Currently, more attention has been paid in these novel applications of virtual FFR derived from coronary artery imaging, such as quantitative flow ratio (QFR), intravascular ultrasound-derived fractional flow fraction (IVUS-FFR) and coronary computed tomography-derived fractional flow reserve (CT-FFR), without pressure wire and administration of hyperemic agents[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].QFR has improved the diagnostic performance for identifying hemodynamically significant coronary stenosis compared with the assessment of coronary lesion based on QCA[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Accumulating evidence has proved the high correlation of QFR with FFR in the diagnosing ischemia and to aid clinical decision-making[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Quantitative flow ratio derived from Murray Law (\u0026micro;FR) is a novel technique for fast computation of FFR from coronary agiography (CAG) and estimates the pressure drop due to coronary stenosis according to semiautomatic delineation of target vessels and FFR simulation from single-angle[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe accuracy of virtual FFR derived from coronary imaging depends importantly on the measurement location of the coronary artery tree. Previous study showed that lesion-CT-FFR (defined the value at approximately 2-3cm distal to the target lesion of the coronary artery) can reclassify positive patients defined by the vessel-derived CT-FFR or lowest CT-FFR (defined the value at the distal to the target lesion of the coronary artery), and that lesion CT-FFR has higher diagnostic performance than vessel CT-FFR[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The outcomes after 60-day follow up indicated that revascularization rates were significantly higher in lesion CT-FFR positive than those with vessel CT-FFR positive (53% vs 44%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that clinicians should not only consider the vessel CT-FFR value when making clinical decisions[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Recently, more and more studies show that the lesion CT-FFR has higher diagnostic accuracy than vessel CT-FFR [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the measurement of the \u0026micro;FR was always stay at the vessel level (vessel-\u0026micro;FR, defined the value at the distal to the target lesion of the coronary artery) in most of current studies, while whether vessel-\u0026micro;FR is different from the target-position Murray Law based quantitative flow ratio (targer-\u0026micro;FR, defined the value at the position recorded during FFR evaluation procedure) has less been reported. As in the case of CT-FFR, we speculate that target-\u0026micro;FR may be more superior to the diagnostic performance than the vessel-\u0026micro;FR and may better guide catheterization laboratory revascularization strategies.\u003c/p\u003e \u003cp\u003eIn this study, we aim to compare with the diagnostic performance of target-\u0026micro;FR and vessel-\u0026micro;FR using the FFR as reference standard. This study may provide more evidence for the clinical usage of QFR in the diagnosis and treatment of coronary artery disease (CAD)..\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA retrospective, single-center, observational study was conducted from January 2021 to March 2023, including consecutive patients with CAD who underlying FFR assessment were eligible for enrollment. The inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) patients with suspected or known CAD; and (3) at least one lesion with 30\u0026ndash;80% diameter stenosis (DS%) based on visual estimation. The exclusion criteria were as follows: (1) angiographic evidence of thrombi-containing lesions; (2) severe valvular heart diseases; (3) left ventricle ejection fraction\u0026thinsp;\u0026lt;\u0026thinsp;30%; (4) angiographic features limiting QFR computation (eg, left main or ostial right coronary artery ongoing ventricular arrhythmias, and significant and persistent tachycardia); (5) significant foreshortening or vessel overlapping; (6) previous coronary artery bypass grafting; (7) inadequate contrast flush; (8) deep catheter intubation into the lesion precluding complete visualization of stenosis; (9) severely calcified vessels; or (10) inconsistent image format.\u003c/p\u003e \u003cp\u003e The study was conducted in compliance with the Declaration of Helsinki, and was approved by the Ethics Review Committee of Zhejiang Hospital. Individual informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eQCA and measurement of physiological indices\u003c/h2\u003e \u003cp\u003eThe radiographic system Allura Xper FD20/10 (PHILIPS Medical Systems, the Netherlands) was used for the angiographic imaging at a rate of 15 frames/s. The contrast medium was injected at a stable rate of approximately 4 mL/s using a pump. The QCA was performed using the Angiogram QCA software (Allura Xper FD20/10; PHILIPS Medical Systems, The Netherlands). A coronary pressure wire (St. Jude Medical, St. Paul, Minnesota, USA) was used to calculate FFR with the pressure sensor positioned at 2\u0026ndash;3 cm distal to the target lesion of the coronary artery. Before placement, the pressure wire was calibrated and equalized, and intravenous adenosine triphosphate concentration was 150\u0026ndash;180 g/kg/min to induce maximum hyperemia of the coronary microvascular system. Simultaneously, the distal and proximal coronary artery pressures at the pressure sensor (Pd) and coronary ostium (Pa) were recorded. The pressure sensor was then pulled back to the proximal end to assess or correct pressure drift. The FFR was determined by dividing Pd by Pa. Further analysis was performed at the core laboratory using all ICA and FFR data. Thus, a standardized radiographic system, pressure wire, and software were used, and strict protocols were followed for data collection and analysis to ensure accuracy and reliability.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u0026micro;FR measurement\u003c/h2\u003e \u003cp\u003eIn this research, the \u0026micro;FR with simultaneous QCA analysis independently used the calculated software (AngioPlus Core, Pulse Medical Imaging Technology Co., Ltd., Shanghai, China). The calculation of \u0026micro;FR, which semiautomatic delineation of vessel contours and FFR simulation from single-angle, was enable by this artificial intelligence-based algorithm. The key frame was first selected from an optimal CAG image which had a sharp lumen contour at the stenosis segment. Then the vessel contours were automatically delineated, and the reference lumen diameter was reconstructed according to Murray Law of blood flow distribution. The proximal and distal reference lumen diameter could be manually adjusted as needed. And an appropriate manual correction was allowed under certain circumstances, such as stenosis or individual contours with wrong automatic recognition. Finally, both \u0026micro;FR of main and side-branches coronary has been calculated. The details of the vessel-\u0026micro;FR (value at the distal to the target lesion of the coronary artery) computation method were reported in previous studies[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Based on previous studies, we use the vessel-\u0026micro;FR\u0026thinsp;\u0026le;\u0026thinsp;0.80 as cutoff value for predicting ischemia myocardial in our research[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Then the target-\u0026micro;FR values were obtain at the position recorded during FFR evaluation procedure (the position was located approximately 2-3cm distal to the target lesion of the coronary artery). Both of vessel-\u0026micro;FR and target-\u0026micro;FR were calculated independently by two senior physicians who were unaware of the clinical data. If the two physicians fell into a disagreement, a third, more senior associate chief physician or chief physician would re-calculate and determine it[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. (Fig.\u0026nbsp;1)\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure.1\u003c/b\u003e Example of the same vessel of the same patients, target-\u0026micro;FR (A) and vessel-\u0026micro;FR (B) was analyzed separately\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous and binary variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and percentages, respectively. Pearson\u0026rsquo;s or Spearman\u0026rsquo;s correlation coefficients were used to quantify the correlations. Bland\u0026ndash;Altman plots were used to assess the agreements, which displayed the differences between each pair of measurements versus their mean values with reference lines for the mean difference of all paired measurements. The agreement limits were defined as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 SD of the absolute difference. To predict functionally significant stenosis (defined as FFR\u0026thinsp;\u0026le;\u0026thinsp;0.80), sensitivity, specificity, and the Youden index (defined as [sensitivity/100] + [specificity/100]\u0026thinsp;\u0026minus;\u0026thinsp;1) were calculated for cutoff values of target-\u0026micro;FR. To assess the area under the curve (AUC) of target-\u0026micro;FR and vessel-\u0026micro;FR, receiver operating characteristic (ROC) curve analysis was performed. All statistical analyses were performed using MedCalc (version 19.0, MedCalc Software, Ostend, Belgium), and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was defined as statistically significant.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline clinical and lesion characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the research period, six hundreds and fifty-six patients (685 lesions) with known or suspected coronary artery disease were screened for this retrospective analysis between January 2021 to March 2023. A total of 161 patients (190 lesions) underwent quantitative coronary angiography (QCA) and FFR evaluations in our catheter lab. For the excluded patients, five patients (12 lesions) had the lesion in left main coronary or ostial right coronary artery, seven patients (12 lesions) had significant foreshortening or vessel overlapping, four patients (9 lesions) had severely calcified vessels, four patients (5 lesions) had inadequate contrast flush, and four patients (10 lesions) had inconsistent image format. In the fnal analysis, 137 patients (146 vessels) were included in this study (Fig.\u0026nbsp;2). The mean age was 64.5±10.5 years, and\u0026nbsp;95 (69.3%)\u0026nbsp;were male. Approximately\u0026nbsp;44.5%\u0026nbsp;of patients had the history of smoking.\u0026nbsp;The mean left ventricular ejection fraction was\u0026nbsp;65.8±7.58%. The baseline clinical patient characteristics are listed in Table1. The target vessels included\u0026nbsp;92 (63.0%)\u0026nbsp;left anterior descending arteries(LAD),\u0026nbsp;18 (12.3%)\u0026nbsp;left circumfex arteries(LCX), and\u0026nbsp;36 (24.7%)\u0026nbsp;right coronary arteries(RCA). The mean values of\u0026nbsp;target-μFR,\u0026nbsp;vessel-μFR, and FFR were\u0026nbsp;0.83±0.101,\u0026nbsp;0.83±0.098, and\u0026nbsp;0.82±0.098, respectively. The baseline lesion characteristics are listed in Table\u0026nbsp;2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure.2\u003c/strong\u003e Participant fowchart of the study. FFR: fractional fow reserve, CAG: coronary angiography, QCA: quantitative coronary angiography, target-μFR: target-position Murray Law based quantitative flow ratio and vessel-μFR: vessel quantitative flow ratio derived from Murray Law.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;TABLE1 Baseline Clinical Patient characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValues are`Mean±SD or n (%). BMI: Body mass index, CAD: coronary artery disease, CABG: coronary artery bypass grafting PCI: percutaneous coronary intervention, MI: myocardial infarction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE2 Baseline Lesion Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValues are`Mean±SD, Mean (P25,P75) or n (%). LAD: lanterior descending coronary artery, LCX: left circumflex artery, RCA: right coronary artery. QCA:\u0026nbsp;quantitative coronary angiography. Target-μFR: target-position Murray Law based quantitative flow ratio. vessel-μFR: Vessel quantitative flow ratio derived from Murray Law.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of the correlations and agreements among target-\u003c/strong\u003e\u003cstrong\u003eμ\u003c/strong\u003e\u003cstrong\u003eFR, vessel-\u003c/strong\u003e\u003cstrong\u003eμ\u003c/strong\u003e\u003cstrong\u003eFR, and invasive-FFR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure. 3\u0026nbsp;illustrates a visual representation of\u0026nbsp;target-μFR,\u0026nbsp;vessel-μFR, and FFR measurements. Figure.\u0026nbsp;4\u0026nbsp;shows the correlation and agreement among these measurements. The results demonstrate that both\u0026nbsp;target-μFR\u0026nbsp;and\u0026nbsp;vessel-μFR\u0026nbsp;are highly correlation\u0026nbsp;with FFR, with R of 0.87 (P\u0026lt;0.001) and 0.90 (P\u0026lt;0.0001), respectively. A great agreement is demonstrated by both\u0026nbsp;target-μFR\u0026nbsp;and\u0026nbsp;vessel-μFR\u0026nbsp;with FFR, with similar mean diferences of 0.02±0.045 and 0.01±0.050 respectively. (Fig.\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure.3\u003c/strong\u003e A representative example of target-μFR, vessel-μFR, and FFR measurements. (A)Wire-based Fractional flow reserve (FFR) =\u0026nbsp;0.77. (B) Vessel quantitative flow ratio derived from Murray Law (vessel-μFR) = 0.75. (C) Target-μFR: target-position Murray Law based quantitative flow ratio (target-μFR) =\u0026nbsp;0.81.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure. 4\u003c/strong\u003e\u0026nbsp; Correlations and agreements among target-μFR, vessel-μFR, and FFR. (A) Correlation between target-μFR\u0026nbsp;and FFR. (B) Agreement between vessel-μFR\u0026nbsp;and FFR. The mean value of target-μFR\u0026nbsp;- FFR = 0.02, the upper limit of agreement = 0.10, and the lower limit of agreement =-0.07. (C) Correlation between vessel-μFR\u0026nbsp;and FFR. (D) Agreement between vessel-μFR\u0026nbsp;and FFR. Mean value of vessel-μFR\u0026nbsp;- FFR = 0.01, the upper limit of agreement = 0.11, and the lower limit of agreement =\u0026nbsp;–0.09.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic performance of target-\u003c/strong\u003e\u003cstrong\u003eμFR\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;adn vessel-\u003c/strong\u003e\u003cstrong\u003eμFR\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for predicting invasive-FFR\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e≤\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;0.80\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure. 5\u0026nbsp;presents the receiver operating characteristic (ROC) curves for\u0026nbsp;target-μFRand\u0026nbsp;vessel-μFR\u0026nbsp;for FFR≤0.80 predictions. The AUC for\u0026nbsp;target-μFR\u0026nbsp;was similar to that for vessel (0.0937 vs. 0.0936). Figure. 6\u0026nbsp;illustrates the sensitivity, specifcity, and Youden index for cutoff values of lesion in predicting FFR≤0.80. The optimal cutoff value for\u0026nbsp;target-μFR\u0026nbsp;to predict FFR≤0.80 was 0.83. This demonstrated the superior diagnostic ability of\u0026nbsp;target-μFR\u0026nbsp;and\u0026nbsp;vessel-μFR\u0026nbsp;in identifying whether stenoses can lead to ischemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure. 5\u003c/strong\u003e The receiver operating characteristic curves of target-μFR\u0026nbsp;and vessel-μFR in detecting FFR≤0.80.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure. 6\u003c/strong\u003e Sensitivity, specificity and Youden index for different cutoff values of leison-QFR to predict FFR≤0.80. The optimal target-μFR\u0026nbsp;cutoff vlaue for predicting FFR≤0.80 was 0.83.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic performance of\u0026nbsp;target-\u003c/strong\u003e\u003cstrong\u003eμFR\u003c/strong\u003e\u003cstrong\u003e≤\u003c/strong\u003e\u003cstrong\u003e0.83 and\u0026nbsp;vessel-\u003c/strong\u003e\u003cstrong\u003eμFR\u003c/strong\u003e\u003cstrong\u003e≤\u003c/strong\u003e\u003cstrong\u003e0.80 for\u0026nbsp;predicting FFR\u003c/strong\u003e\u003cstrong\u003e≤\u003c/strong\u003e\u003cstrong\u003e0.80\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic performance of\u0026nbsp;target-μFR≤0.83 and\u0026nbsp;vessel-μFR≤0.80 for predicting FFR≤0.80 is presented in Table 3. Among all 146 lesions, using FFR as the reference standard,\u0026nbsp;target-μFR\u0026nbsp;had 53 true positives (TP), 81 true negatives (TN), 4 false positive (FP), and 8 false negative (FN), while\u0026nbsp;vessel-μFR\u0026nbsp;had 46 TP, 83 TN, 2 FP, and 15 FN.\u0026nbsp;target-μFR\u0026nbsp;had a false discovery rate of 13.11% (positive predict value 86.89% ) and a false omission rate of 14.71% (negative predict value 95.29%) compared with FFR. This indicates that 7.02% of stenosis were misclassified using target-μFR\u0026nbsp;compared to FFR. On the other hand, the vessel-μFR\u0026nbsp;also showed a relatively good diagnostic performance, 13.6% were misclassified using vessel-μFR\u0026nbsp;compared to FFR. The diagnostic accuracy of target-μFR≤0.83 for predicting FFR≤0.80 was 92.98% (95% confidence interval [CI]: 83.00% to 98.06%), while that of vessel-μFR\u0026nbsp;was 86.40% (95% CI: 75.00% to 94.00% ). A good diagnostic performance indicates that an accurate assessment of coronary stenosis is feasible. What’s more, the target-μFR’s diagnostic performance (Youden index 0.840) was slightly better than the traditional QFR calculation (vessel-μFR, Youden index 0.750).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Diagnostic performance of t\u003c/strong\u003e\u003cstrong\u003earget-μFR and vessel-μFR\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for predicting FFR≤0.80\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValue are n(95%confidence interval). Abbreviations as in Table 2.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we compared the diagnostic performance between target-\u0026micro;FR and vessel-\u0026micro;FR using the FFR\u0026thinsp;\u0026le;\u0026thinsp;0.80 as reference standard. The primary study findings are summarized as follows: 1) the \u0026micro;FR is a novel physiological assessment methods which estimates the pressure drop due to coronary stenosis according to semiautomatic delineation of target vessels and FFR simulation from single-angle. 2) both target-\u0026micro;FR and vessel-\u0026micro;FR demonstrated high correlations and great agreements with FFR; 3) the ability of target-\u0026micro;FR defining hemodynamic significant of coronary stenosis was similar to vessel-\u0026micro;FR; 4) diagnostic performance of target-\u0026micro;FR was slightly better than that of vessel-\u0026micro;FR;5) the selection of the measurement location has less influence on the accuracy of \u0026micro;FR. Based on these findings, it could improve the calculation algorithm for enhancing the time-efficient and showed the potential in the coronary imagine and virtual physiological evaluation of CAD. The ability of \u0026micro;FR highlights that by integration of the imaging information in order to enable a comprehensive assessment of the CAD.\u003c/p\u003e \u003cp\u003eDEFER, FAME, FAME II and FAME III establish FFR as the \"gold standard\" of coronary physiology for assessing coronary artery stenosis, treatment plan formulation and evaluation of treatment effect[\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, as an invasive method, the application of FFR requires expensive equipment and has potentially procedure-related complications, such as non-fatal myocardial infarction, cerebrovascular accident, and has been limited in clinical practice because of the invasive of the procedure, requirement of pressure wire, the administration of hyperemic agents and so on[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To solve these limitations and reduce the complications, the QFR had been developed which is a virtual FFR technique derived from coronary angiography. And QFR derived from Murray Law as a novel angiographic-based method could enable fast computation of FFR, which provides an avenue for determining the most appropriate therapy for the intermediate lesions.\u003c/p\u003e \u003cp\u003eA large number of clinical studies have confirmed the accuracy of QFR in assessing coronary artery function. In FAVOR Pilot Study, the fix-flow QFR (fQFR), contrast-flow QFR (cQFR) and adenosine-flow QFR (aQFR) was compared with the gold standard FFR for evaluate the capability of those QFR in predicting coronary stenosis. The results confirm that fQFR, cQFR and aQFR had shown the great agreement and diagnostic performance (accuracy 80%, 86%, and 87%) for predicting ischemia myocardial[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Then, in the FAVOR II China, the sensitivity and specificity in identifying hemodynamically significant stenosis were evidently higher for QFR than for QCA (94.6% vs. 62.5%; 91.7% vs. 58.1%). The FAVOR II China also revealed that vessel-level QFR had a high diagnostic accuracy of 93.3%[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In a large study of FAVOR II E/J, the good diagnostic performance of QFR assessed the degree of coronary stenosis (accuracy 86.3%, specificity 86.9%, sensitivity 86.5%, AUC 0.92) and evaluated the calculation time of QFR and FFR. Furthermore, the time to complete QFR (5min) was significantly shorter than the time to complete FFR (7min) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The FAVOR II China and FAVOR II E/J had proved that the diagnostic accuracy of QFR at both the patients and vessels level was better than QCA in the assessment of the relevance of functional stenoses.In a meta-analysis of 16 high quality researches comparing FFR and QFR, QFR has demonstrated good positive and significant negative predictive values in confirming the relevance of the ischemia myocardial on the basis of the FFR cut-off \u0026le;\u0026thinsp;0.80[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Subsequently, Wienemann etal further verified the diagnostic performance of cQFR (sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 82%, 95%, 97%, 92% and 91%, respectively). The area under the curve shown for cQFR is greater than that for fQFR and RFR (0.938 vs. 0.891,0.869, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The good diagnostic performance of cQFR was maintained in different clinical subpopulations (including gender, aortic stenosis and atrial fibrillation, etc.) and different anatomical subpopulations (including focal and non-focal lesions, etc.)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In a head-to-head study, QFR showed good agreement with QFR compared with SPECT and PET. Meanwhile, the accuracy of QFR was 88%, 82% for SPECT and 78% for PET[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. What\u0026rsquo;s more, the QFR derived from CAG is the only functional angiography system for which the value in clinical practice has been assessed in a randomised clinical trial. The patients, who enrolled in FAVOR III China, with at least one 50\u0026ndash;90% coronary stenosis underwent QFR-guide strategies (if QFR\u0026thinsp;\u0026le;\u0026thinsp;0.80) or angiographic-based strategies. After 1-year of follow up, patients randomised to the QFR-guide strategies demonstrated better outcomes driven by fewer myocardial infarctions and ischemia-driven revascularisations[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Based on those studies, QFR has demonstrated good diagnostic accuracy in detecting myocardial ischemia. Meanwhile, QFR could more quickly and conveniently calculate virtual FFR after CAG without any intervention operations, further providing clinical support for revascularization strategies.\u003c/p\u003e \u003cp\u003eBut none of the QFR mentioned above studies is a lesion-specific QFR. In previous studies, they demonstrated that the choice of virtual FFR measurement locations is particular importance when identifying ischemic lesions or guiding treatment strategies[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Series studies demonstrated that lesion-specific FFR, such as lesion-specific CT-FFR, can reclassify positive patients defined by the vessel-derived FFR value, and that lesion-specific FFR has higher diagnostic performance than vessel-derived FFR[\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The possiable causes of the above phenomennon were as follows: 1) the virtual FFR measurement at far distal segments may overeatimate coronary ischemia, 2) these differences between vessel territories in pressure gradients for segments 1\u0026ndash;2 cm distal to the stenosis versus far distal segments relate to the larger territory of perfused myocardium, 3) the virtual FFR were only assessed in the main coronary arteries, which may have disregarded the impact of collateral stenosis on myocardial ischemia[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Then, Kołtowski Ł et al analysised the diagnostic performance of index QFR, vessel QFR (assessemnt for entire segmented vessel) and lesion QFR (assessment for the target lesion) to identify the best measurement location for optimal accuracy of QFR. Thie researche demostrated the index QFR value which obtained at the pressure transducer position (R 0.85, accuracy 85.4%, AUC 0.94) had better diagnostic performance than vessel QFR (R 0.78, accuracy 78.5%, AUC 0.90) and lesion QFR (R 0.70, accuracy 76.1%, AUC 0.82)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Of note, the \u0026micro;FR had been showed gteat agreement and correlation with standard three-dimensional quantitative flow ratio (R 0.996)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. All the above results are comparable to those shown in our analysis.\u003c/p\u003e \u003cp\u003eIn previous studies, the \u0026micro;FR demonstrated powerful and superior diagnostic performance for lesion-specific ischemia compared with angiography alone regardless of coronary calcification[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Hence, this paper explores whether target-\u0026micro;FR could further improve the diagnostic ability of myocardial ischemic by comparing the diagnostic performance of target-\u0026micro;FR and vessel-\u0026micro;FR. The diagnostic performance (accuracy 86.40% ,sensitivity 86.44%, specificity 88.51%, and Youden index 0.750) and AUC (0.936) of vessel-\u0026micro;FR were similar to those reported in previous studies. Then, those indexes of target-\u0026micro;FR (accuracy 92.98%,sensitivity 92.98%, specificity 91.01%, Youden index 0.840, and AUC 0.937) were slightly better than vessel-\u0026micro;FR and previous research. At the same time, we found the calculation time of target-\u0026micro;FR (4-5min) and vessel-\u0026micro;FR (4-5min) was also similar to the QFR reported in previous papers. Becaose of that, the \u0026micro;FR could assess the degree of coronary stenosis which is a time-efficient and accurate method, the visualized anatomic geometry of the coronary artery can provide guidance for subsequent therapeutic regimens. By the way, the \u0026micro;FR calcualted the pressure loss by the frictional loss along the lesion entrance and stenotic segment as well as the inertial loss stemming from the sudden expansion of the flow as it emerged from the stenosis were calculated, based on the stenosis geometry and the hyperemic flow rate. What\u0026rsquo;s more, based on the U-Net architecture, Murray Law and artificial intelligence, \u0026micro;FR automatically outlines the lumen of the target vessels and their collaterals through artificial intelligence[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. All in all, we suggest the accuracy of \u0026micro;FR have almost less influence by the selection of the remote measurement locations. At the same time, \u0026micro;FR behaved similarly well in sexes and has great diagnostic performance, indicating its potential as a reliable wireless tool for identifying functional ischemia[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, our study had several limitations. First, it was a single-center and retrospective study with a small sample size. This might have introduced selection bias even though consecutive patients were included. The limited number of enrolled patients due to the low adoption rate of patients undergoing FFR in clinical practice also affected the statistical efficiency of the study. Secondly, not all the vessels were interrogated for the enrolled patients. The vessels with diameter stenosis\u0026thinsp;\u0026lt;\u0026thinsp;30% or \u0026gt;\u0026thinsp;90% were not assessed because performing physiological assessments in such lesions was unnecessary. Thirdly this is a retrospective analysis in which one-third of the data were excluded because the QFR assessment was not applicable, the study should more likely be viewed as a hypothesis-generating study, and further prospective studies would provide more evidence. The availability of QFR can be improved by requiring careful attention to the projection angle and location of the target lesions in coronary angiography; however, the extent to which this can be improved remains to be assessed. Forth, target-\u0026micro;FR and vessel-\u0026micro;FR computation require automatic reconstruction of 3D anatomical models of coronary vessels, and further studies should be consider that analyze the impact of anatomical features on diagnostic accuracy in target vascular lesions. Fifth, there may be inter-operator differences and previous PCI operation effect in the target-\u0026micro;FR and vessel-\u0026micro;FR calculation process, so further evidences from larger studies are needed. Sixth, the selection of the measurement location depends on the location recorded during the FFR evaluation procedure. Influenced by the real world, some of the measurement locations cannot be accurately positioned ar rhe 2-3cm distal to the target vessels. Hence, further large-sample. Multicenter, prospective, randomized sthudies are needed to further confirm the feasibility of target-\u0026micro;FR in clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe target-\u0026micro;FR has the similar diagnostic performance with vessel-\u0026micro;FR. The accuracy of \u0026micro;FR does not seem to be affected by the selection of the measurement point. Both of the virtual model could be used as computations tools for diagnosing ischemia and to aid clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Committee of Zhejiang Hospital and conducted according to the principles of the Declaration of Helsinki. Since this clinical study was a retrospective analysis of the information from previous cases, without direct contact with the subjects and subject privacy protection was ensured, the risk borne by the subjects was not greater than the minimum risk. The Ethics Review Committee of Zhejiang Hospital agreed to exempt informed consent after review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the Major medical and health science and technology plan of Zhejiang Province (grant No. WKJ-ZJ-1913), the Natural Science Foundation of Zhejiang Province (grant No. LY21H020002),and the Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province(grant no. 2023ZL223). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design of the work: all authors; acquisition and assembly of data: all authors; analysis, computation and interpretation of data: Wenhao Huang, Qianqian Wang and Yajun Liu; drafting the work: Wenhao Huang and Lin Yang; revising it critically for important intellectual content: Changqing Du and Lijiang Tang; Final approval of the manuscript: all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate very much for all the healthcare staff and engineers\u0026nbsp;who assisted in the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWriting Committee Members, Lawton JS, Tamis-Holland JE, et al. 2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2022;79(2):197-215.\u003c/li\u003e\n \u003cli\u003eNeumann FJ, Sousa-Uva M, Ahlsson A, et al. 2018 ESC/EACTS Guidelines on myocardial revascularization. Eur Heart J. 2019;40(2):87\u0026ndash;165\u003c/li\u003e\n \u003cli\u003ePijls NH, De Bruyne B, Peels K, et al. Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. N Engl J Med. 1996;334(26):1703\u0026ndash;1708.\u003c/li\u003e\n \u003cli\u003eZimmermann FM, Ferrara A, Johnson NP, et al. Deferral vs. performance of percutaneous coronary intervention of functionally non-significant coronary stenosis: 15-year follow-up of the DEFER trial. Eur Heart J. 2015;36(45):3182\u0026ndash;3188. .\u003c/li\u003e\n \u003cli\u003eN. Pijls,\u0026nbsp;Bernard De,\u0026nbsp;Mamdouh El, et al. Fractional Flow Reserve: The Ideal Parameter for Evaluation of Coronary, Myocardial, and Collateral Blood Flow by Pressure Measurements at PTCA. Journal of Interventional Cardiology, 1993, 6(4) : 331\u0026ndash;344.\u003c/li\u003e\n \u003cli\u003eHuang W, Zhang J, Yang L, et al. Accuracy of intravascular ultrasound-derived virtual fractional flow reserve (FFR) and FFR derived from computed tomography for functional assessment of coronary artery disease. Biomed Eng Online. 2023;22(1):64.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCort\u0026eacute;s C, Carrasco-Moraleja M, Aparisi A, et al. Quantitative flow ratio-Meta-analysis and systematic review. Catheter Cardiovasc Interv. 2021;97(5):807-814.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTomaniak M, Serruys PW. Combining anatomy and physiology: New angiography-based and computed tomography coronary angiography-derived fractional flow reserve indices. Cardiol J. 2020;27(3):225\u0026ndash;229.\u003c/li\u003e\n \u003cli\u003eTu S, Westra J, Yang J, et al. Diagnostic Accuracy of Fast Computational Approaches to Derive Fractional Flow Reserve From Diagnostic Coronary Angiography: The International Multicenter FAVOR Pilot Study. JACC Cardiovasc Interv. 2016;9(19):2024-2035.\u003c/li\u003e\n \u003cli\u003eXu B, Tu S, Qiao S, et al. Diagnostic Accuracy of Angiography-Based Quantitative Flow Ratio Measurements for Online Assessment of Coronary Stenosis. J Am Coll Cardiol. 2017;70(25):3077-3087.\u003c/li\u003e\n \u003cli\u003eWestra J, Andersen BK, Campo G, et al. Diagnostic Performance of In-Procedure Angiography-Derived Quantitative Flow Reserve Compared to Pressure-Derived Fractional Flow Reserve: The FAVOR II Europe-Japan Study. J Am Heart Assoc. 2018;7(14):e009603.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTu S, Ding D, Chang Y, Li C, et al. Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law. Catheter Cardiovasc Interv. 2021;97 Suppl 2:1040-1047.\u003c/li\u003e\n \u003cli\u003eKueh SH, Mooney J, Ohana M, et al. Fractional flow reserve derived from coronary computed tomography angiography reclassification rate using value distal to lesion compared to lowest value[J]. J Cardiovasc Comput Tomogr, 2017,11(6):462\u0026ndash;467.\u003c/li\u003e\n \u003cli\u003e.Omori H, Hara M, Sobue Y, et al. Determination of the optimal measurement point for fractional flow reserve derived from CTA using pressure wire assessment as reference[J]. AJR Am J Roentgenol,2021,216(6):1492\u0026ndash;1499.\u003c/li\u003e\n \u003cli\u003eCami E, Tagami T, Raff G, et al. Assessment of lesion-specific ischemia using fractional flow reserve (FFR) profiles derived from coronary computed tomography angiography (FFRCT) and invasive pressure measurements (FFRINV): Importance of the site of measurement and implications for patient referral for invasive coronary angiography and percutaneous coronary intervention[J]. J Cardiovasc Comput Tomogr,2018,12(6):480\u0026ndash;492.\u003c/li\u003e\n \u003cli\u003eGuan C, Geng L, Zhang R, et al. Long-term prognostic value of dynamic function assessment of intermediate coronary lesion with computational physiology. Catheter Cardiovasc Interv. 2022;99 Suppl 1:1386-1394.\u003c/li\u003e\n \u003cli\u003eTanigaki T, Emori H, Kawase Y, et al. QFR Versus FFR Derived From Computed Tomography for Functional Assessment of Coronary Artery Stenosis. JACC Cardiovasc Interv. 2019;12(20):2050\u0026ndash;2059.\u003c/li\u003e\n \u003cli\u003eWestra J, Tu S, Campo G, et al. Diagnostic performance of quantitative flow ratio in prospectively enrolled patients: An individual patient-data meta-analysis. Catheter Cardiovasc Interv. 2019;94(5):693-701\u003c/li\u003e\n \u003cli\u003eWestra J, Sejr-Hansen M, Koltowski L, et al. Reproducibility of quantitative flow ratio: the QREP study. EuroIntervention. 2022;17(15):1252-1259.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZimmermann FM, Ferrara A, Johnson NP, et al. Deferral vs. performance of percutaneous coronary intervention of functionally non-significant coronary stenosis: 15-year follow-up of the DEFER trial. Eur Heart J. 2015;36(45):3182-3188.\u003c/li\u003e\n \u003cli\u003eTonino PA, Fearon WF, De Bruyne B, et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol. 2010;55(25):2816-2821.\u003c/li\u003e\n \u003cli\u003eFearon WF, Nishi T, De Bruyne B, et al. Clinical Outcomes and Cost-Effectiveness of Fractional Flow Reserve-Guided Percutaneous Coronary Intervention in Patients With Stable Coronary Artery Disease: Three-Year Follow-Up of the FAME 2 Trial (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation). Circulation. 2018;137(5):480-487.\u003c/li\u003e\n \u003cli\u003eFearon WF, Zimmermann FM, Ding VY, et al. Quality of Life After Fractional Flow Reserve-Guided PCI Compared With Coronary Bypass Surgery. Circulation. 2022;145(22):1655-1662.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhuang B, Wang S, Zhao S, et al. Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis. Eur Radiol. 2020;30(2):712-725.\u003c/li\u003e\n \u003cli\u003eWestra J, Andersen BK, Campo G, et al. Diagnostic Performance of In-Procedure Angiography-Derived Quantitative Flow Reserve Compared to Pressure-Derived Fractional Flow Reserve: The FAVOR II Europe-Japan Study. J Am Heart Assoc. 2018;7(14):e009603.\u003c/li\u003e\n \u003cli\u003eWienemann H, Ameskamp C, Mej\u0026iacute;a-Renter\u0026iacute;a H, et al. Diagnostic performance of quantitative flow ratio versus fractional flow reserve and resting full-cycle ratio in intermediate coronary lesions. Int J Cardiol. 2022;362:59-67. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVan Diemen PA, Driessen RS, Kooistra RA, et al. Comparison Between the Performance of Quantitative Flow Ratio and Perfusion Imaging for Diagnosing Myocardial Ischemia. JACC Cardiovasc Imaging. 2020;13(9):1976-1985.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eXu B, Tu S, Song L, et al. Angiographic quantitative flow ratio-guided coronary intervention (FAVOR III China): a multicentre, randomised, sham-controlled trial. Lancet. 2021;398(10317):2149-2159\u003c/li\u003e\n \u003cli\u003eTakagi H, Ishikawa Y, Orii M, et al. Optimized interpretation of fractional flow reserve derived from computed tomography: Comparison of three interpretation methods[J]. J Cardiovasc Comput Tomogr,2019,13(2):134-141.\u003c/li\u003e\n \u003cli\u003eLee JM, Choi G, Koo BK, et al. Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics[J]. JACC Cardiovasc Imaging, 2019,12(6):1032-1043.\u003c/li\u003e\n \u003cli\u003eTakagi H, Leipsic JA, McNamara N, et al. Trans-lesional fractional flow reserve gradient as derived from coronary CT improves patient management: ADVANCE registry[J]. J Cardiovasc Comput Tomogr, 2022,16(1):19-26.\u003c/li\u003e\n \u003cli\u003eYan H, Gao Y, Zhao N, et al. Change in Computed Tomography-Derived Fractional Flow Reserve Across the Lesion Improve the Diagnostic Performance of Functional Coronary Stenosis[J]. Front Cardiovasc Med, 2022,8:788703.\u003c/li\u003e\n \u003cli\u003eKołtowski Ł, Zaleska M, Maksym J, et al. Quantitative flow ratio derived from diagnostic coronary angiography in assessment of patients with intermediate coronary stenosis: a wire-free fractional flow reserve study. Clin Res Cardiol. 2018;107(9):858-867.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCort\u0026eacute;s C, Liu L, Berdin SL, et al. Agreement between Murray law-based quantitative flow ratio (\u0026mu;QFR) and three-dimensional quantitative flow ratio (3D-QFR) in non-selected angiographic stenosis: A multicenter study. Cardiol J. 2022;29(3):388-395.\u003c/li\u003e\n \u003cli\u003eTu S, Ding D, Chang Y, Li C, et al. Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law. Catheter Cardiovasc Interv. 2021;97 Suppl 2:1040-1047.\u003c/li\u003e\n \u003cli\u003eZuo W, Sun R, Xu Y, et al. Impact of calcification on Murray law-based quantitative flow ratio for physiological assessment of intermediate coronary stenoses [published online ahead of print, 2023 Jul 4]. Cardiol J. 2023;10.5603/CJ.a2023.0045\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coronary physiology, Murray Law based Quantitative Flow Ratio, Fractional Flow Reserve, Coronary Artery Disease, Diagnostic Performance","lastPublishedDoi":"10.21203/rs.3.rs-3844865/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3844865/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eWe aim to compare with the diagnostic performance of target-position quantitative flow ratio derived from Murray Law (target-μFR) and vessel quantitative flow ratio derived from Murray Law (vessel-μFR) using the fractional flow reserve (FFR) as reference standard. This study may provide more evidence for the novel clinical usage of target-μFR in the diagnosis of coronary artery disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eSix hundreds and fifty-six patients (685 lesions) with known or suspected coronary artery disease were screened for this retrospective analysis between January 2021 to March 2023. A total of 161 patients (190 lesions) underwent quantitative coronary angiography and FFR evaluations. Both of target-μFR and vessel-μFR were compared the diagnostic performance using the FFR≤0.80 as the reference standard.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eBoth target-μFR (R=0.90) and vessel-μFR (R=0.87) demonstrated a strong correlation with FFR, and both methods showed great agreement with FFR. The area under the receiver operating characteristic curve was 0.937 for target-μFR and 0.936 for vessel-μFR in predicting FFR≤0.80. FFR≤0.80 were predicted with high sensitivity (92.98%), specificity (91.01%) and the Youden index (0.840) using the cutoff value of 0.83 for target-μFR. A good diagnostic performance (sensitivity 86.44%, specificity 88.51% and Youden index 0.750) was also demonstrated by vessel-μFR which the cutoff value was 0.80.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe target-μFR has the similar diagnostic performance with vessel-μFR. The accuracy of μFR does not seem to be affected by the selection of the measurement point. Both of the virtual model could be used as computations tools for diagnosing ischemia and to aid clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Diagnostic Performance of Target-position Murray Law based Quantitative Flow Ratio (target-μFR) vs Vessel-μFR in Patients with stable Coronary Artery Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-10 20:07:36","doi":"10.21203/rs.3.rs-3844865/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-06T08:52:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T07:46:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90351278557372550774567169720053941310","date":"2024-08-16T07:18:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-18T19:34:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"070a87ef-56d9-49a0-aa48-c35b2d4e4096","date":"2024-05-08T13:18:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-16T13:36:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T04:38:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-09T05:28:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-09T05:27:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-01-08T07:59:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc7a02d9-d2b7-49d3-bb8c-94141573700b","owner":[],"postedDate":"January 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:02:29+00:00","versionOfRecord":{"articleIdentity":"rs-3844865","link":"https://doi.org/10.1186/s12872-025-04757-x","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-05-02 15:57:13","publishedOnDateReadable":"May 2nd, 2025"},"versionCreatedAt":"2024-01-10 20:07:36","video":"","vorDoi":"10.1186/s12872-025-04757-x","vorDoiUrl":"https://doi.org/10.1186/s12872-025-04757-x","workflowStages":[]},"version":"v1","identity":"rs-3844865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3844865","identity":"rs-3844865","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}