Calculation Of Fractional Flow Reserve Research Articles

Correlation and Agreement of Quantitative Flow Ratio With Fractional Flow Reserve in Saphenous Vein Grafts.

The applicability of quantitative flow ratio (QFR), a nonhyperemic, invasive coronary angiography-derived computation of fractional flow reserve (FFR), has not been studied in coronary artery bypass grafts. We sought to explore the correlation and diagnostic agreement between QFR and FFR in saphenous vein grafts (SVGs). A total of 129 prospectively included patients (mean age 73±8 years, 84% male) with prior coronary artery bypass grafting underwent invasive coronary angiography and pressure-derived functional assessment in 150 nonoccluded SVGs. QFR dedicated angiography images of the SVGs were acquired and used for offline QFR computation. The diagnostic performance of QFR was compared with 2-dimensional quantitative coronary angiography, using FFR as a reference. A threshold of ≤0.80 was used to define functional significance. QFR was successfully computed in 140 (93%) SVGs. We found a significant correlation between QFR and FFR (r=0.72, P<0.001). FFR indicated significant disease in 43 (31%) SVGs, whereas QFR analysis showed significant lesions in 53 (38%) bypass grafts. QFR exhibited a higher sensitivity and diagnostic accuracy compared with angiographic lesion assessment (84% versus 63%, P=0.030 and 83% versus 74%, P=0.036, respectively), whereas specificity did not differ (82% versus 79%, P=0.466). Lastly, QFR demonstrated a higher area under the receiver operating curve than quantitative coronary angiography (0.90 versus 0.82, P=0.008) for the detection of FFR-defined significant vein graft disease. This study shows the potential applicability of contemporary QFR computation in venous bypass grafts with a moderate correlation and good diagnostic accuracy compared with functional assessment using FFR.

Accuracy of coronary computed tomography angiography-derived quantitative flow ratio for onsite assessment of coronary lesions.

Coronary computed tomography angiography (CCTA)-derived Murray law-based quantitative flow ratio (CT-μFR) is a novel non-invasive method for fast computation of fractional flow reserve (FFR) from CCTA images, yet its diagnostic performance remains to be prospectively validated. We aimed to evaluate the diagnostic performance of onsite CT-μFR in patients with coronary artery disease. This prospective, single-centre trial enrolled patients with ≥1 lesion with 30-90% diameter stenosis on CCTA and planned invasive coronary angiography (ICA) within 30 days. CT-μFR, ICA-derived μFR and FFR were evaluated separately in a blinded fashion. The primary endpoint was the diagnostic accuracy of CT-μFR in identifying patients with haemodynamically significant coronary stenosis defined by the invasive standard: FFR ≤0.80, or μFR ≤0.80 when FFR was not available. Between December 2020 and August 2023, 260 patients were consecutively enrolled. Paired comparison between CT-μFR and the invasive standard was obtained in 706 vessels from 260 patients. The patient-level accuracy of CT-μFR was 89.6% (95% confidence interval [CI]: 85.9-93.4%), which was significantly higher than the prespecified target of 72.0% (p<0.001). Sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios for CT-μFR were 93.1%, 86.1%, 87.1%, 92.5%, 6.7, and 0.1, respectively. Out of the 231 vessels investigated by FFR, the accuracy of CT-μFR in vessels without extensive calcification was non-inferior to that of μFR (90.6% vs 88.9%; difference=1.8% [95% CI: -2.8 to 5.5%]; p for non-inferiority<0.001). The study met its prespecified primary endpoint of the diagnostic accuracy of CT-μFR in identifying patients with haemodynamically significant coronary stenosis. CT-μFR was non-inferior to ICA-derived μFR in vessels without extensive calcification. (ClinicalTrials.gov: NCT04665817).

Influence of the pressure wire on the fractional flow reserve calculation: CFD analysis of an ideal vessel and clinical patients with stenosis

Background and objectiveFractional Flow Reserve (FFR) is generally considered the gold standard in hemodynamics to assess the impact of a stenosis on the blood flow. The standard procedure to measure involves the displacement of a pressure guide along the circulatory system until it is placed next to the lesion to be analyzed. The main objective of the present study is to analyze the influence of the pressure guide on the invasive FFR measurements and its implications in clinical practice. MethodsWe studied the influence of pressure wires on the measurement of Fractional Flow Reserve (FFR) through a combination of Computational Fluid Dynamics (CFD) simulations using 45 clinical patient data with 58 lesions and ideal geometries. The analysis is conducted considering patients that were subjected to a computer tomography and also have direct measurements using a pressure guide. Influence of the stenosis severity, degree of occlusion and blood viscosity has also been studied. ResultsThe influence of pressure wires specifically affects severe stenosis with a lumen diameter reduction of 50 % or greater. This type of stenosis leads to reduced hyperemic flow and increased coronary pressure drop. Thus, we identified that the placement of wires during FFR measurements results in partial obstruction of the coronary artery lumen, leading to increased pressure drop and subsequent reduction in blood flow. The severity of low FFR values associated with severe stenosis may be prone to overestimation when compared to stenosis without severe narrowing. These results have practical implications, particularly in the interpretation of lesions falling within the “gray zone” (0,75−0,80). ConclusionsThe pressure wire's presence significantly alters the flow on severe lesions, which has an impact on the FFR calculation. In contrast, the impact of the pressure wire appears to be reduced when the FFR is larger than 0.8. The findings provide critical information for physicians, emphasizing the need for cautious interpretation of FFR values, particularly in severe stenosis. It also offers insights into improving the correlation between FFRct models and invasive measurements by incorporating the influence of pressure wires.

Non-invasive fractional flow reserve estimation in coronary arteries using angiographic images

Coronary artery disease is the leading global cause of mortality and Fractional Flow Reserve (FFR) is widely regarded as the gold standard for assessing coronary artery stenosis severity. However, due to the limitations of invasive FFR measurements, there is a pressing need for a highly accurate virtual FFR calculation framework. Additionally, it’s essential to consider local haemodynamic factors such as time-averaged wall shear stress (TAWSS), which play a critical role in advancement of atherosclerosis. This study introduces an innovative FFR computation method that involves creating five patient-specific geometries from two-dimensional coronary angiography images and conducting numerical simulations using computational fluid dynamics with a three-element Windkessel model boundary condition at the outlet to predict haemodynamic distribution. Furthermore, four distinct boundary condition methodologies are applied to each geometry for comprehensive analysis. Several haemodynamic features, including velocity, pressure, TAWSS, and oscillatory shear index are investigated and compared for each case. Results show that models with average boundary conditions can predict FFR values accurately and observed errors between invasive FFR and virtual FFR are found to be less than 5%.

Rapid and Precise Computation of Fractional Flow Reserve from Routine Two-Dimensional Coronary Angiograms Based on Fluid Mechanics: The Pilot FFR2D Study.

Objective: To present a novel pipeline for rapid and precise computation of fractional flow reserve from an analysis of routine two-dimensional coronary angiograms based on fluid mechanics equations (FFR2D). Material and methods: This was a pilot analytical study that was designed to assess the diagnostic performance of FFR2D versus the gold standard of FFR (threshold ≤ 0.80) measured with a pressure wire for the physiological assessment of intermediate coronary artery stenoses. In a single academic center, consecutive patients referred for diagnostic coronary angiography and potential revascularization between 1 September 2020 and 1 September 2022 were screened for eligibility. Routine two-dimensional angiograms at optimal viewing angles with minimal overlap and/or foreshortening were segmented semi-automatically to derive the vascular geometry of intermediate coronary lesions, and nonlinear pressure-flow mathematical relationships were applied to compute FFR2D. Results: Some 88 consecutive patients with a single intermediate coronary artery lesion were analyzed (LAD n = 74, RCA n = 9 and LCX n = 5; percent diameter stenosis of 45.7 ± 11.0%). The computed FFR2D was on average 0.821 ± 0.048 and correlated well with invasive FFR (r = 0.68, p < 0.001). There was very good agreement between FFR2D and invasive-wire FFR with minimal measurement bias (mean difference: 0.000 ± 0.048). The overall accuracy of FFR2D for diagnosing a critical epicardial artery stenosis was 90.9% (80 cases classified correctly out of 88 in total). FFR2D identified 24 true positives, 56 true negatives, 4 false positives, and 4 false negatives and predicted FFR ≤ 0.80 with a sensitivity of 85.7%, specificity of 93.3%, positive likelihood ratio of 13.0, and negative likelihood ratio of 0.15. FFR2D had a significantly better discriminatory capacity (area under the ROC curve: 0.95 [95% CI: 0.91-0.99]) compared to 50%DS on 2D-QCA (area under the ROC curve: 0.70 [95% CI: 0.59-0.82]; p = 0.0001) in predicting wire FFR ≤ 0.80. The median time of image analysis was 2 min and the median time of computation of the FFR2D results was 0.1 s. Conclusion: FFR2D may rapidly derive a precise image-based metric of fractional flow reserve with high diagnostic accuracy based on a single two-dimensional coronary angiogram.

Angiography-based Fractional Flow Reserve for the Prediction of Clinical Outcomes After Drug-coated Balloon or Plain Old Balloon Angioplasty.

AccuFFRangio is a novel method for fast computation of fractional flow reserve (FFR) based on coronary angiography and computational fluid dynamics. The association between the AccuFFRangio and clinical outcomes after drug-coated balloon (DCB) or plain old balloon angioplasty (POBA) remains to be investigated. This study included consecutive patients who underwent balloon angioplasty from December 2016 to October 2020. AccuFFRangio was calculated retrospectively based on the post-procedural angiography obtained immediately after angioplasty. The primary endpoint was major adverse cardiac events (MACE), defined as a composite of all-cause death, vessel-related myocardial infarction, and repeat target vessel revascularization. A total of 169 patients were retrospectively analyzed in this study. Post-procedural AccuFFRangio (hazard ratio [HR] per 0.1 increase 0.33, 95% confidence interval [CI] 0.22-0.48, p < 0.001) was an independent predictor for MACE at 2-year follow-up. Post-procedural AccuFFRangio ≤ 0.87 was determined as the optimal cutoff value to predict MACE with an area under the curve (AUC) of 0.872 (95% CI 0.813-0.919, p < 0.001). AccuFFRangio measured immediately after balloon angioplasty is a promising predictor of unfavorable clinical outcomes.

Three-Year Clinical Impact of Murray Law-Based Quantitative Flow Ratio and OCT- or FFR-Guidance in Angiographically Intermediate Coronary Lesions.

The FORZA trial (FFR or OCT Guidance to Revascularize Intermediate Coronary Stenosis Using Angioplasty) prospectively compared the use of fractional flow reserve (FFR) or optical coherence tomography (OCT) for treatment decisions and percutaneous coronary intervention (PCI) optimization in patients with angiographically intermediate coronary lesions. Murray law-based quantitative-flow-ratio (μQFR) is a novel noninvasive method for the computation of FFR. In the present study, we evaluated the clinical impact of μQFR, FFR, or OCT guidance in FORZA trial lesions at 3-year follow-up. μQFR was assessed at baseline and, in the case of a decision to intervene, after (FFR- or OCT-guided) PCI. The baseline μQFR was considered the final μQFR for deferred lesions, and post-PCI μQFR value was taken as final for stented lesions. The primary end point was target vessel failure ([TVF]; cardiac death, target-vessel-related myocardial infarction, and target-vessel-revascularization) at a 3-year follow-up. A total of 419 vessels (199 OCT-guided and 220 FFR-guided) were included in the FORZA trial. μQFR was evaluated in 256 deferred lesions and 159 treated lesions (98 OCT-guided PCI and 61 FFR-guided PCI). In treated lesions, post-PCI μQFR was higher in OCT-group compared with FFR-group (median, 0.93 versus 0.91; P=0.023), and the post-PCI μQFR improvement was greater in FFR-group (0.14 versus 0.08; P<0.0001). At 3-year follow-up, OCT- and FFR-guided treatment decisions resulted in comparable TVF rate (6.7% versus 7.9%; P=0.617). Final μQFR was the only predictor of TVF. μQFR ≤0.89 was associated with 3× increase in TVF (11.6% versus 3.7%; P=0.004). PCI was a predictor of higher final μQFR (odds ratio, 0.22 [95% CI, 0.14-0.34]; P<0.001). In vessels with angiographically intermediate coronary lesions, OCT-guided PCI resulted in comparable clinical outcomes as FFR-guided PCI. μQFR estimated at the end of diagnostic or interventional procedure predicted 3-year TVF. URL: https://www.clinicaltrials.gov; Unique identifier: NCT01824030.

The value of AI-based analysis of fractional flow reserve of volume computed tomographically detected coronary artery stenosis with regard to their hemodynamic relevance.

The aim of our work was to demonstrate the importance of artificial intelligence-based analysis of fractional flow reserves of computed tomographically detected coronary artery stenosis with regard to their hemodynamic relevance in patients with unclear chest pain and suspected stable coronary heart disease with a low to medium pre-test probability.The collective of our retrospective analysis includes 63 patients in whom coronary artery stenosis was detected by volume computed tomographic examination in "one beat, whole heart" mode in the period from March to October 2022. In these patients, the fractional flow reserve was also determined by computed tomography, which was modulated by the use of artificial intelligence.The calculated values of the fractional flow reserve and the degrees of stenosis determined by computed tomography showed a moderate and significant negative correlation for all three coronary vascular territories (LAD/CX/RCA) (correlation coefficient rho = 0.54/0.54/0.6; p < 0.01 respectively). In just over a third (37.6 %) of all stenoses classified as high-grade by computed tomography, the assessment of hemodynamic relevance by calculating the fractional flow reserve deviated from the severity of the stenosis diagnosed by computed tomography, while the results in the peripheral areas "no stenosis/vascular occlusion" were 100 % consistent in each case.The present results of this work illustrate that the calculation of the fractional flow reserve based on artificial intelligence as a supplement to volume computed tomography of the heart can make a decisive contribution to further therapy planning by increasing the specificity of the purely morphological method by the physiological aspect. · Calculation of fractional flow reserve is a useful addition to computed tomography of the heart.. · It provides possibility to dispense with unnecessary further diagnostics by increasing specificity.. · The combination of both procedures leads to therapy optimization for patients.. · Noblé H, Mühlbauer N, Ehling J et al. The value of AI-based analysis of fractional flow reserve of volume computed tomographically detected coronary artery stenosis with regard to their hemodynamic relevance. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2271-0887.

Diagnostic Performance of Angiography-Derived Quantitative Flow Ratio in Complex Coronary Lesions.

Quantitative flow ratio derived from computed tomography angiography (CT-QFR) and invasive coronary angiography (Murray law-based quantitative flow ratio [μQFR]) are novel approaches enabling rapid computation of fractional flow reserve without the use of pressure guidewires and vasodilators. However, the feasibility and diagnostic performance of both CT-QFR and μQFR in evaluating complex coronary lesions remain unclear. Between September 2014 and September 2021, 240 patients with 30% to 90% coronary diameter stenosis who underwent both coronary computed tomography angiography and invasive coronary angiography with fractional flow reserve within 60 days were retrospectively enrolled. The diagnostic performance of CT-QFR and μQFR in detecting functional ischemia among all lesions, especially complex coronary lesions, was analyzed using fractional flow reserve as the reference standard. CT-QFR and μQFR analyses were performed on 309 and 289 vessels, respectively. The diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for CT-QFR in all lesions at the per-vessel level were 91% (with a 95% CI of 84%-96%), 92% (95% CI, 88%-95%), 83% (95% CI, 75%-90%), 96% (95% CI, 93%-98%), and 92% (95% CI, 88%-95%), with values for μQFR of 90% (95% CI, 81%-95%), 97% (95% CI, 93%-99%), 92% (95% CI, 84%-97%), 96% (95% CI, 92%-98%), and 94% (95% CI, 91%-97%), respectively. Among bifurcation, tandem, and moderate-to-severe calcified lesions, the diagnostic values of CT-QFR and μQFR showed great correlation and agreement with those of invasive fractional flow reserve, achieving an area under the receiver operating characteristic curve exceeding 0.9 for each complex lesion at the vessel level. Furthermore, the accuracies of CT-QFR and μQFR in the gray zone were 85% and 84%, respectively. Angiography-derived quantitative flow ratio (CT-QFR and μQFR) demonstrated remarkable diagnostic performance in complex coronary lesions, indicating its pivotal role in the management of patients with coronary artery disease.

Diagnostic performance of ultrasonic flow ratio versus quantitative flow ratio for assessment of coronary stenosis

BackgroundUltrasonic flow ratio (UFR) is a novel intravascular ultrasound (IVUS)-derived modality for fast computation of fractional flow reserve (FFR) without pressure wires and adenosine. AimsThis study was sought to compare the diagnostic performance of UFR and quantitative flow ratio (QFR), using FFR as the reference standard. MethodsThis is a retrospective study enrolling consecutive patients with intermediate coronary artery lesions (diameter stenosis of 30%–90% by visual estimation) for IVUS and FFR measurement. UFR and QFR were performed offline in a core-lab by independent analysts blinded to FFR. ResultsFrom December 2022 to May 2023, a total of 78 eligible patients were enrolled. IVUS and FFR measurements were successfully conducted in 104 vessels, finally 98 vessels with both FFR, UFR and QFR evaluation were analyzed. Mean FFR was 0.79 ± 0.12. UFR showed a strong correlation with FFR similar to QFR (r = 0.83 vs. 0.82, p = 0.795). Diagnostic accuracy of UFR was non-inferior to QFR (94% [89%–97%] versus 90% [84%–94%], p = 0.113). Sensitivity and specificity in identifying hemodynamically significant stenosis were comparable between UFR and QFR (sensitivity: 89% [79%–96%] versus 85% [74%–92%], p = 0.453; specificity: 97% [91%–99%] versus 95% [88%–99%], p = 0.625). The area under curve for UFR was 0.95 [0.90–0.98], non-inferior to QFR (difference = 0.021, p = 0.293), and significantly higher than minimum lumen area (MLA; difference = 0.13, p < 0.001). Diagnostic accuracy of UFR and QFR was not statically different in bifurcation nor non-bifurcation lesions. ConclusionsUFR showed excellent concordance with FFR, non-inferior to QFR, superior to MLA. UFR provides a potentiality for the integration of physiological assessment and intravascular imaging in clinical practice.

Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance

Background and objectiveRecently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis. MethodsA reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR ≤ 0.80 were defined as hemodynamically significant. ResultsA total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFRML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFRML simulation was about 10 min. On a per-vessel basis, the FFRML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFRML was 0.984. ConclusionIn this selected cohort of patients, the FFRML improves the computational efficiency and ensures the accuracy. The favorable performance of FFRML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.

Hemodynamic assessment of pci using ultrasonic flow ratio in multivessel cad

Abstract Background Ultrasonic flow ratio (UFR) is a novel method for fast computation of fractional flow reserve (FFR) from intravascular ultrasound. Recently studies have shown the feasibility of morphofunctional computational methods to reliably estimate coronary physiology stemming from intravascular ultrasound images. Purpose The objective of the study was to evaluate the reasons for suboptimal physiological results after PCI using the UFR. Methods The study population comprised 150 consecutive patients (283 vessels) who underwent intravascular ultrasound for optimization of PCI in three-vessel-disease as mandated in the ongoing MULTIVESSEL TALENT trial (NCT04390672). The UFR analysis of final IVUS pullback was performed at the CORRIB Core Lab by an analyst who was blinded to the clinical data using a prototype software package (IvusPlus prototype, Shanghai Pulse Medical, Shanghai, China). Suboptimal physiological results were defined as post PCI UFR value of &amp;lt;0.91. UFR drop of ≥0.05 (gradient) was taken as a significant drop either intra or outside of the stent. Based on the definition of the HAWKEYE study, Focal lesions were defined as UFR drop of ≥0.05 in 10mm, diffuse lesions were defined as progressive decline of UFR of ≥0.05 without any clear evidence of focal drop, and mixed lesions were defined as the combination of focal and diffuse lesions. Results The median UFR with IQR among 283 vessels was 0.94 (0.91-0.97). Sixty-four (22.61%) of the 283 vessels had a UFR less than 0.91. We measured gradients using the UFR pull back curve intra-stent and outside of stent (Figure 1) in vessels with UFR &amp;lt;0.91. Significant "intra stent gradient", "out of stent gradient", combined "intra stent "and "out of stent" gradients were seen in 28 (44%), 18 (28%), and 10 (16%) vessels respectively. No clearly defined gradients were found in 8 (12%) vessels. Out of the 28 "out of stent" gradients, 20 were diffuse lesions, 5 were focal lesions and 3 was a mixed lesion. Conclusion IVUS has been widely used in optimization of PCI especially in scenarios with complex multi vessel disease. Combination of IVUS and FFR enable the operator to conform to the highest standards in PCI. This study has demonstrated that using UFR it is possible to identify the cause(s) of suboptimal PCI results and thereby to further optimize PCI results.

Minimum stent area, stent expansion and ultrasonic flow ratio post pci: insights from multivessel talent trial

Abstract Background Ultrasonic flow ratio (UFR) is a novel method for fast computation of fractional flow reserve (FFR) from intravascular ultrasound. Recently studies have shown the feasibility of morphofunctional computational methods to reliably estimate coronary physiology from intravascular ultrasound images. Purpose To assess minimum stent area and stent expansion and its relation to ultrasonic flow ratio post percutaneous coronary intervention. Methods The study population consisted of 150 consecutive patients (280 vessels) who underwent intravascular ultrasound for optimization of PCI for three vessel disease as mandated in the ongoing MULTIVESSEL TALENT trial (NCT04390672). The UFR analysis was performed at the CORRIB Core Lab by an analyst who was blinded to the clinical data using a prototype software package (IvusPlus prototype, Pulse Medical Imaging Technology, Shanghai, China). The stent expansion and minimum stent area was automatically detected by the software. Results The median and mean UFR of 280 vessels post-PCI was 0.94 (0.91-0.97).and 0.93±0.04, respectively. Twenty three percent (n=115) of the 280 vessels had a UFR less than 0.91.The average minimum stent area (MSA) among the population was 5.82 ± 1.95 mm2. The mean stent expansion among the population was 97% ± 22%. We compared the site reported MSA with the software derived MSA which showed strong correlation (r=0.859; P&amp;lt;0.001). There was no correlation between MSA and UFR (r= -0.13, P=0.81). The stent expansion among patients who had UFR &amp;lt;0.91 when compared to ≥0.91 was significantly lower (82% vs 98%, P&amp;lt;0.001). The delta UFR within the stent was significantly higher in patients who had a UFR&amp;lt;0.94 vs ≥0.94 (0.06 vs 0.02; P&amp;lt;0.01). Delta UFR within the stent was higher when the stent expansion was &amp;lt;80% and &amp;lt;90% when compared to &amp;gt;80% and &amp;gt;90% (0.07 vs 0.03; P&amp;lt;0.01) and (0.06 vs 0.02; P&amp;lt;0.01) respectively. Conclusion Combination of IVUS and FFR enables the operator to conform to highest standards in PCI. This study demonstrates that UFR is not related to the minimum stent area, and patients who have UFR &amp;lt;0.94 have sub-optimal stent expansion.MSA and UFRUFR and Stent expansion

Abstract 15380: Diagnostic Performance of Ultrasonic Flow Ratio versus Quantitative Flow Ratio for Assessment of Coronary Artery Stenosis

Introduction: Ultrasonic flow ratio (UFR) is a novel intravascular ultrasound (IVUS)-derived method for fast computation of fractional flow reserve (FFR) without pressure wires and adenosine. Aims: The aim of this study was to evaluate the diagnostic performance of UFR and compare it with angiography-based quantitative flow ratio (QFR), using FFR as the reference standard. Methods: Patients who underwent coronary angiography, IVUS and FFR were analyzed. UFR and QFR was computed offline in a central core-lab by independent analysts blinded to FFR. Results: A total of 111 paired comparisons between UFR, QFR and FFR from 56 patients were analyzed. UFR showed a numerically better correlation (r=0.83 vs. 0.79; p=0.27) and significant better agreement (standard deviation of the difference=0.07 vs. 0.08; p=0.02) with FFR than QFR. Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio for UFR to predict FFR was 92% (95% CI: 85%-96%), 85% (95% CI: 71%-94%), 97% (95% CI: 89%-99%), 95% (95% CI: 83%-99%), 90% (95% CI: 82%-95%), 27.6 (95% CI: 7.0-108.4), and 0.16 (95% CI: 0.08-0.31), respectively. The area under curve for UFR to predict an FFR ≤ 0.80 was 0.94, is equivalent to QFR (difference=0.04; p=0.23), and higher than minimal lumen area (difference=0.09, p=0.006). Diagnostic accuracy was not significantly different in bifurcation lesions, nor in non-bifurcation lesions. Conclusions: The UFR showed good diagnostic concordance with FFR, equivalent to QFR. The good diagnostic performance of UFR provides a potentiality for the integration of physiological assessment and intravascular imaging in real-world clinical practice.

A simplified coronary model for diagnosis of ischemia-causing coronary stenosis

Background and objectiveThe functional assessment of the severity of coronary stenosis from coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR) has recently attracted interest. However, existing algorithms run at high computational cost. Therefore, this study proposes a fast calculation method of FFR for the diagnosis of ischemia-causing coronary stenosis. MethodsWe combined CCTA and machine learning to develop a simplified single-vessel coronary model for rapid calculation of FFR. First, a zero-dimensional model of single-vessel coronary was established based on CCTA, and microcirculation resistance was determined through the relationship between coronary pressure and flow. In addition, a coronary stenosis model based on machine learning was introduced to determine stenosis resistance. Computational FFR (cFFR) was then obtained by combining the zero-dimensional model and the stenosis model with inlet boundary conditions for resting (cFFRr) and hyperemic (cFFRh) aortic pressure, respectively. We retrospectively analyzed 75 patients who underwent clinically invasive FFR (iFFR), and verified the model accuracy by comparison of cFFR with iFFR. ResultsThe average computing time of cFFR was less than 2 s. The correlations between cFFRr and cFFRh with iFFR were r = 0.89 (p < 0.001) and r = 0.90 (p < 0.001), respectively. Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio for cFFRr and cFFRh were 90.7%, 95.0%, 89.1%, 76.0%, 98.0%, 8.7, 0.1 and 92.0%, 95.0%, 90.9%, 79.2%, 98.0%, 10.5, 0.1, respectively. ConclusionsThe proposed model enables rapid prediction of cFFR and exhibits high diagnostic performance in selected patient cohorts. The model thus provides an accurate and time-efficient computational tool to detect ischemia-causing stenosis and assist with clinical decision-making.

On-site CT-derived cFFR in patients with suspected coronary artery disease: Feasibility on a 128-row CT scanner in everyday clinical practice.

Technical feasibility of CT-based calculation of fractional flow reserve (cFFR) using a 128-row computed tomography scanner in an everyday routine setting. Post-processing and everyday practicability should be analyzed on the scanner on-site in connection with clinical parameters. This single-center retrospective analysis included 230 patients (74 female; mean age 63.8years) with CCTA within 21 months between 01/2018 and 09/2019 without non-pathological examinations. cFFR values were obtained using a deep learning-based non-commercial research prototype (cFFR Version3.5.0; Siemens Healthineers GmbH, Erlangen). cFFR values were evaluated at two points: at the maximum point of the stenosis and 1.0 cm distal to the stenosis. Comparison with invasive coronary angiography in 57/230 patients (24.7 %) was performed. CT parameters and quality were evaluated. Further subgroup classification concerning criteria of technical postprocessing was performed: no changes necessary, minor corrections necessary, major corrections necessary, and no evaluation was possible. The required time from starting the software to the final result was evaluated. A total of 116/448 (25.9 %) mild, 223/448 (49.8 %) moderate, and 109/448 (24.3 %) obstructive stenoses was found. The mean cFFR at the maximum point of the stenosis was 0.92 ± 0.09 and significantly higher than the cFRR value of 0.89 ± 0.13 distal to the stenosis (p < 0.001*). The mean degree of stenosis was 44.02 ± 26.99 % (range: 1-99 %) with an area of 5.39 ± 3.30 mm2. In a total of 45 patients (19.1 %), a relevant reduction in cFFR below 0.80 was determined. Overall, in 57/230 patients (24.8 %), catheter angiography was performed. No significant difference in the degree of maximal stenosis (CAD-RADS 0-2/3/4) was detected between the classification of CCTA and ICA (p = 0.171). The mean post-processing time varied significantly with 8.34 ± 4.66 min. in single-vessel CAD vs. 12.91 ± 3.92 min. in two-vessel CAD vs. 21.80 ± 5.94 min. in three-vessel CAD (each p < 0.001). Noninvasive onsite quantification of cFFR is feasible with minimal observer interaction in a routine real-world setting on a 128-row scanner. Deep learning-based algorithms allow a robust and semi-automatic on-site determination of cFFR based on data from standard CT scanners. · Non-invasive on-site quantification of cFFR is feasible with minimal observer interaction.. · Deep-learning based algorithms allow robust and semi-automatic on-site determination of cFFR.. · The mean follow-up time varied significantly with the extent of vascular CAD..

Retracted: Research on individualized distribution approach of coronary resting blood flow for noninvasive calculation of fractional flow reserve

Background and ObjectivesThe distribution of coronary resting blood flow is critical for accurately calculating the computed tomography (CT) angiography-derived fractional flow reserve (FFRCT). However, the diagnostic accuracy of FFRCT calculated by the fixed exponents between two risk factors and coronary resting blood flow, including myocardial mass and diameter of the coronary artery branch, was insufficient compared with invasive fractional flow reserve (FFR). In this study, we proposed the individualized distribution of coronary resting blood flow based on coronary ultrasound blood flow measurement, to improve the diagnostic accuracy of FFRCT calculation. MethodsFive risk factors and an unknown coefficient K were integrated to calculate the individualized distribution of coronary resting blood flow. The K value was fit using the least square method based on coronary ultrasound blood flow measurement results of 30 volunteers. We developed a novel approach for calculating the individualized distribution of coronary resting blood flow and applied it to calculate FFRCT (FFRCTI). Then, we tested the performance of the individualized distribution approach by comparing it with the approach proposed by Taylor based on coronary ultrasound blood flow measurement results of 13 volunteers. Finally, we tested the diagnostic accuracy of FFRCT calculated by two approaches in invasive FFR of 121 vessels with coronary stenosis. ResultsWe identified five risk factors and 6.83×10−5 for K value, including cardiac output, mean arterial pressure, myocardial mass, coronary artery volume, and diameter of the coronary artery branch, to calculate the individualized distribution of coronary resting blood flow. The mean square error of the individualized distribution approach (0.0088) was significantly less than that of the approach proposed by Taylor (0.0799). The diagnostic accuracy of FFRCTI calculated by the individualized distribution approach (91.74%) was higher than that of the approach proposed by Taylor (FFRCTT) (82.64%). ConclusionsThe individualized distribution approach of coronary resting blood flow can significantly improve the diagnostic accuracy of FFRCT calculation compared with invasive FFR, and support its wide clinical application for diagnosing myocardial ischemia caused by coronary stenosis.

Diagnostic accuracy of optical flow ratio: an individual patient-data meta-analysis.

Optical flow ratio (OFR) is a novel method for the fast computation of fractional flow reserve (FFR) from optical coherence tomography. We aimed to evaluate the diagnostic accuracy of OFR in assessing intermediate coronary stenosis using wire-based FFR as the reference. We performed an individual patient-level meta-analysis of all available studies with paired OFR and FFR assessments. The primary outcome was vessel-level diagnostic concordance of the OFR and FFR, using a cut-off of ≤0.80 to define ischaemia and ≤0.90 to define suboptimal post-percutaneous coronary intervention (PCI) physiology. This meta-analysis was registered in PROSPERO (CRD42021287726). Five studies were finally included, providing 574 patients and 626 vessels (404 pre-PCI and 222 post-PCI) with paired OFR and FFR from 9 international centres. Vessel-level diagnostic concordance of the OFR and FFR was 91% (95% confidence interval [CI]: 88%-94%), 87% (95% CI: 82%-91%), and 90% (95% CI: 87%-92%) in pre-PCI, post-PCI, and overall, respectively. The overall sensitivity, specificity, and positive and negative predictive values were 84% (95% CI: 79%-88%), 94% (95% CI: 92%-96%), 90% (95% CI: 86%-93%), and 89% (95% CI: 86%-92%), respectively. Multivariate logistic regression indicated that a low pullback speed (odds ratio [OR] 7.02, 95% CI: 1.68-29.43; p=0.008) was associated with a higher risk of obtaining OFR values at least 0.10 higher than FFR. Increasing the minimal lumen area was associated with a lower risk of obtaining an OFR at least 0.10 lower than FFR (OR 0.39, 95% CI: 0.18-0.82; p=0.013). This individual patient data meta-analysis demonstrated a high diagnostic accuracy of OFR. OFR has the potential to provide an improved integration of intracoronary imaging and physiological assessment for the accurate evaluation of coronary artery disease.

Numerical vs analytical comparison with experimental fractional flow reserve values of right coronary artery stenosis.

The fractional flow reserve (FFR) index has been widely accepted as a standard diagnostic method for identifying functional relevance of coronary stenosis. Since the invasive techniques used for its determination are associated with a certain risk of vascular injury, as well as with an increased cost, several non-invasive procedures have been developed. The aim of this study was to compare FFR values for the coronary artery obtained by computational fluid dynamics (CFD) and coronary computed tomography angiography (CCTA). Computation of FFR has been performed using both numerical and the analytical method. The numerical method employs CFD to solve the governing equations which relate to mass and momentum conservation (the continuity equation and the Navier-Stokes equations) as well as CCTA to generate the three-dimensional computational domain. After imposing the appropriate boundary conditions, the values of the pressure change are calculated and the FFR index is determined. Based on Bernoulli's law, the analytical method calculates the overall pressure drop across the stenosis in the coronary artery, enabling FFR determination. The clinical data for twenty patients who underwent invasive coronary angiography are used to validate the results obtained by using CFD (together with CCTA) simulation and analytical solution. The medically measured FFR compared to the analytical one differs by about 4%, while, the difference is about 2.6% when compared to the numerical FFR. For FFR values below 0.8 (which are considered to be associated with myocardial ischemia) the standard error has a value of 0.01201, while the standard deviation is 0.02081. For FFR values above 0.80, these values are slightly higher. Bland-Altman analysis showed that medical measurement and numerical FFR were in good agreement (SD = 0.0292, p< 0.0001). The analytically calculated FFR has a slightly lower coefficient of determination than the numerically computed FFR when compared with experimental one. However, it can still give a reliable answer to the question of whether patients need a stent, bypass surgery or only drug treatment and it requires a significantly lower computation time.

Quantitative flow ratio versus fractional flow reserve for guiding percutaneous coronary intervention: design and rationale of the randomised FAVOR III Europe Japan trial.

Quantitative flow ratio (QFR) is a computation of fractional flow reserve (FFR) based on invasive coronary angiographic images. Calculating QFR is less invasive than measuring FFR and may be associated with lower costs. Current evidence supports the call for an adequately powered randomised comparison of QFR and FFR for the evaluation of intermediate coronary stenosis. The aim of the FAVOR III Europe Japan trial is to investigate if a QFR-based diagnostic strategy yields a non-inferior 12-month clinical outcome compared with a standard FFR-guided strategy in the evaluation of patients with intermediary coronary stenosis. FAVOR III Europe Japan is an investigator-initiated, randomised, clinical outcome, non-inferiority trial scheduled to randomise 2,000 patients with either 1) stable angina pectoris and intermediate coronary stenosis, or 2) indications for functional assessment of at least 1 non-culprit lesion after acute myocardial infarction. Up to 40 international centres will randomise patients to either a QFR-based or a standard FFR-based diagnostic strategy. The primary endpoint of major adverse cardiovascular events is a composite of all-cause mortality, any myocardial infarction, and any unplanned coronary revascularisation at 12 months. QFR could emerge as an adenosine- and wire-free alternative to FFR, making the functional evaluation of intermediary coronary stenosis less invasive and more cost-effective.