Estimation Of Fractional Flow Reserve Research Articles

Diagnostic accuracy of Murray law-based quantitative flow ratio in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

Murray law-based quantitative flow ratio (μQFR) is a novel computational method that enables accurate estimation of fractional flow reserve (FFR) using a single angiographic projection. However, its diagnostic value in patients with severe aortic stenosis (AS) remains unclear. We included 25 consecutive patients who underwent transcatheter aortic valve replacement (TAVR) for severe AS with intermediate or greater (30-90%) coronary artery disease (CAD). Pre- and post-TAVR μQFR, QFR, instantaneous flow reserve (iFR), and post-TAVR invasive FFR values were measured. We evaluated the diagnostic performance of pre-TAVR μQFR, QFR, and iFR using post-TAVR FFR ≤ 0.80 as a reference standard of ischemia. Pre-TAVR μQFR was significantly correlated with post-TAVR FFR (r = 0.73, p < 0.0001). The area under the curve of pre-TAVR μQFR on post-TAVR FFR ≤ 0.8 was 0.91 (95% confidence interval [CI] 0.77-0.98), comparable to that of pre-TAVR iFR (0.86 [95% CI 0.71-0.98], p = 0.97). The accuracy, sensitivity, specificity, and positive and negative predictive values of pre-TAVR μQFR on post-TAVR FFR ≤ 0.8 were 84.2% (95% CI 68.7-93.4), 61.6% (95% CI 31.6-86.1), 96.0% (95% CI 79.6-99.9), 88.9% (95% CI 52.9-98.3), and 82.8% (95% CI 70.6-90.6), respectively. For pre-TAVR iFR, these values were 76.5% (95% CI 58.8-89.3), 90.9% (95% CI 58.7-99.8), 69.6% (95% CI 47.1-86.8), 58.8% (95% CI 42.8-73.1), and 94.1% (95% CI 70.8-99.1), respectively. μQFR could be useful for the physiological evaluation of patients with severe AS with concomitant CAD.

High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.

BACKGROUND. Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer with long turnaround times for results. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of FFR-CT computed on-site with a high-speed deep learning-based algorithm with invasive hemodynamic indexes as the reference standard. METHODS. This retrospective study included 59 patients (46 men, 13 women; mean age, 66.5 ± 10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive fractional flow reserve (FFR) and/or instantaneous wave-free ratio measurements from December 2014 to October 2021. Coronary artery lesions were considered to have hemodynamically significant stenosis in the presence of invasive FFR of 0.80 or less and/or instantaneous wave-free ratio of 0.89 or less. A single cardiologist evaluated the CTA images using an on-site deep learning-based semiautomated algorithm entailing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected with invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. RESULTS. A total of 74 lesions were identified with invasive angiography. FFR-CT and invasive FFR had strong correlation (r = 0.81) and, in Bland-Altman analysis, bias of 0.01 and 95% limits of agreement of -0.13 to 0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At a cutoff of 0.80 or less, FFR-CT had 95.9% accuracy, 93.5% sensitivity, and 97.7% specificity. In 39 lesions with severe calcifications (≥ 400 Agatston units), FFR-CT had AUC of 0.991 and at a cutoff of 0.80, 94.7% sensitivity, 95.0% specificity, and 94.9% accuracy. Mean analysis time per patient was 7 minutes 54 seconds. Intraobserver agreement (intraclass correlation coefficient, 0.85; bias, -0.01; 95% limits of agreement, -0.12 and 0.10) and interobserver agreement (intraclass correlation coefficient, 0.94; bias, -0.01; 95% limits of agreement, -0.08 and 0.07) were good to excellent. CONCLUSION. A high-speed on-site deep learning-based FFR-CT algorithm had excellent diagnostic performance for hemodynamically significant stenosis with high reproducibility. CLINICAL IMPACT. The algorithm should facilitate implementation of FFR-CT technology into routine clinical practice.

Current status and future perspectives of fractional flow reserve derived from invasive coronary angiography.

Assessment of the functional significance of coronary artery stenosis using invasive measurement of fractional flow reserve (FFR) or non-hyperemic indices has been shown to be safe and effective in making clinical decisions on whether to perform percutaneous coronary intervention (PCI). Despite strong evidence from clinical trials, utilization of these techniques is still relatively low worldwide. This may be to some extent attributed to factors that are inherent to invasive measurements like prolongation of the procedure, side effects of drugs that induce hyperemia, additional steps that the operator should perform, the possibility to damage the vessel with the wire, and additional costs. During the last few years, there was a growing interest in the non-invasive assessment of coronary artery lesions, which may provide interventionalist with important physiological information regarding lesion severity and overcome some of the limitations. Several dedicated software solutions are available on the market that could provide an estimation of FFR using 3D reconstruction of the interrogated vessel derived from two separated angiographic projections taken during diagnostic coronary angiography. Furthermore, some of them use data about aortic pressure and frame count to more accurately calculate pressure drop (and FFR). The ideal non-invasive system should be integrated into the workflow of the cath lab and performed online (during the diagnostic procedure), thereby not prolonging procedural time significantly, and giving the operator additional information like vessel size, lesion length, and possible post-PCI FFR value. Following the development of these technologies, they were all evaluated in clinical trials where good correlation and agreement with invasive FFR (considered the gold standard) were demonstrated. Currently, only one trial (FAVOR III China) with clinical outcomes was completed and demonstrated that QFR-guided PCI may provide better results at 1-year follow-up as compared to the angiography-guided approach. We are awaiting the results of a few other trials with clinical outcomes that test the performance of these indices in guiding PCI against either FFR or angiography-based approach, in various clinical settings. Herein we will present an overview of the currently available data, a critical review of the major clinical trials, and further directions of development for the five most widely available non-invasive indices: QFR, vFFR, FFRangio, caFFR, and AccuFFRangio.

Computational Analysis of Hemodynamic Indices Based on Personalized Identification of Aortic Pulse Wave Velocity by a Neural Network

Adequate personalized numerical simulation of hemodynamic indices in coronary arteries requires accurate identification of the key parameters. Elastic properties of coronary vessels produce a significant effect on the accuracy of simulations. Direct measurements of the elasticity of coronary vessels are not available in the general clinic. Pulse wave velocity (AoPWV) in the aorta correlates with aortic and coronary elasticity. In this work, we present a neural network approach for estimating AoPWV. Because of the limited number of clinical cases, we used a synthetic AoPWV database of virtual subjects to train the network. We use an additional set of AoPWV data collected from real patients to test the developed algorithm. The developed neural network predicts brachial–ankle AoPWV with a root-mean-square error (RMSE) of 1.3 m/s and a percentage error of 16%. We demonstrate the relevance of a new technique by comparing invasively measured fractional flow reserve (FFR) with simulated values using the patient data with constant (7.5 m/s) and predicted AoPWV. We conclude that patient-specific identification of AoPWV via the developed neural network improves the estimation of FFR from 4.4% to 3.8% on average, with a maximum difference of 2.8% in a particular case. Furthermore, we also numerically investigate the sensitivity of the most useful hemodynamic indices, including FFR, coronary flow reserve (CFR) and instantaneous wave-free ratio (iFR) to AoPWV using the patient-specific data. We observe a substantial variability of all considered indices for AoPWV below 10 m/s and weak variation of AoPWV above 15 m/s. We conclude that the hemodynamic significance of coronary stenosis is higher for the patients with AoPWV in the range from 10 to 15 m/s. The advantages of our approach are the use of a limited set of easily measured input parameters (age, stroke volume, heart rate, systolic, diastolic and mean arterial pressures) and the usage of a model-generated (synthetic) dataset to train and test machine learning methods for predicting hemodynamic indices. The application of our approach in clinical practice saves time, workforce and funds.

Diagnostic performance of on-site computation of quantitative flow ratio by a coronary computed tomography angiography based algorithm: comparison of distal and lesion-specific measurements

Abstract Introduction Guidelines recommend secondary ischemia assessment following a coronary computed tomography angiography (CTA) with suspected obstructive coronary artery disease (CAD). Coronary CTA-derived quantitative flow ratio (CT-QFR) is an on-site technique performed on acquired CTA images that estimates the functional severity of a coronary stenosis. However, CT-QFR measurements are available throughout the coronary vessel with no clear recommendations as to which specific values should be used for identifying obstructive CAD, e.g. most distal or lesion-specific values. Purpose First, to investigate the feasibility of CT-QFR and the correlation and agreement with invasive fractional flow reserve (FFR). Secondly, to compare the diagnostic performance of distal versus lesion-specific CT-QFR for identifying obstructive CAD defined by invasive coronary angiography (ICA) with FFR. Methods A total of 1732 prospectively included patients with symptoms suggestive of CAD referred for CTA were included. All patients with ≥50% diameter stenosis (DS) on CTA were subsequently referred for ICA with conditional FFR in lesions with 30–89%DS. Obstructive CAD was defined by ICA as FFR ≤0.80 or high-grade stenosis by visual assessment (≥90%DS). A blinded analysis of CT-QFR was performed in patients referred to ICA with measurements at the distal end of a vessel (distal CT-QFR) and 1 cm distal to stenotic lesions on CTA (lesion-specific). CT-QFR ≤0.80 was defined as abnormal. For correlation analyses to invasive FFR, CT-QFR was assessed corresponding to the position of the invasive pressure sensor. Results In total, 445/1732 (25%) patients had suspected obstructive CAD at CTA and underwent subsequent ICA. CT-QFR analysis was feasible in 423/445 (95%) patients. CT-QFR correlated (Pearson's rho 0.54, p&amp;lt;0.001) and agreed (mean difference –0.02±0.09) to FFR with CT-QFR overestimating FFR (Fig. 1). Obstructive CAD was identified in 190/423 (44%) patients by ICA. Distal and lesion-specific CT-QFR classified 196 (46%) and 171 (40%) patients as abnormal, respectively. Areas under the receiver-operating characteristic curves for distal versus lesion-specific CT-QFR were similar (0.86 (95% CI: 0.82–0.89) vs. 0.86 (0.82–0.90), p=0.80). Sensitivities for distal and lesion-specific CT-QFR were 78% (95% CI: 71–84) vs. 74% (67–80), p=0.01, respectively, and specificities 79% (95% CI: 74–84) vs. 87% (82–91), p&amp;lt;0.01, respectively. Distal and lesion-specific CT-QFR had similar diagnostic accuracy (79 (95% CI: 75–83), vs. 81 (77–85), p=0.07) (Fig. 2). Conclusion In patients with suspected obstructive CAD on CTA, non-invasive estimation of FFR using CT-QFR is feasible with moderate correlation and good agreement with invasive FFR. Overall diagnostic performance of distal and lesion-specific values for discriminating obstructive CAD by invasive FFR are similar. The use of CT-QFR could therefore potentially reduce the need for referral to invasive angiography after CTA. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Aarhus UniversityRegion Mid Jutland

Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches.

The computational fluid dynamic method has been widely used to quantify the hemodynamic alterations in a diseased artery and investigate surgery outcomes. The artery model reconstructed based on optical coherence tomography (OCT) images generally does not include the side branches. However, the side branches may significantly affect the hemodynamic assessment in a clinical setting, i.e., the fractional flow reserve (FFR), defined as the ratio of mean distal coronary pressure to mean aortic pressure. In this work, the effect of the side branches on FFR estimation was inspected with both idealized and optical coherence tomography (OCT)-reconstructed coronary artery models. The electrical analogy of blood flow was further used to understand the impact of the side branches (diameter and location) on FFR estimation. Results have shown that the side branches decrease the total resistance of the vessel tree, resulting in a higher inlet flowrate. The side branches located at the downstream of the stenosis led to a lower FFR value, while the ones at the upstream had a minimal impact on the FFR estimation. Side branches with a diameter larger than one third of the main vessel diameter are suggested to be considered for a proper FFR estimation. The findings in this study could be extended to other coronary artery imaging modalities and facilitate treatment planning.

Physiologically personalized coronary blood flow model to improve the estimation of noninvasive fractional flow reserve.

Coronary outlet resistance is influenced by the quantification and distribution of resting coronary blood flow. It is crucial for a more physiologically accurate estimation of fractional flow reserve (FFR) derived from computed tomography angiography (CTA), referred to as FFRCT. This study presents a physiologically personalized (PP)-based coronary blood flow model involving the outlet boundary condition (BC) and a standardized outlet truncation strategy to estimate the outlet resistance and FFRCT. In this study, a total of 274 vessels were retrospectively collected from 221 patients who underwent coronary CTA and invasive FFR within 14 days. For FFRCT determination, we have employed a PP-based outlet BC model involving personalized physiological parameters and left ventricular mass (LVM) to quantify resting coronary blood flow. We evaluated the improvement achieved in the diagnostic performance of FFRCT by using the PP-based outlet BC model relative to the LVM-based model, with respect to the invasive FFR. Additionally, in order to evaluate the impact of the outlet truncation strategy on FFRCT, 68 vessels were randomly selected and analyzed independently by two operators, by using two different outlet truncation strategies at 1-month intervals. The per-vessel diagnostic performance of the PP-based outlet BC model was improved, based on invasive FFR as reference, compared to the LVM-based model: (i) accuracy/sensitivity/specificity: 91.2%/90.4%/91.8% versus 86.5%/84.6%/87.6%, for the entire dataset of 274 vessels, (ii) accuracy/sensitivity/specificity: 88.7%/82.4%/90.4% versus 82.4%/ 76.5%/84.0%, for moderately stenosis lesions. The standardized outlet truncation strategy showed good repeatability with the Kappa coefficient of 0.908. It has been shown that our PP-based outlet BC model and standardized outlet truncation strategy can improve the diagnostic performance and repeatability of FFRCT.

A 1D–3D Hybrid Model of Patient-Specific Coronary Hemodynamics

Coronary flow is affected by evolving events such as atherosclerotic plaque formation, rupture, and thrombosis, resulting in myocardial ischemia and infarction. Highly resolved 3D hemodynamic data at the stenosis is essential to model shear-sensitive thrombotic events in coronary artery disease. We developed a hybrid 1D-3D simulation framework to compute patient-specific coronary hemodynamics efficiently. A 1D model of the coronary flow is coupled to an image-based 3D model of the region of interest. This framework affords the advantages of reduced-order modeling, decreasing the global computational cost, without sacrificing the accuracy of the quantities of interest. We validated our 1D-3D model against full 3D coronary simulations in healthy and diseased conditions. Our results showed good agreement between the 3D and the 1D-3D models while reducing the computational cost by 40-fold compared to the 3D simulation. The 1D-3D model predicted left/right coronary flow distribution within 3% and provided an accurate estimation of fractional flow reserve and wall shear stress distribution at the stenosis comparable to the 3D simulation. Savings in computational cost may be significant in situations with changing geometry, such as growing thrombosis. Also, this approach would allow quantifying the time-dependent effect of thrombotic growth and occlusion on the global coronary circulation.

Coronary plaque assessment of Vasodilative capacity by CT angiography effectively estimates fractional flow reserve

BackgroundTo evaluate the feasibility of non-invasive fractional flow reserve (FFR) estimation using histologically-validated assessment of plaque morphology on coronary CTA (CCTA) as inputs to a predictive model further validated against invasive FFR. MethodsPatients (n = 113, 59 ± 8.9 years, 77% male) with suspected coronary artery disease (CAD) who had undergone CCTA and invasive FFR between August 2013 and May 2018 were included. Commercially available software was used to extract quantitative plaque morphology inclusive of both vessel structure and composition. The extracted plaque morphology was then fed as inputs to an optimized artificial neural network to predict lesion-specific ischemia/hemodynamically significant CAD with performance validated by invasive FFR. ResultsA total of 122 lesions were considered, 59 (48%) had low FFR values. Plaque morphology-based FFR assessment achieved an area under the curve, sensitivity and specificity of 0.94, 0.90 and 0.81, respectively, versus 0.71, 0.71, and 0.50, respectively, for an optimized threshold applied to degree of stenosis. The optimized ridge regression model for continuous value estimation of FFR achieved a cross-correlation coefficient of 0.56 and regression slope of 0.59 using cross validation, versus 0.18 and 0.10 for an optimized threshold applied to degree of stenosis. ConclusionsOur results show that non-invasive plaque morphology-based FFR assessment may be used to predict lesion-specific ischemia resulting in hemodynamically significant CAD. This substantially outperforms degree of stenosis interpretation and has a comparable level of sensitivity and specificity relative to publicly reported results from computational fluid dynamics-based approaches.

Early Feasibility of Automated Artificial Intelligence Angiography Based Fractional Flow Reserve Estimation

Despite the evidence of improved patients' outcome, fractional flow reserve (FFR) is underused in current everyday practice. We aimed to evaluate the feasibility of a novel automated artificial intelligence angiography-based FFR software (AutocathFFR) as a decision supporting tool for interventional cardiologists. AutocathFFR was performed on angiographic images of patients who underwent coronary angiography with a pressure wire FFR measurement. Sensitivity and specificity for detection of FFR cut-off of 0.8 were calculated. Thirty-one patients were included in the present study, with a mean age of 64 ± 10 years, 80% were males, 32% patients had diabetes, 39% had previous percutaneous coronary intervention. The left anterior descending artery was the target vessel in 80% of patients. Automatic lesion detection was successful in all of the lesions with FFR value of ≤0.8. The sensitivity of AutocathFFR for predicting a wire based FFR ≤0.8 was 88% and the specificity for FFR >0.8 was 93%, with a positive predictive value of 94% and negative predictive value of 87%, indicating an accuracy level of 90% and area under the curve of 0.91. AutocathFFR has excellent accuracy in prediction of wire based FFR and is a promising technology that may facilitate appropriate decision and treatment choices for coronary artery disease patients.

Optical coherence tomography-defined plaque vulnerability in relation to functional stenosis severity stratified by fractional flow reserve and quantitative flow ratio.

We sought to investigate that the quantitative flow ratio (QFR) might be associated with optical coherence tomography (OCT)-defined plaque vulnerability. Both functional stenosis severity and plaque instability are related to adverse clinical outcomes in patients with coronary artery disease. Recent studies have shown an association between physiological stenosis severity and the presence of thin-cap fibroatheroma (TCFA). Measurement of QFR is a novel method for rapid computational estimation of fractional flow reserve (FFR). We investigated 327 de novo intermediate-to-severe coronary lesions in 295 stable patients who underwent OCT, FFR, and QFR computation. The lesions were divided into tertiles based on either the FFR or QFR. The OCT findings were compared among these tertiles of FFR and QFR. Each tertile was defined as follows: FFR-T1 (FFR < 0.72), FFR-T2 (0.72 ≤ FFR ≤ 0.79), and FFR-T3 (FFR > 0.79) and QFR-T1 (QFR < 0.73), QFR-T2 (0.73 ≤ QFR ≤ 0.78), and QFR-T3 (QFR > 0.78). The prevalence of OCT-defined TCFA showed graded differences in proportion to the QFR tertiles (25.0% vs. 12.8% vs. 6.6%, p = .003). An overall significant difference in the prevalence of TCFA was found among FFR tertiles (p = .048), although pairwise comparison did not show statistical significance. Compared with FFR-based classifications, the model that integrated the FFR and QFR categorization improved the incremental reclassification efficacy (relative integrated discrimination improvement, 0.069; p = .002; continuous net reclassification improvement, 0.356; p = .022) for predicting the presence of TCFA. OCT-defined plaque instability was associated with the QFR in angiographically intermediate-to-severe lesions. Compared with the FFR alone, the QFR can provide incremental efficacy in predicting the presence of TCFA.

Functional classification discordance in intermediate coronary stenoses between fractional flow reserve and angiography-based quantitative flow ratio

BackgroundMeasurement of the contrast-flow quantitative flow ratio (cQFR) is a novel method for rapid computational estimation of fractional flow reserve (FFR). Discordance between FFR and cQFR has not been completely...

Clinical and angiographic factors predicting fractional flow reserve and explaining the visual-functional mismatch in patients with intermediate coronary artery stenosis.

Visual-functional mismatch between coronary angiography and fractional flow reserve (FFR) has been reported, and the underlying reason remains poorly understood. Therefore, the relationship between angiographic measurements and FFR was evaluated, and predictors for FFR in intermediate coronary artery stenosis were determined. Consecutive 314 patients (405 lesions) with a lesion of 30-80% angiographic diameter stenosis who underwent invasive FFR were recruited. The myocardial area supplied by the coronary artery distal to the stenosis was evaluated using a modified version of the Bypass Angioplasty Revascularization Investigation (BARI) score. Participants underwent follow-up, and major cardiovascular events (MACE), including all-cause death, myocardial infarction (MI), and unplanned revascularization were recorded. Although % diameter stenosis was correlated with FFR (R = 0.279, P < 0.001), diameter stenosis-FFR mismatch was observed in 37.8% of the lesions. Although FFR values were not associated with clinical factors, such as age, sex, and comorbidities, it was correlated with minimal lumen diameter (MLD), diffuse lesion, presence of proximal lesion, and BARI score. In addition, the lesions in left anterior descending (LAD) coronary artery showed low FFR values compared with those in the left circumflex coronary artery or right coronary artery. In multivariate logistic analysis, MLD (β coefficient = 0.330), diffuse lesion (β coefficient = -0.266), proximal lesion (β coefficient = -0.144), BARI score (β coefficient = -0.219), and LAD lesion (β coefficient = -0.293) were all independent predictors for FFR value. The estimated FFR value based on these factors showed smaller mismatch and higher sensitivity. No difference was observed in the event rates for MACE and MI or revascularization between the FFR-guided and estimated FFR-guided strategies. MLD, diffuse lesion, proximal lesion, BARI score, and lesion vessel were independent predictors for FFR in intermediate coronary stenosis. Not only the extent of local lesion stenosis but also the amount of myocardial supply and the lesion location may determine the physiological significance and explain the visual-functional mismatch. The estimation of FFR by these factors may be useful in clinical practice.

P2705Hybrid QFR-FFR decision making strategy for revascularization

Abstract Background Measurement of the fractional flow reserve (FFR) has become a standard practice for revascularization decision-making in evaluating the functional significance of angiographically intermediate epicardial coronary stenosis. The quantitative flow ratio (QFR) is a novel method for rapid computational estimation of FFR without pressure wire and vasodilator drugs. Purpose Nevertheless, the evidence was shown the clinical better outcome of coronary revascularization stratified by FFR, the adoption of FFR remains low. We hypothesized that combined QFR and FFR hybrid strategy could improve the physiological assessment without pressure wire and drugs. Methods and results We performed a post-hocanalysis of 549 vessels with angiographically intermediate stenosis in 549 patients who underwent measurement of FFR. The median FFR and QFR values were 0.81 (0.73–0.87) and 0.79 (0.74–0.87), respectively.The ischemic threshold was defined as 0.80 for both QFR and FFR measures. The diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the QFR for predicting an FFR of ≤0.80 were 86.2%, 71.9%, 78.9%, 74.5%, and 84.5%, respectively. The area under the receiver operating characteristic curve using the cut-off threshold of ≤0.80 for the FFR was 0.85 (95% confidence interval [CI], 0.81–0.88) for the QFR.In total, 433 (78.9%) and 116 (21.1%) lesions showed concordant and discordant FFR and QFR functional classifications, respectively. A hybrid QFR-FFR strategy was developed, by allowing deferral when QFR values providing negative predictive value greater than 90% and treat others when QFR values greater than that showing 90% positive predictive value, with adenosine being given only to patients with QFR in between those values. For the FFR cut-off (0.8), an QFR of &lt;0.73 could be used to confirm treatment (PPV of 90.7%), while an QFR value of &gt;0.83 could be used to defer revascularization (NPV of 90.0%). When QFR values fall between 0.73 and 0.84, adenosine is given for hyperemic induction and the FFR cut-off of 0.8 is used to guide revascularization. This hybrid QFR-FFR approach has a 95% agreement with an FFR-only decision making, and 285 lesions (51.9%) would have obviated the need of a pressure wire and adenosine. Hybrid QFR-FFR strategy Conclusions A hybrid QFR-FFR strategy for coronary revascularization could reduce the need of a pressure wire and vasodilator drugs, which may increase the penetration of functional assessment of coronary lesions.

P2703Impact of subtended myocardial mass on the assessment of functional ischemia as evaluated by FFR and QFR

Abstract Background The fractional flow reserve (FFR) becomes a standard practice for revascularization decision-making in evaluating the functional significance of angiographically intermediate epicardial coronary stenosis. The quantitative flow ratio (QFR) is a novel method for rapid computational estimation of FFR without pressure wire and hyperemic induction. FFR has been reported to be associated with not only epicardial stenosis but also subtended myocardial mass (Vsub). In contrast, the relationship between QFR and Vsub has not been clarified. Purpose We sought to examine if subtended myocardial mass (Vsub) assessed by coronary computed tomography showed a significant relationship with QFR in comparison with FFR. Methods and results We performed a post-hoc analysis of 152 territories (LAD 116, RCA 25 and LCX 11 lesions) with angiographically intermediate-to-severe stenosis in 152 patients who underwent FFR assessment. The median FFR and QFR values were 0.76 (0.64–0.84) and 0.76 (0.72–0.83), respectively. The median diameter stenosis (%DS) and minimum lumen diameter (MLD) were 54.5 (43.9–64.2) and 1.2 (0.9–1.6), respectively. In total, 123 (80.9%) and 29 (19.1%) lesions showed concordant and discordant FFR and QFR functional classifications, respectively. The ability of Vsub/MLD2 to discriminate lesions with FFR≤0.80 and QFR≤0.80 was assessed compared with QCA data. FFR values were associated with Vsub (R=0.37, P&lt;0.001). In contrast, a trend albeit no significant linear relationshipwas detected between QFR and Vsub (R=0.15, P=0.060). The area under the curve (AUC) of Vsub/MLD2 predicting FFR≤0.80 (0.88: 95% confidence interval [CI], 0.83–0.94)was significantly better than that of MLD (0.80: 95% [CI], 0.72–0.88) (P&lt;0.001). On the other hand, the AUC of Vsub/MLD2 predicting QFR≤0.80 (0.82: 95% [CI], 0.75–0.90) was similar to that of MLD (0.80: 95% [CI], 0.72–0.87) (P=0.276). Multivariate analysis showed that the value of Vsub/MLD2 was an independent predictor of FFR≤0.80 (odds ratio [OR]: 1.09, 95% [CI]: 1.03–1.15, P=0.002), whereas it was not an independent predictor of QFR≤0.80. Conclusions Subtended cardiac mass volume derived from CT segmentation improved the diagnostic performance of angiography-derived parameters to identify ischemia-producing lesions when FFR used as a reference standard, whereas QFR showed non-significant relationship with subtended cardiac mass.

One-dimensional modeling of fractional flow reserve in coronary artery disease: Uncertainty quantification and Bayesian optimization

Non-invasive estimation of fractional flow reserve (FFR) values, the key index in the diagnosis of obstructive coronary artery disease, is a promising alternative to traditional way of performing invasive coronary angiography. With the advances in computational fluid dynamics (CFD), one can estimate FFR based on the solution obtained in a reconstructed coronary geometry from coronary computed tomography (CT) angiography. However, the computational cost to perform three-dimensional (3D) simulations has limited the use of CFD in most clinical settings. This could become more restrictive if one aims to quantify the uncertainty associated with FFR calculations due to the uncertainty in anatomic and physiologic properties as a significant number of 3D simulations is required to sample a relatively large parametric space. We have developed a predictive probabilistic model of FFR, which quantifies the uncertainty of the predicted values with significantly lower computational costs. Based on global sensitivity analysis, we first identify the important physiologic and anatomic parameters that impact the predictions of FFR. Our approach is to employ one-dimensional blood flow simulations of coronary trees that offer fast FFR predictions with uncertainty quantification in computing blood pressure and flow distributions within the coronaries. This is complemented with a multifidelity algorithm that is used to infer their optimal values using available patient-specific clinical measurements.

FUNCTIONAL CLASSIFICATION DISCORDANCE IN INTERMEDIATE CORONARY STENOSES BETWEEN FRACTIONAL FLOW RESERVE AND ANGIOGRAPHY-BASED QUANTITATIVE FLOW RATIO

Measurement of the contrast-flow quantitative flow ratio (cQFR) is a novel method for rapid computational estimation of fractional flow reserve (FFR). Discordance between FFR and cQFR has not been completely characterized. We performed a post-hoc analysis of 504 vessels with angiographically

Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry.

OBJECTIVE. Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFRML). However, attenuation values vary according to the tube voltage used, and it has not been shown whether this significantly affects the diagnostic performance of cFFR and cFFRML. Therefore, the purpose of this study is to retrospectively evaluate the effect of tube voltage on the diagnostic performance of cFFRML. MATERIALS AND METHODS. A total of 525 coronary vessels in 351 patients identified in the MACHINE consortium registry were evaluated in terms of invasively measured FFR and cFFRML. CCTA examinations were performed with a tube voltage of 80, 100, or 120 kVp. For each tube voltage value, correlation (assessed by Spearman rank correlation coefficient), agreement (evaluated by intraclass correlation coefficient and Bland-Altman plot analysis), and diagnostic performance (based on ROC AUC value, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of the cFFRML in terms of detection of significant stenosis were calculated. RESULTS. For tube voltages of 80, 100, and 120 kVp, the Spearman correlation coefficient for cFFRML in relation to the invasively measured FFR value was ρ = 0.684, ρ = 0.622, and ρ = 0.669, respectively (p < 0.001 for all). The corresponding intraclass correlation coefficient was 0.78, 0.76, and 0.77, respectively (p < 0.001 for all). Sensitivity was 100.0%, 73.5%, and 85.0%, and specificity was 76.2%, 79.0%, and 72.8% for tube voltages of 80, 100, and 120 kVp, respectively. The ROC AUC value was 0.90, 0.82, and 0.80 for 80, 100, and 120 kVp, respectively (p < 0.001 for all). CONCLUSION. CCTA-derived cFFRML is a robust method, and its performance does not vary significantly between examinations performed using tube voltages of 100 kVp and 120 kVp. However, because of rapid advancements in CT and postprocessing technology, further research is needed.

Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve

In this work we propose to validate the predictive capabilities of one-dimensional (1D) blood flow models with full three-dimensional (3D) models in the context of patient-specific coronary hemodynamics in hyperemic conditions. Such conditions mimic the state of coronary circulation during the acquisition of the Fractional Flow Reserve (FFR) index. Demonstrating that 1D models accurately reproduce FFR estimates obtained with 3D models has implications in the approach to computationally estimate FFR. To this end, a sample of 20 patients was employed from which 29 3D geometries of arterial trees were constructed, 9 obtained from coronary computed tomography angiography (CCTA) and 20 from intra-vascular ultrasound (IVUS). For each 3D arterial model, a 1D counterpart was generated. The same outflow and inlet pressure boundary conditions were applied to both (3D and 1D) models. In the 1D setting, pressure losses at stenoses and bifurcations were accounted for through specific lumped models. Comparisons between 1D models (FFR1D) and 3D models (FFR3D) were performed in terms of predicted FFR value. Compared to FFR3D, FFR1D resulted with a difference of 0.00 ± 0.03 and overall predictive capability AUC, Acc, Spe, Sen, PPV and NPV of 0.97, 0.98, 0.90, 0.99, 0.82, and 0.99, with an FFR threshold of 0.8. We conclude that inexpensive FFR1D simulations can be reliably used as a surrogate of demanding FFR3D computations.

Uncertainty Quantification and Sensitivity Analysis for Computational FFR Estimation in Stable Coronary Artery Disease

The main objectives of this study are to validate a reduced-order model for the estimation of the fractional flow reserve (FFR) index based on blood flow simulations that incorporate clinical imaging and patient-specific characteristics, and to assess the uncertainty of FFR predictions with respect to input data on a per patient basis. We consider 13 patients with symptoms of stable coronary artery disease for which 24 invasive FFR measurements are available. We perform an extensive sensitivity analysis on the parameters related to the construction of a reduced-order (hybrid 1D-0D) model for FFR predictions. Next we define an optimal setting by comparing reduced-order model predictions with solutions based on the 3D incompressible Navier-Stokes equations. Finally, we characterize prediction uncertainty with respect to input data and identify the most influential inputs by means of sensitivity analysis. Agreement between FFR computed by the reduced-order model and by the full 3D model was satisfactory, with a bias ([Formula: see text]) of [Formula: see text] at the 24 measured locations. Moreover, the uncertainty related to the factor by which peripheral resistance is reduced from baseline to hyperemic conditions proved to be the most influential parameter for FFR predictions, whereas uncertainty in stenosis geometry had greater effect in cases with low FFR. Model errors related to solving a simplified reduced-order model rather than a full 3D problem were small compared with uncertainty related to input data. Improved measurement of coronary blood flow has the potential to reduce uncertainty in computational FFR predictions significantly.