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

A review of computational methodologies to predict the fractional flow reserve in coronary arteries with stenosis

Computational methodologies for predicting the fractional flow reserve (FFR) in coronary arteries with stenosis have gained significant attention due to their potential impact on healthcare outcomes. Coronary artery disease is a leading cause of mortality worldwide, prompting the need for accurate diagnostic and treatment approaches. The use of medical image-based anatomical vascular geometries in computational fluid dynamics (CFD) simulations to evaluate the hemodynamics has emerged as a promising tool in the medical field. This comprehensive review aims to explore the state-of-the-art computational methodologies focusing on the possible considerations. Key aspects include the rheology of blood, boundary conditions, fluid-structure interaction (FSI) between blood and the arterial wall, and multiscale modelling (MM) of stenosis. Through an in-depth analysis of the literature, the goal is to obtain an overview of the major achievements regarding non-invasive methods to compute FFR and to identify existing gaps and challenges that inform further advances in the field. This research has the major objective of improving the current diagnostic capabilities and enhancing patient care in the context of cardiovascular diseases.

A simple angio-based coronary flow assessment of culprit vessels in primary percutaneous coronary intervention is associated with long-term prognosis after ST-segment-elevation myocardial infarction

BACKGROUND

Despite prompt reperfusion, the risk of adverse clinical outcomes following ST-segment-elevation myocardial infarction (STEMI) remains pronounced, owing partly to suboptimal reperfusion. However, coronary functional evaluation is seldom feasible during primary percutaneous coronary intervention (PPCI). We aimed to examine the clinical implication of a simple coronary assessment based on single-angiographic view (μQFR) during PPCI in discriminating impaired coronary flow and adverse outcomes for STEMI.

METHODS

STEMI Patients undergoing successful PPCI were enrolled and followed up prospectively from 4 medical centers in China. Post-PPCI μQFR of culprit vessels were analyzed. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiac death, non-fatal MI, ischemia-driven target-vessel revascularization and readmission for heart failure.

RESULTS

A total of 570 patients with STEMI were enrolled, and post-PCI μQFR was analyzable in 557 (97.7%) patients, with a median of 0.94. Patients with low post-PCI μQFR showed higher incidence of adverse outcomes than those with high μQFR, showing a 2.5-fold increase in the risk for MACE (hazard ratio: 2.51, 95% confidence intervals: 1.33 to 4.72; P = 0.004). Moreover, post-PCI μQFR significantly increased discriminant ability for the occurrence of MACE when added to traditional GRACE risk score for STEMI (integrated discrimination improvement: 0.029; net reclassification index: 0.229; P < 0.05).

CONCLUSIONS

A low μQFR of culprit vessel in PPCI is independently associated with worse clinical outcomes in patients with STEMI. The single-angiographic-view-based coronary evaluation is a feasible tool for discriminating poor prognosis and could serve as a valuable complement in risk stratification for STEMI.

Advancements and future perspectives in coronary angiography-derived fractional flow reserve

Angiography-derived fractional flow reserve (FFR) has emerged as a non-invasive technique to assess the functional significance of coronary artery stenoses. The clinical applications of angiography-derived FFR span a wide range of scenarios, including assessing intermediate coronary lesions and guiding revascularization decisions. This review paper aims to provide an overview of angiography-derived FFR, including its principles, clinical applications, and evidence supporting its accuracy and utility. Lastly, the review discusses future directions and ongoing research in the field, including the integration of angiography-derived FFR into routine clinical practice.

Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.

Simulation of coronary fractional flow reserve and whole-cycle flow based on optical coherence tomography in individual patients with coronary artery disease

Computer simulations of coronary fractional flow reserve (FFR) based on coronary imaging have emerged as an attractive alternative to invasive measurements. However, most methods are proprietary and employ non-physiological assumptions. Our aims were to develop and validate a physiologically realistic open-source simulation model for coronary flow, and to use this model to predict FFR based on intracoronary optical coherence tomography (OCT) data in individual patients. We included patients undergoing elective coronary angiography with angiographic borderline coronary stenosis. Invasive measurements of coronary hyperemic pressure and absolute flow and OCT imaging were performed. A computer model of coronary flow incorporating pulsatile flow and the effect of left ventricular contraction was developed and calibrated, and patient-specific flow simulation was performed. Forty-eight coronary arteries from 41 patients were included in the analysis. Average FFR was 0.79 ± 0.14, and 50% had FFR ≤ 0.80. Correlation between simulated and measured FFR was high (r = 0.83, p < 0.001). Average difference between simulated FFR and observed FFR in individual patients was - 0.009 ± 0.076. Overall diagnostic accuracy for simulated FFR ≤ 0.80 in predicting observed FFR ≤ 0.80 was 0.88 (0.75-0.95) with sensitivity 0.79 (0.58-0.93) and specificity 0.96 (0.79-1.00). The positive predictive value was 0.95 (0.75-1.00) and the negative predictive value was 0.82 (0.63-0.94). In conclusion, realistic simulations of whole-cycle coronary flow can be produced based on intracoronary OCT data with a new, computationally simple simulation model. Simulated FFR had moderate numerical agreement with observed FFR and a good diagnostic accuracy for predicting hemodynamic significance of coronary stenoses.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning

BACKGROUND

Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance.

METHODS

AI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL).

RESULTS

AI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections.

CONCLUSION

AI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

Comprehensive Angiography-Derived Functional Assessment of Epicardial and Microvascular Coronary Disease. Correlation With Non-invasive Myocardial Stress Imaging

Coronary angiography (CA) is poorly correlated with non-invasive myocardial stress imaging (NSI) and myocardial ischemia is often observed in patients with unobstructed coronary arteries. Moreover, the diagnostic performance of combined epicardial and microcirculatory angiography-derived physiological assessment and its correlation with NSI remains unknown. A total of 917 coronary vessels in 319 patients who underwent both CA and NSI were included in this multicenter observational retrospective analysis. Quantitative flow ratio (QFR) and angiography-derived index of microcirculatory resistance (IMRangio) analyses were performed to estimate coronary epicardial and microcirculatory function respectively. NSI demonstrated evidence of myocardial ischemia in 76% of the cases. IMRangio (36 [22 to 50] vs 29 [21 to 41], p <0.001) was significantly higher and QFR (0.92 [0.78 to 0.99] vs 0.97 [0.91 to 0.99], p <0.001) was significantly lower in vessels subtending ischemic territories. Overall, the diagnostic accuracy of QFR was moderate (area under the curve of receiver operating characteristic [AUCROC] 0.632 [95% confidence interval [CI] 0.589 to 0.674], p <0.0001) but it was higher in patients with normal microcirculatory function (AUCROC = 0.726 [95% CI 0.669 to 0.784], p <0.0001, p Value for AUCROC comparison = 0.009). Combined QFR/IMRangio assessment provided incremental diagnostic performance compared with the evaluation of epicardial or microcirculatory districts in isolation (p Value for AUC comparison <0.0001) and it was able to identify the predominant mechanism of myocardial ischemia in 77% of the patients with positive NSI. Our study suggests the value of a combined angiography-derived assessment of epicardial and microvascular function for the definition of the predominant mechanism of myocardial ischemia in patients with suspected coronary artery disease.

Virtual Hemodynamic Assessment of Coronary Lesions: The Advent of Functional Angiography and Coronary Imaging

The fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive and invasive imaging modalities. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy.

METHODS

Search of the bibliographic database for articles published from 2011 to 2023 using the following search terms: invasive FFR and non-invasive FFR. Pooled analysis of the sensitivity and specificity, with the corresponding confidence intervals from 32% to 94%. In addition, the summary processing times were determined.

RESULTS

In total, 24 studies published between 2011 and 2023 were included, with a total of 13,591 patients and 3345 vessels. The diagnostic accuracy of the invasive and non-invasive techniques at the per-patient level was 89% (95% CI, 85-92%) and 76% (95% CI, 61-80%), respectively, while on the per-vessel basis, it was 92% (95% CI, 82-88%) and 81% (95% CI, 75-87%), respectively.

CONCLUSION

These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice.

Coronary Physiology as Part of a State-of-the-Art Percutaneous Coronary Intervention Strategy: Lessons from SYNTAX II and Beyond

The use of coronary physiology allows for rational decision making at the time of PCI, contributing to better patient outcomes. Yet, coronary physiology is only one aspect of optimal revascularization. State-of-the-art PCI must also consider other important aspects such as intracoronary imaging guidance and specific procedural expertise, as tested in the SYNTAX II study. In this review, we highlight the technical aspects pertaining to the use of physiology as used in that trial and offer a glimpse into the future with emerging physiologic metrics, including functional coronary angiography, which have already established themselves as useful indices to guide decision making.

Development and validation of a model integrating clinical and coronary lesion-based functional assessment for long-term risk prediction in PCI patients

OBJECTIVES

To establish a scoring system combining the ACEF score and the quantitative blood flow ratio (QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention (PCI).

METHODS

In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263 consecutive cases of CAD patients after PCI in PANDA III trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.

RESULTS

In both the Random Forest Model and the DeepSurv Model, age, renal function (creatinine), cardiac function (LVEF) and post-PCI coronary physiological index (QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age (years)/EF (%) + 1 (if creatinine ≥ 2.0 mg/dL) + 1 (if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination (C-statistic = 0.651; 95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration (Hosmer-Lemeshow χ2 = 7.070; P = 0.529) for predicting 2-year patient-oriented composite endpoint (POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan-Meier analysis (adjusted HR = 1.89; 95% CI: 1.18-3.04; log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group.

CONCLUSIONS

An improved scoring system combining clinical and coronary lesion-based functional variables (ACEF-QFR) was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.

The diagnostic performance of quantitative flow ratio and perfusion imaging in patients with prior coronary artery disease

AIMS

In chronic coronary syndrome (CCS) patients with documented coronary artery disease (CAD), ischaemia detection by myocardial perfusion imaging (MPI) and an invasive approach are viable diagnostic strategies. We compared the diagnostic performance of quantitative flow ratio (QFR) with single-photon emission computed tomography (SPECT), positron emission tomography (PET), and cardiac magnetic resonance imaging (CMR) in patients with prior CAD [previous percutaneous coronary intervention (PCI) and/or myocardial infarction (MI)].

METHODS AND RESULTS

This PACIFIC-2 sub-study evaluated 189 CCS patients with prior CAD for inclusion. Patients underwent SPECT, PET, and CMR followed by invasive coronary angiography with fractional flow reserve (FFR) measurements of all major coronary arteries (N = 567), except for vessels with a sub-total or chronic total occlusion. Quantitative flow ratio computation was attempted in 488 (86%) vessels with measured FFR available (FFR ≤0.80 defined haemodynamically significant CAD). Quantitative flow ratio analysis was successful in 334 (68%) vessels among 166 patients and demonstrated a higher accuracy (84%) and sensitivity (72%) compared with SPECT (66%, P < 0.001 and 46%, P = 0.001), PET (65%, P < 0.001 and 58%, P = 0.032), and CMR (72%, P < 0.001 and 33%, P < 0.001). The specificity of QFR (87%) was similar to that of CMR (83%, P = 0.123) but higher than that of SPECT (71%, P < 0.001) and PET (67%, P < 0.001). Lastly, QFR exhibited a higher area under the receiver operating characteristic curve (0.89) than SPECT (0.57, P < 0.001), PET (0.66, P < 0.001), and CMR (0.60, P < 0.001).

CONCLUSION

QFR correlated better with FFR in patients with prior CAD than MPI, as reflected in the higher diagnostic performance measures for detecting FFR-defined, vessel-specific, significant CAD.

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography

BACKGROUND

Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction, limiting their application in the catheterization room.

PURPOSE

This study aims to propose rank-based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA.

METHODS

Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per-image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner (ESEN). Five-fold cross-validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients.

RESULTS

The selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one-sixth of a second.

CONCLUSION

Proposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real-time QCA-based diagnostic methods in routine clinical settings.

Intravascular Ultrasound-Based Fractional Flow Reserve for Predicting Prognosis after Percutaneous Coronary Intervention

AccuFFRivus is an alternative to fractional flow reserve (FFR) based on intravascular ultrasound (IVUS) images for functional assessment of coronary stenosis. However, its prognostic impact in patients undergoing percutaneous coronary intervention (PCI) is still unclear. This retrospective study aimed to investigate the capability of AccuFFRivus in predicting prognosis. AccuFFRivus was calculated based on postoperative angiographic and IVUS images. Vessel-oriented clinical events (VOCE) at 2 years were recorded and analyzed. A total of 131 participants with 131 vessels were included in the study. VOCE occurred in 15 patients during 2-year follow-up. AccuFFRivus after PCI (post-AccuFFRivus) was significantly higher in the non-VOCE group than in the VOCE group (0.95 ± 0.03 vs. 0.91 ± 0.02, p < 0.001). Multivariate Cox regression showed that AccuFFRivus ≤ 0.94 was a strong independent predictor of VOCE during 2-year follow-up (hazard ratio 23.76, 95% confidence interval: 3.04-185.81, p < 0.001). The left panel displays the Receiver operating characteristics (ROC) curves of postoperative parameters (post-AccuFFRivus and post-MLA) versus vessel-oriented clinical events (VOCE) occurrence within 2-year follow-up. The right panel demonstrates Kaplan-Meier curves of VOCE stratified by the optimal cut-off of post-AccuFFRivus.

Angiography-based coronary microvascular assessment with and without intracoronary pressure measurements: a systematic review

BACKGROUND

In recent years, several indices have been proposed for quantifying coronary microvascular resistance. We intended to conduct a comprehensive review that systematically evaluates indices of microvascular resistance derived from angiography.

OBJECTIVE

The objective of this study was to identify and analyze angiography-derived indices of microvascular resistance that have been validated against an invasive reference method. We aimed to compare their limits of agreement with their reference methods and explore their advantages and inherent limitations.

METHODS AND RESULTS

We searched PubMed from inception until 2022 for studies on different techniques for quantifying microvascular resistance. Seven studies met the inclusion criteria. Five studies included techniques that applied calculations based solely on invasive angiography, and were validated against invasively measured thermodilution-derived index of microvascular resistance. The remaining two studies combined angiography with invasively measured intracoronary pressure data, and were validated against invasive Doppler measurements. We converted the ± 1.96 standard deviation limits of agreement with the reference method from the seven studies into percentages relative to the cut-off value of the reference method. The lower limits of agreement for angiography-based methods ranged from - 122 to - 60%, while the upper limits ranged from 74 to 135%. The range of the limits of agreement was considerably lower for the two combined angiography- and pressure-based methods, standing at - 52 to 60% and - 25 to 27%.

CONCLUSION

Our findings suggest that combined angiography- and pressure-based methods provide a more reliable assessment of microvascular resistance compared to methods relying solely on angiography. Central illustration. Comparative assessment of image-based methods quantifying microvascular resistance with and without intracoronary pressure measurements. Angiography-based methods rely on angiography alone to calculate the microvascular resistance by utilizing angiographic frame counting to extrapolate coronary flow (Q) and subsequently deriving distal coronary pressure using fluid dynamic equations. Combined angiography- and pressure-based methods utilize invasive intracoronary pressure gradients measured during rest and maximal vasodilation to determine coronary flow in their calculation of microvascular resistance. The combined methods showed more acceptable levels of agreement with their reference methods compared to angiography-based methods alone.

Long-term prognostic value of quantitative-flow-ratio-concordant revascularization in stable coronary artery disease

OBJECTIVES

Confirming the prognostic value of global QFR and evaluating the long-term prognosis of QFR-concordant therapy in stable coronary artery disease.

BACKGROUND

Wire-based functional evaluation of coronary disease is linked to patient's prognosis. Quantitative Flow Ratio (QFR) is a newer index of computational physiology, linked to clinical outcomes and prognosis at 1 year follow-up. Long-term prognosis of QFR-concordant revascularization in stable coronary artery disease is however unknown hitherto.

METHODS

Consecutive patients with stable coronary disease undergoing coronary angiography were included. Centralized and blinded QFR analysis of three coronary territories was performed. Three vessel QFR (3vQFR) was defined as the sum of the basal QFR of each coronary territory. QFR-concordant revascularization was met if all significant lesions (QFR ≤ 0.80) were revascularized and all non-significant lesions (QFR > 0.80) were not; otherwise, the case was defined as QFR-discordant revascularization. Patient-oriented composite end-point (POCE) of cardiac death, myocardial infarction and unscheduled revascularization was the primary endpoint.

RESULTS

A total of 803 patients from six high-volume centers were included. Canadian Cardiovascular Society (CCS) class II angina was the most frequent (48.9%) clinical presentation. Median of follow-up was 68.8 months. 3vQFR was an independent predictor of POCE (HR 1.79 CI95% 1.01-3.18), with 2.75 as optimal cut-off value, irrespective of the therapy received. QFR-discordant revascularization (QFR+/Revascularization- or QFR-/Revascularization+) was an independent predictor of POCE in multivariate analysis (HR 1.65, CI 95% 1.03-2.64).

CONCLUSION

Global burden of epicardial coronary atherosclerosis, as evaluated by 3vQFR, as well as QFR-discordant therapy are independent predictors of adverse clinical outcome at long-term follow-up in stable coronary artery disease.

Diagnostic performance of angiography-derived fractional flow reserve analysis based on bifurcation fractal law for assessing hemodynamic significance of coronary stenosis

OBJECTIVES

Blood flow into the side branch affects the calculation of coronary angiography-derived fractional flow reserve (FFR), called Angio-FFR. Neglecting or improperly compensating for the side branch flow may decrease the diagnostic accuracy of Angio-FFR. This study aims to evaluate the diagnostic accuracy of a novel Angio-FFR analysis that considers the side branch flow based on the bifurcation fractal law.

METHODS

A one-dimensional reduced-order model based on the vessel segment was used to perform Angio-FFR analysis. The main epicardial coronary artery was divided into several segments according to the bifurcation nodes. Side branch flow was quantified using the bifurcation fractal law to correct the blood flow in each vessel segment. In order to verify the diagnostic performance of our Angio-FFR analysis, two other computational methods were taken as control groups: (i) FFR_s: FFR calculated by delineating the coronary artery tree to consider side branch flow, (ii) FFR_n: FFR calculated by just delineating the main epicardial coronary artery and neglecting the side branch flow.

RESULTS

The analysis of 159 vessels from 119 patients showed that our Anio-FFR calculation method had comparable diagnostic accuracy to FFR_s and provided significantly higher diagnostic accuracy than that of FFR_n. In addition, using invasive FFR as a reference, the Pearson correlation coefficients of Angio-FFR and FFRs were 0.92 and 0.91, respectively, while that of FFR_n was only 0.85.

CONCLUSIONS

Our Angio-FFR analysis has demonstrated good diagnostic performance in assessing the hemodynamic significance of coronary stenosis by using the bifurcation fractal law to compensate for side branch flow.

CLINICAL RELEVANCE STATEMENT

Bifurcation fractal law can be used to compensate for side branch flow during the Angio-FFR calculation of the main epicardial vessel. Compensating for side branch flow can improve the ability of Angio-FFR to diagnose stenosis functional severity.

KEY POINTS

• The bifurcation fractal law could accurately estimate the blood flow from the proximal main vessel into the main branch, thus compensating for the side branch flow. • Angiography-derived FFR based on the bifurcation fractal law is feasible to evaluate the target diseased coronary artery without delineating the side branch.

Fast Computational Approaches to Derive Fractional Pressure Ratio in Patients with Extracranial or Intracranial Symptomatic Stenosis

OBJECTIVE

We aimed to evaluate the performance of fast and straightforward Murray law-based quantitative flow ratio (μQFR) computation in cerebrovascular stenosis.

METHODS

A total of 30 patients with symptomatic stenosis of 50%-70% luminal stenosis and underwent fractional pressure ratio (FPR) assessment at our hospital were included in the present study. μQFR was applied to the interrogated vessel. An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of cerebrovascular stenosis. We used invasive FPRs as a reference standard. Pearson's correlation coefficient (r) was used to assess the correlation strength between the μQFR and FPR, and Bland-Altman plots were used to evaluate the agreement between the μQFR and FPR. An analysis of the receiver operating characteristic was used to evaluate the performance of μQFR.

RESULTS

Our results displayed a strong positive correlations (r = 0.92; P < 0.001) between the μQFR and pressure wire FPR. Excellent agreement was observed between the μQFR and FPR with a mean difference of 0.01 ± 0.08 (range, -0.16 to 0.14; P = 0.263). The overall accuracy for identifying an FPR of ≤0.7 was 92% (95% confidence interval [CI], 85%-100%). The area under the receiver operating characteristic curve was higher for the μQFR (0.92; 95% CI, 0.81-0.98) than for diameter stenosis (0.88; 95% CI, 0.75-0.95). The positive likelihood ratio was 3.9 for the μQFR with a negative likelihood ratio of 0.

CONCLUSIONS

The μQFR computation has a strong correlation and agrees with the FPR calculated from the pressure wire. Therefore, the μQFR might provide an essential therapeutic aid for patients with symptomatic stenosis.

Clinical Value of Computational Angiography-derived Fractional Flow Reserve in Stable Coronary Artery Disease

The utilization of FFR remains low. Our study evaluated the per-vessel prognostic value of computational pressure-flow dynamics-derived FFR (caFFR) among patients with stable coronary artery disease. A total of 3329 vessels from 1308 patients were included and analysed. They were stratified into ischaemic (caFFR ≤ 0.8) and non-ischaemic (caFFR > 0.8) cohorts, and the associations between PCI and outcomes were evaluated. The third cohort comprised all included vessels, and the associations between treatment adherent-to-caFFR (PCI in vessels with caFFR ≤ 0.8 and no PCI in vessels with caFFR > 0.8) and outcomes were evaluated. The primary outcome was VOCE, defined as a composite of vessel-related cardiovascular mortality, non-fatal myocardial infarction, and repeat revascularization. PCI was associated with a lower 3-year risk of VOCE in the ischaemic cohort (HR, 0.44; 95% CI, 0.26-0.74; P = 0.002) but not in the non-ischaemic cohort. The risk of VOCE was lower in the adherent-to-caFFR group (n = 2649) (HR, 0.69; 95% CI, 0.48-0.98; P = 0.039). A novel index that uses coronary angiography images to estimate FFR may have substantial clinical value in guiding management among patients with stable coronary artery disease.

Clinical prognostic value of a novel quantitative flow ratio from a single projection in patients with coronary bifurcation lesions treated with the provisional approach

Background

A novel quantitative flow ratio (μQFR) for bifurcated coronary vessels, derived from a single projection, has been recently reported. Provisional stenting is effective for most bifurcation lesions. However, the clinical value of the side branch (SB) μQFR in patients with coronary bifurcation lesions undergoing provisional stenting remains unclear.

Aims

This study aims to determine the clinical predictive value of the SB μQFR after provisional stenting in patients with coronary bifurcation lesions.

Methods

Between June 2015 and May 2018, 288 patients with true coronary bifurcation lesions who underwent a provisional approach without SB treatment (including predilation, kissing balloon inflation or stenting) were classified by an SB μQFR <0.8 (n=65) and ≥0.8 (n=223) groups. The primary endpoint was the three-year composite of target vessel failure (TVF), including cardiac death, target vessel myocardial infarction (TVMI), and revascularisation (TVR).

Results

Three years after the procedures, there were 43 (14.9%) TVFs, with 19 (29.2%) in the SB μQFR <0.8 and 24 (10.8%) in the SB μQFR ≥0.8 groups (adjusted hazard ratio [HR] 2.45, 95% confidence interval [CI] 1.39-5.54; p=0.003), mainly driven by increased TVMI (16.9% vs 5.4%, adjusted HR 3.29, 95% CI: 1.15-6.09; p=0.030) and TVR (15.4% vs 2.2%, adjusted HR 6.39, 95% CI: 2.04-13.48; p=0.007). Baseline diameter stenosis at the ostial SB and SB lesion length were the two predictors of an SB μQFR <0.8 immediately after stenting the main vessel, whereas previous percutaneous coronary intervention and an SB μQFR <0.8 were the two independent factors of 3-year TVF.

Conclusions

An SB μQFR <0.8 immediately after the provisional approach is strongly associated with clinical events. Further randomised studies with large patient populations are warranted.

Effects of remote ischemic preconditioning on coronary blood flow and microcirculation

This study aimed to determine the effect of short-term remote ischemic preconditioning (RIPC) on coronary blood flow and microcirculation function using the quantitative flow ratio (QFR) and index of microcirculatory resistance (IMR). We randomly divided 129 patients undergoing coronary angiography (CAG) into RIPC and control groups. Following the first CAG, we randomly divided the patients further into the unilateral upper limb and lower limb groups for four cycles of ischemia/reperfusion circulation; subsequently, we performed the second CAG. During each CAG, contrast-flow QFR (cQFR), fixed-flow QFR (fQFR), and IMR (in patients with cardiac syndrome X) were calculated and compared. We measured 253 coronary arteries in 129 patients. Compared to the control group, the average cQFR of the RIPC group increased significantly after RIPC. Additionally, 23 patients with cardiac syndrome X (IMR > 30) were included in this study. Compared to the control group, IMR and the difference between cQFR and fQFR (cQFR-fQFR) both decreased significantly after receiving RIPC. The application of RIPC can increase coronary blood flow and improve coronary microcirculation function.

Functional Assessment of Coronary Artery Stenosis from Coronary Angiography and Computed Tomography: Angio-FFR vs. CT-FFR

This study was designed to compare the diagnostic performance of angio-FFR and CT-FFR for detecting hemodynamically significant coronary stenosis. Angio-FFR and CT-FFR were measured in 110 patients (139 vessels) with stable coronary disease using invasive FFR as the reference standard. On per-patient basis, angio-FFR was highly correlated with FFR (r =0.78, p <0.001), while the correlation was moderate between CT-FFR and FFR (r =0.68, p <0.001). Diagnostic accuracy, sensitivity, and specificity for angio-FFR were 94.6%, 91.4%, and 96.0%, respectively; and those of CT-FFR were 91.8%, 91.4%, and 92%, respectively. Bland-Altman analysis showed that angio-FFR had a larger average difference and a smaller root mean squared deviation than CT-FFR compared with FFR (-0.014±0.056 vs. 0.0003±0.072). Angio-FFR had a slightly higher AUC than that of CT-FFR (0.946 vs. 0.935, p =0.750). Angio-FFR and CT-FFR computed from coronary images could be accurate and efficient computational tools for detecting lesion-specific ischemia of coronary artery stenosis. Angio-FFR and CT-FFR calculated based on the two types of images can both accurately diagnose functional ischemia of coronary stenosis. CT-FFR can act as a gatekeeper to the catheter room, assisting doctors in determining whether patients need to be screened by coronary angiography. Angio-FFR can be used in the catheter room to determine the functional significant stenosis for helping decision-making in revascularization.

Intravascular Imaging versus Physiological Assessment versus Biomechanics-Which Is a Better Guide for Coronary Revascularization

Today, coronary artery disease (CAD) continues to be a prominent cause of death worldwide. A reliable assessment of coronary stenosis represents a prerequisite for the appropriate management of CAD. Nevertheless, there are still major challenges pertaining to some limitations of current imaging and functional diagnostic modalities. The present review summarizes the current data on invasive functional and intracoronary imaging assessment using optical coherence tomography (OCT), and intravascular ultrasound (IVUS). Amongst the functional parameters-on top of fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR)-we point to novel angiography-based measures such as quantitative flow ratio (QFR), vessel fractional flow reserve (vFFR), angiography-derived fractional flow reserve (FFRangio), and computed tomography-derived flow fractional reserve (FFR-CT), as well as hybrid approaches focusing on optical flow ratio (OFR), computational fluid dynamics and attempts to quantify the forces exaggerated by blood on the coronary plaque and vessel wall.

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.

Coronary Angiography Upgraded by Imaging Post-Processing: Present and Future Directions

Advances in computer technology and image processing now allow us to obtain from angiographic images a large variety of information on coronary physiology without the use of a guide-wire as a diagnostic information equivalent to FFR and iFR but also information allowing for the performance of a real virtual percutaneous coronary intervention (PCI) and finally the ability to obtain information to optimize the results of PCI. With specific software, it is now possible to have a real upgrading of invasive coronary angiography. In this review, we present the different advances in this field and discuss the future perspectives offered by this technology.

Anonymous Comparison of Various Angiography-Derived Fractional Flow Reserve Software With Pressure-Derived Physiological Assessment

BACKGROUND

Software to compute angiography-derived fractional flow reserve (angio-FFR) have been validated against pressure wire-derived fractional flow reserve (PW-FFR) with an area under the receiver-operating characteristic curve (AUC) of 0.93 to 0.97.

OBJECTIVES

The aim of this study was to investigate diagnostic accuracies of 5 angio-FFR software/methods by an independent core lab in a prospective cohort of 390 vessels with carefully documented sites of PW-FFR and pressure wire-derived instantaneous wave-free ratio.

METHODS

One "matcher investigator" colocalized on angiography the sites of pressure wire measurement with angio-FFR measurements and provided the same 2 optimal angiographic views and frame selection to independent analysts who were blinded to invasive physiological results and results from other software. The results were anonymized and randomly presented. The AUC of each angio-FFR was compared with 2-dimensional quantitative coronary angiography (QCA) percent diameter stenosis (%DS) using a 2-tailed paired comparison of AUC.

RESULTS

All 5 software/methods yielded a high proportion of analyzable vessels (A: 100%, B: 100%, C: 92.1%, D: 99.5%, and E: 92.1%). The AUCs for predicting fractional flow reserve ≤0.8 for software A, B, C, D, E, and 2-dimensional QCA %DS were 0.75, 0.74, 0.74, 0.73, 0.73, and 0.65, respectively. The AUC for each angio-FFR was significantly greater than that for 2-dimensional QCA %DS.

CONCLUSIONS

This head-to-head comparison by an independent core lab demonstrated that the diagnostic accuracy of various angio-FFR software for predicting PW-FFR ≤0.80 was useful, with a higher discrimination compared with 2-dimensional QCA %DS; however, it did not reach the diagnostic accuracy previously reported in validation studies of various vendors. Therefore, the intrinsic clinical value of "angiography-derived fractional flow reserve" requires confirmation in large clinical trials.

Accuracy of the angiography-based quantitative flow ratio in intermediate left main coronary artery lesions and comparison with visual estimation

BACKGROUND

Revascularization of left main coronary artery (LMCA) stenosis is mostly based on angiography. Indices based on angiography might increase accuracy of the decision, although they have been scarcely used in LMCA. The objective of this study is to study the diagnostic agreement of QFR (quantitative flow ratio) with wire-based fractional flow reserve (FFR) in LMCA lesions and to compare with visual severity assessment.

METHODS

In a series of patients with invasive FFR assessment of intermediate LMCA stenoses we retrospectively compared the measured value of QFR with that of FFR and the estimate of significance from angiography.

RESULTS

107 QFR studies were included. The QFR intra-observer and inter-observer agreement was 87% and 82% respectively. The mean QFR-FFR difference was 0.047 ± 0.05 with a concordance of 90.7%, sensitivity 88.1%, specificity 92.3%, positive predictive value 88.1% and negative predictive value 92.3%. All these values were superior to those observed with the visual estimation which showed an intra- and inter-observer agreement of 73% and 72% respectively, besides 78% with the FFR value. The low diagnostic performance of the visual estimation and the acceptable performance of the QFR index measurement were observed in all subgroups analysed.

CONCLUSIONS

QFR allows an acceptable estimate of the FFR obtained with intracoronary pressure guidewire in intermediate LMCA lesions, and clearly superior to the assessment based on angiography alone. The decision to revascularize patients with moderate LMCA lesions should not be based solely on the degree of angiographic stenosis.

Comparison of vessel fractional flow reserve with invasive resting full-cycle ratio in patients with intermediate coronary lesions

BACKGROUND

Vessel fractional flow reserve (vFFR) is a novel angiography-derived index for the assessment of myocardial ischemia without the need for pressure wires and hyperemic agents. vFFR has demonstrated very good diagnostic performance compared with the hyperemic index fractional flow reserve (FFR). The aim of this study was to compare vFFR to the non-hyperemic pressure ratio resting full-cycle ratio (RFR).

METHODS

This was a retrospective, observational, single-center study of an all-comer cohort undergoing RFR assessment. Invasive coronary angiography was obtained without a dedicated vFFR acquisition protocol, and vFFR calculation was attempted in all vessels interrogated by RFR (1483 lesions of 1030 patients).

RESULTS

vFFR could be analyzed in 986 lesions from 705 patients. Median diameter stenosis was 37% (interquartile range (IQR): 30.0-44.0%), vFFR 0.86 (IQR: 0.81-0.91) and RFR 0.94 (IQR: (0.90-0.97). The correlation between vFFR and RFR was strong (r = 0.70, 95% confidence interval (CI): 0.66-0.74, p < 0.001). Using RFR ≤0.89 as reference, the sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy for vFFR were 77%, 93%, 77%, and 92% and 89%. vFFR yielded a high area under the curve (AUC) of 0.92 (95% CI: 0.90-0.94). The good diagnostic performance of vFFR was confirmed among subgroups of patients with diabetes, severe aortic stenosis, female gender and lesions located in the left anterior descending artery.

CONCLUSION

vFFR has a high diagnostic performance taking RFR as the reference standard for evaluating the functional significance of coronary stenoses.

Non-invasive Angiographic-based Fractional Flow Reserve: Technical Development, Clinical Implications, and Future Perspectives

New non- and less-invasive techniques have been developed to overcome the procedural and operator related burden of the fractional flow reserve (FFR) for the assessment of potentially significant stenosis in the coronary arteries. Virtual FFR-techniques can obviate the need for the additional flow or pressure wires as used for FFR measurements. This review provides an overview of the developments and validation of the virtual FFR-algorithms, states the challenges, discusses the upcoming clinical trials, and postulates the future role of virtual FFR in the clinical practice.

Validity and Diagnostic Performance of Computing Fractional Flow Reserve From 2-Dimensional Coronary Angiography Images

BACKGROUND

Measurement of fractional flow reserve (FFR) is the gold standard for determining the physiologic significance of coronary artery stenosis, but newer software programs can calculate the FFR from 2-dimensional angiography images.

METHODS

A retrospective analysis was conducted using the records of patients with intermediate coronary stenoses who had undergone adenosine FFR (aFFR). To calculate the computed FFR, a software program used simulated coronary blood flow using computational geometry constructed using at least 2 patient-specific angiographic images. Two cardiologists reviewed the angiograms and determined the computational FFR independently. Intraobserver variability was measured using κ analysis and the intraclass correlation coefficient. The correlation coefficient and Bland-Altman plots were used to assess the agreement between the calculated FFR and the aFFR.

RESULTS

A total of 146 patients were included, with 95 men and 51 women, with a mean (SD) age of 61.1 (9.5) y. The mean (SD) aFFR was 0.847 (0.072), and 41 patients (27.0%) had an aFFR of 0.80 or less. There was a strong intraobserver correlation between the computational FFRs (r = 0.808; P < .001; κ = 0.806; P < .001). There was also a strong correlation between aFFR and computational FFR (r = 0.820; P < .001) and good agreement on the Bland-Altman plot. The computational FFR had a high sensitivity (95.1%) and specificity (90.1%) for detecting an aFFR of 0.80 or less.

CONCLUSION

A novel software program provides a feasible method of calculating FFR from coronary angiography images without resorting to pharmacologically induced hyperemia.

Quantitative flow ratio-guided versus angiography-guided operation for valve disease accompanying coronary heart disease

Background

Valve replacement combined with coronary artery bypass graft (CABG) operation (VR + CABG) is usually associated with higher mortality and complication rates. Currently, angiography remains the most commonly used approach to guide CABG. The aim of this study is to investigate whether a quantitative flow ratio (QFR)-guided strategy can improve the clinical outcomes of VR + CABG.

Methods

Patients (n = 536) treated by VR + CABG between January 2018 and December 2021 were retrospectively assessed. In 116 patients, all lesions were revascularized entirely based on QFR (the QFR-guided group), whereas in 420 patients, all lesions were revascularized entirely based on angiography (the angiography-guided group). To minimize selection bias between the 2 groups, propensity score matching was performed at a ratio of 1:2. The primary endpoint of the study was the rate of major adverse cardiac and cerebrovascular events (MACCE) at 1-year, which was defined as a composite of cardiac mortality, myocardial infarction (MI), any repeat revascularization, and stroke.

Results

No statistically significant differences were observed in the baseline clinical characteristics between the QFR-guided and angiography-guided groups after propensity score matching. The mean age of all patients was 66.2 years [standard deviation (SD) = 8.3], 370 (69%) were men, the mean body-mass index of the population was 24.8 kg/m² (SD = 4.5), 129 (24%) had diabetes, and 229 (43%) had angina symptoms. When compared with the angiography-guided group, the QFR-guided group had a significantly shorter operative time (323 ± 60 min vs. 343 ± 71 min, P = 0.010), extra corporal circulation time (137 ± 38 min vs. 155 ± 62 min, P = 0.004), clamp time (73 ± 19 min vs. 81 ± 18 min, P < 0.001), and less intraoperative bleeding volume (640 ± 148 ml vs. 682 ± 166 ml, P = 0.022). Compared with the angiography-guided group, the 1-year MACCE was significantly lower in the QFR-guided group (6.9% vs. 14.7%, P = 0.036, hazard ratio = 0.455, 95% confidence interval: 0.211-0.982).

Conclusion

Our results raise the hypothesis that among patients who undergo VR + CABG, QFR-guided strategy is associated with optimized surgical procedure and a superior clinical outcome, as evidenced by a lower rate of MACCE at 1-year compared with conventional angiography-guided strategy.

Coronary Physiology as Part of a State-of-the-Art Percutaneous Coronary Intervention Strategy: Lessons from SYNTAX II and Beyond

The use of coronary physiology allows for rational decision making at the time of PCI, contributing to better patient outcomes. Yet, coronary physiology is only one aspect of optimal revascularization. State-of-the-art PCI must also consider other important aspects such as intracoronary imaging guidance and specific procedural expertise, as tested in the SYNTAX II study. In this review, we highlight the technical aspects pertaining to the use of physiology as used in that trial and offer a glimpse into the future with emerging physiologic metrics, including functional coronary angiography, which have already established themselves as useful indices to guide decision making.

Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

Background

Personalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR baseline ), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines.

Aims

To determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR invasive ) as gold standard.

Materials and Methods

Personalized coronary geometries ( N = 50 ) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR baseline , was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR baseline was validated against FFR invasive and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR streamlined was created by only incorporating the most sensitive inputs and FFR semi-streamlined additionally included patient-specific distal location.

Results

FFR baseline was validated against FFR invasive via correlation ( r = 0.714 , p < 0.001 ), agreement (mean difference: 0.01 ± 0.09 ), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR semi-streamlined provided identical diagnostic performance with FFR baseline . Compared to FFR baseline vs. FFR invasive , FFR streamlined vs. FFR invasive had decreased correlation ( r = 0.64 , p < 0.001 ), improved agreement (mean difference: 0.01 ± 0.08 ), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90).

Conclusion

Streamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement.

Prediction of functional results of percutaneous coronary interventions with virtual stenting and quantitative flow ratio

BACKGROUND

The clinical value of residual quantitative flow ratio (rQFR), a novel function of QFR technique, is unknown.

AIM

We investigated the clinical value of rQFR, aimed to predict residual ischemia after virtual percutaneous coronary intervention (vPCI).

METHODS

This is a substudy of the COE-PERSPECTIVE registry, which investigated the prognostic value of post-PCI fractional flow reserve (FFR). From pre-PCI angiograms, QFR and rQFR were analyzed and their diagnostic performance was assessed at blinded fashion using pre-PCI FFR and post-PCI FFR as reference, respectively. The prognostic value of rQFR after vPCI was assessed according to vessel-oriented composite outcome (VOCO) at 2 years.

RESULTS

We analyzed 274 patients (274 vessels) with FFR-based ischemic causing lesions (49%) from 555 screened patients. Pre-PCI QFR and FFR were 0.63 ± 0.10 and 0.66 ± 0.11 (R = 0.756, p < 0.001). rQFR after vPCI and FFR after real PCI were 0.93 ± 0.06 and 0.86 ± 0.07 (R = 0.528, p < 0.001). The mean difference between rQFR and post-PCI FFR was 0.068 (95% limit of agreement: -0.05 to 0.19). Diagnostic performance of rQFR to predict residual ischemia after PCI was good (area under the curve [AUC]: 0.856 [0.804-0.909], p < 0.001). rQFR predicted well the incidence of 2-year VOCO after index PCI (AUC: 0.712 [0.555-0.869], p = 0.041), being similar to that of actual post-PCI FFR (AUC: 0.691 [0.512-0.870], p = 0.061). rQFR ≤0.89 was associated with increased risk of 2-year VOCO (hazard ratio [HR]: 12.9 [2.32-71.3], p = 0.0035). This difference was mainly driven by a higher rate of target vessel revascularization (HR: 16.98 [2.33-123.29], p = 0.0051).

CONCLUSIONS

rQFR estimated from pre-PCI angiography and virtual coronary stenting mildly overestimated functional benefit of PCI. However, it well predicted suboptimal functional result and long-term vessel-related clinical events.

CLINICAL TRIAL REGISTRATION

Influence of fractional flow reserve on the Clinical OutcomEs of PERcutaneouS Coronary Intervention (COE-PESPECTIVE) Registry, NCT01873560.

Quantitative Flow Ratio Is Related to Anatomic Left Main Stem Lesion Parameters as Assessed by Intravascular Imaging

Introduction: Previously, an association between anatomic left main stem (LMS) lesion parameters, as described by intravascular ultrasound (IVUS) and fractional flow reserve (FFR), was shown. Quantitative flow ratio (QFR) is a novel, promising technique which can assess functional stenosis relevance based only on angiography. However, as little is known about the relationship between anatomic LMS parameters and QFR, it was thus investigated in this study. Methods: In 53 patients with LMS disease, we tested the association between anatomic assessment using OCT (n = 28) or IVUS (n = 25) on the one hand and functional assessment as determined by QFR on the other hand. LMS-QFR was measured using a dedicated approach, averaging QFR over left anterior descending (LAD) and circumflex (LCX) and manually limiting segment of interest to LMS. Results: The minimal luminal area of the LMS (LMS-MLA) as measured by intravascular imaging showed a consistent correlation with QFR (R = 0.61, p < 0.001). QFR could predict a LMS-MLA < 6 mm2 with very good diagnostic accuracy (AUC 0.919) and a LMS-MLA < 4.5 mm2 with good accuracy (AUC 0.798). Similar results were obtained for other stenosis parameters. Conclusions: QFR might be a valuable tool to assess LMS disease. Further studies focusing on patient outcomes are needed to further validate the effectiveness of this approach.

Artificial intelligence and cloud based platform for fully automated PCI guidance from coronary angiography-study protocol

Ischemic heart disease represent a heavy burden for the medical systems irrespective of the methods used for diagnosis and treatment of such patients in the daily medical routine. The present paper depicts the protocol of a study whose main aim is to develop, implement and test an artificial intelligence algorithm and cloud based platform for fully automated PCI guidance using coronary angiography images. We propose the utilisation of multiple artificial intelligence based models to produce three-dimensional coronary anatomy reconstruction and assess function- post-PCI FFR computation- for developing an extensive report describing and motivating the optimal PCI strategy selection. All the relevant artificial intelligence model outputs (anatomical and functional assessment–pre- and post-PCI) are presented to the clinician via a cloud platform, who can then take the utmost treatment decision. The physician will be provided with multiple scenarios and treatment possibilities for the same case allowing a real-time evaluation of the most appropriate PCI strategy planning and follow-up. The artificial intelligence algorithms and cloud based PCI selection workflow will be verified and validated in a pilot clinical study including subjects prospectively to compare the artificial intelligence services and results against annotations and invasive measurements.

Relation of quantitative flow ratio with transit time coronary artery bypass graft flow measurement

Background

Quantitative flow ratio (QFR) is a new functional index to assess the functional significance of coronary stenosis. While whether there is an association between QFR and transit-time flow measurement (TTFM) parameters of the target coronary artery has not been well addressed.

Methods

A total of 89 patients receiving the in situ left internal thoracic artery (LITA) grafts to the left anterior descending artery (LAD), and 19 patients undergoing the saphenous vein grafts (SVG) were enrolled in this retrospective study. The QFR value of the LAD was evaluated preoperatively. According to the QFR values, patients with the LITA to the LAD bypass grafts were divided into two groups (group A1: QFR < 0.75, group A2: QFR ≥ 0.75), and SVG patients were divided into two groups (V1 group: QFR < 0.75, V2 group: QFR ≥ 0.75).

Results

In groups A1 and A2, respectively, median graft flow (Qm) was 44 (34) mL/minute and 26.5 (30.0) ml/minute; median pulsatility index (PI) was 2.00 (1.00) and 2.65 (0.90). Significant differences were observed in Qm (P = 0.034) and PI (P = 0.030). And the correlation coefficients of the TTFM variables with QFR were Qm: r = r = −0.226, (P = 0.036), PI: r = 0.265 (P = 0.012) among the LITA to LAD population.

Conclusion

TTFM variables, especially the PI, of the LITA in situ graft to the LAD during Coronary artery bypass grafting (CABG) are strongly affected by preoperative QFR values. Moreover, in functionally mild coronary stenosis, the chance of competitive flow increases.

Diagnostic performance of quantitative flow ratio versus fractional flow reserve and resting full-cycle ratio in intermediate coronary lesions

BACKGROUND

Quantitative flow ratio (QFR) is a novel angiography-derived index aimed to assess the functional relevance of coronary stenoses without pressure wires and adenosine. Good diagnostic yield with the hyperemic fractional flow reserve (FFR) have been reported, while data on the comparison of QFR to non-hyperemic pressure ratios (NHPR) are scarce.

METHODS

In this retrospective, observational and single-center study with a large population representative of the real practice, we assessed and compared the diagnostic performance of contrast flow (cQFR) and fixed flow (fQFR) QFR against the NHPR resting full-cyle ratio (RFR) using FFR as reference standard.

RESULTS

A total of 626 lesions from 544 patients were investigated. Mean diameter stenosis, FFR, cQFR, fQFR and RFR were 44.8%, 0.842, 0.847, 0.857 and 0.912, respectively. The correlation between cQFR and FFR was stronger (r = 0.830, P < 0.001) compared to that between FFR and RFR (r = 0.777, P < 0.001) and between cQFR and RFR (r = 0.687, P < 0.001). Using FFR ≤0.80 as reference, the sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy for cQFR were 82%, 95%, 87%, 92%, and 91%, respectively. cQFR displayed a higher area under the curve (AUC) than fQFR and RFR (0.938 vs. 0.891 vs. 0.869, P < 0.01). The good diagnostic yield of cQFR appeared to be maintained in different clinical subsets including female gender, aortic valve stenosis and atrial fibrillation, and in different anatomical subsets including focal and non-focal lesions.

CONCLUSION

cQFR has a high and better diagnostic performance than the NHPR RFR in predicting FFR-based functional significance of coronary stenoses.

Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022;3:481-488. [PMID: 36712154 PMCID: PMC9707918 DOI: 10.1093/ehjdh/ztac045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Aims

Angiography-derived fractional flow reserve (angio-FFR) permits physiological lesion assessment without the need for an invasive pressure wire or induction of hyperaemia. However, accuracy is limited by assumptions made when defining the distal boundary, namely coronary microvascular resistance (CMVR). We sought to determine whether machine learning (ML) techniques could provide a patient-specific estimate of CMVR and therefore improve the accuracy of angio-FFR.

Methods and results

Patients with chronic coronary syndromes underwent coronary angiography with FFR assessment. Vessel-specific CMVR was computed using a three-dimensional computational fluid dynamics simulation with invasively measured proximal and distal pressures applied as boundary conditions. Predictive models were created using non-linear autoregressive moving average with exogenous input (NARMAX) modelling with computed CMVR as the dependent variable. Angio-FFR (VIRTUheart™) was computed using previously described methods. Three simulations were run: using a generic CMVR value (Model A); using ML-predicted CMVR based upon simple clinical data (Model B); and using ML-predicted CMVR also incorporating echocardiographic data (Model C). The diagnostic (FFR ≤ or >0.80) and absolute accuracies of these models were compared. Eighty-four patients underwent coronary angiography with FFR assessment in 157 vessels. The mean measured FFR was 0.79 (±0.15). The diagnostic and absolute accuracies of each personalized model were: (A) 73% and ±0.10; (B) 81% and ±0.07; and (C) 89% and ±0.05, P < 0.001.

Conclusion

The accuracy of angio-FFR was dependent in part upon CMVR estimation. Personalization of CMVR from standard clinical data resulted in a significant reduction in angio-FFR error.

Diagnostic comparison of automatic and manual TIMI frame-counting-generated quantitative flow ratio (QFR) values

Quantitative flow ratio (QFR) is a computational measurement of FFR (fractional flow reserve), calculated from coronary angiography. Latest QFR software automates TIMI frame counting (TFC), which occurs during the flow step of QFR analyses, making the analysis faster and more reproducible. The objective is to determine the diagnostic performance of QFR values obtained from analyses using automatic TFC compared to those obtained from analyses using manual TFC. This was a single-arm clinical trial that used the prospective analysis of the coronary angiographic image series of 97 patients who underwent evaluation of stable coronary artery disease with FFR/iFR at MedStar Washington Hospital Center in Washington, DC, USA. Automatic and manual TFC QFR values were obtained from the analyses of each of the 97 patients' image series, with manual TFC QFR values as the current gold standard for comparison. The diagnostic performance of automatic TFC QFR values was measured as follows: sensitivity was 0.87 (95% CI 0.66-0.97) and specificity was 1.00 (95% CI 0.9514-1.00), positive predictive value (PPV) was 1.00 (95%CI 1.00-1.00), while the NPV was 0.96 (95% CI 0.96-0.99). Overall accuracy was 96.91% (95% CI 91.23%-99.36%). The agreement as illustrated by the Bland-Altman plot shows a bias of 0.0023 (SD 0.0208) and narrow limits of agreement (LOA): Upper LOA 0.0573 and Lower LOA - 0.0528. The area under curve (AUC) was 0.996. QFR values generated from automatic TFC are comparable to those generated from manual TFC in diagnostic capability. The most recent software update produces values equivalent to those of the previous manual option, and can therefore be used interchangeably.

Towards a Deep-Learning Approach for Prediction of Fractional Flow Reserve from Optical Coherence Tomography

Cardiovascular disease (CVD) is the number one cause of death worldwide, and coronary artery disease (CAD) is the most prevalent CVD, accounting for 42% of these deaths. In view of the limitations of the anatomical evaluation of CAD, Fractional Flow Reserve (FFR) has been introduced as a functional diagnostic index. Herein, we evaluate the feasibility of using deep neural networks (DNN) in an ensemble approach to predict the invasively measured FFR from raw anatomical information that is extracted from optical coherence tomography (OCT). We evaluate the performance of various DNN architectures under different formulations: regression, classification—standard, and few-shot learning (FSL) on a dataset containing 102 intermediate lesions from 80 patients. The FSL approach that is based on a convolutional neural network leads to slightly better results compared to the standard classification: the per-lesion accuracy, sensitivity, and specificity were 77.5%, 72.9%, and 81.5%, respectively. However, since the 95% confidence intervals overlap, the differences are statistically not significant. The main findings of this study can be summarized as follows: (1) Deep-learning (DL)-based FFR prediction from reduced-order raw anatomical data is feasible in intermediate coronary artery lesions; (2) DL-based FFR prediction provides superior diagnostic performance compared to baseline approaches that are based on minimal lumen diameter and percentage diameter stenosis; and (3) the FFR prediction performance increases quasi-linearly with the dataset size, indicating that a larger train dataset will likely lead to superior diagnostic performance.

Improved Functional Assessment of Ischemic Severity Using 3D Printed Models

Objective

To develop a novel in vitro method for evaluating coronary artery ischemia using a combination of non-invasive coronary CT angiograms (CCTA) and 3D printing (FFR_3D).

Methods

Twenty eight patients with varying degrees of coronary artery disease who underwent non-invasive CCTA scans and invasive fractional flow reserve (FFR) of their epicardial coronary arteries were included in this study. Coronary arteries were segmented and reconstructed from CCTA scans using Mimics (Materialize). The segmented models were then 3D printed using a Carbon M1 3D printer with urethane methacrylate (UMA) family of rigid resins. Physiological coronary circulation was modeled in vitro as flow-dependent stenosis resistance in series with variable downstream resistance. A range of physiological flow rates (Q) were applied using a peristaltic steady flow pump and titrated with a flow sensor. The pressure drop (ΔP) and the pressure ratio (P_d/P_a) were assessed for patient-specific aortic pressure (P_a) and differing flow rates (Q) to evaluate FFR_3D using the 3D printed model.

Results

There was a good positive correlation (r = 0.87, p < 0.0001) between FFR_3D and invasive FFR. Bland-Altman analysis revealed a good concordance between the FFR_3D and invasive FFR values with a mean bias of 0.02 (limits of agreement: −0.14 to 0.18; p = 0.2).

Conclusions

3D printed patient-specific models can be used in a non-invasive in vitro environment to quantify coronary artery ischemia with good correlation and concordance to that of invasive FFR.

Angiographic Lesion Morphology Provides Incremental Value to Generalize Quantitative Flow Ratio for Predicting Myocardial Ischemia

Aim

The quantitative flow ratio (QFR) is favorable for functional assessment of coronary artery stenosis without pressure wires and induction of hyperemia. The aim of this study was to explore whether angiographic lesion morphology provides incremental value to generalize QFR for predicting myocardial ischemia in unselected patients.

Methods

This study was a substudy to the CT-FFR CHINA trial, referring 345 participants from five centers with suspected coronary artery disease on coronary CT angiography for diagnostic invasive coronary angiography (ICA). Fractional flow reserve (FFR) was measured in all vessels with 30–90% diameter stenosis. QFR was calculated in 186 lesions from 159 participants in a blinded manner. In addition, parameters to characterize lesion features were recorded or measured, including left anterior descending arteries (LADs)-involved lesions, side branch located at stenotic lesion (BL), multiple lesions (ML), minimal lumen diameter (MLD), reference lumen diameter (RLD), percent diameter stenosis (%DS), lesion length (LL), and LL/MLD4. Logistic regression was used to construct two kinds of models by combining single or two lesion parameters with the QFR. The performances of these models were compared with that of QFR on a per-vessel level.

Results

A total of 148 participants (mean age: 59.5 years; 101 men) with 175 coronary arteries were included for final analysis. In total, 81 (46%) vessels were considered hemodynamically significant. QFR correctly classified 82.29% of the vessels using FFR with a cutoff of 0.80 as reference standard. The area under the receiver operating characteristic curve (AUC) of QFR was 0.86 with a sensitivity, specificity, positive predictive value, and negative predictive value of 80.25, 84.04, 81.25, and 83.16%, respectively. The combined models (QFR + LAD + MLD, QFR + LAD + %DS, QFR + BL + MLD, and QFR + BL + %DS) outperformed QFR with higher AUCs (0.91 vs. 0.86, P = 0.02; 0.91 vs. 0.86, P = 0.02; 0.91 vs. 0.86, P = 0.02; 0.90 vs. 0.86, P = 0.03, respectively). Compared with QFR, the sensitivity of the combined models (QFR + BL and QFR + MLD) was improved (91.36 vs. 80.25%, 91.36 vs. 80.25%, respectively, both P &lt; 0.05) without compromised specificity or accuracy.

Conclusion

Combined with angiographic lesion parameters, QFR can be optimized for predicting myocardial ischemia in unselected patients.

Quantitative flow ratio for evaluation of borderline coronary lesions in patients with severe aortic stenosis

INTRODUCTION AND OBJECTIVES

Quantitative flow ratio (QFR) is a novel noninvasive method for evaluating coronary physiology. However, data on the QFR in patients with aortic stenosis (AS) and coronary artery disease are scarce. Thus, we compared the diagnostic performance of the QFR with that of the resting distal to aortic coronary pressure (Pd/Pa) ratio, fractional flow reserve (FFR), and instantaneous wave-free ratio (iFR), as well as angiographic indices.

METHODS

A total of 221 AS patients with 416 vessels undergoing FFR/iFR measurements were enrolled in the study.

RESULTS

The mean percent diameter stenosis (%DS) was 58.6%±13.4% and the mean Pd/Pa ratio, FFR, iFR, and QFR were 0.95±0.03, 0.85±0.07, 0.90±0.04, and 0.84±0.07, respectively. A FFR ≤ 0.80 was noted in 26.0% of interrogated vessels, as well as an iFR ≤ 0.89 in 33.2% and QFR ≤ 0.80 in 31.7%. The QFR had better agreement with FFR (intraclass correlation coefficient [ICC], 0.96; 95% confidence interval [95%CI], 0.95-0.96) than with the iFR (ICC, 0.79; 95%CI, 0.75-0.82) and Pd/Pa ratio (ICC, 0.52; 95%CI, 0.44-0.58). In addition, the QFR showed better diagnostic accuracy (98.6% vs 94.2%; P <.001) and discriminant function (area under the curve=0.996 vs 0.988; P <.001) when the iFR was used as the reference instead of FFR.

CONCLUSIONS

In patients with AS, the QFR has good agreement with both FFR and iFR. However, the agreement appears to be even better when the iFR is used as the reference, presumably due to the complex nature of the coronary physiology in the assessment of coronary artery disease in patients with severe AS.

A novel CFD-based computed index of microcirculatory resistance (IMR) derived from coronary angiography to assess coronary microcirculation

OBJECTIVES

This study sought to present a novel approach for computation of the index of microcirculatory resistance (IMR) and to evaluate its diagnostic performance.

BACKGROUND

IMR is a quantitative assessment to identify coronary microvascular dysfunction. However, its clinical use remains extremely limited. Calculation of IMR from coronary angiography images may increase the utility of coronary microvasculature assessment.

METHODS

203 patients with 203 vessels were included in this study. Physiology measurements were obtained with pressure-wire in the whole cohort. The computational fluid dynamics (CFD)-based AccuIMR was computed and evaluated in a blinded fashion using wire-based IMR as the reference standard.

RESULTS

The overall diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of AccuIMR for detecting coronary microvascular disease were 91.1% (95% CI: 86.4% to 94.7%), 89.4% (95% CI: 80.9% to 95.0%), 92.4% (95% CI: 86.0% to 96.5%), 89.4% (95% CI: 81.8% to 94.1%), and 92.2% (95% CI: 86.7% to 95.8%), respectively. The correlation coefficient equaled to 0.81 (p < 0.001) between AccuIMR and wire-based IMR with the receiver-operating curve had area under the curve of 0.924 (95% CI: 0.878 to 0.956).

CONCLUSIONS

AccuIMR is a novel pressure-wire free approach to assess coronary microvascular disease with great diagnostic performance, which can be a valid, efficient, and cost-reducing tool to provide an easier routine assessment of coronary microcirculation.