Comparison of Fractional Flow Reserve Based on Computational Fluid Dynamics Modeling Using Coronary Angiographic Vessel Morphology Versus Invasively Measured Fractional Flow Reserve

Co-registered optical coherence tomography and X-ray angiography for the prediction of fractional flow reserve

Cardiovascular disease (CVD) stands as the leading global cause of mortality, and coronary artery disease (CAD) has the highest prevalence, contributing to 42% of these fatalities. Recognizing the constraints inherent in the anatomical assessment of CAD, Fractional Flow Reserve (FFR) has emerged as a pivotal functional diagnostic metric. Herein, we assess the potential of employing an ensemble approach with deep neural networks (DNN) to predict invasively measured Fractional Flow Reserve (FFR) using raw anatomical data extracted from both optical coherence tomography (OCT) and X-ray coronary angiography (XA). In this study, we used a challenging dataset, with 46% of the lesions falling within the FFR range of 0.75 to 0.85. Despite this complexity, our model achieved an accuracy of 84.3%, demonstrating a sensitivity of 87.5% and a specificity of 81.4%. Our results demonstrate that incorporating both OCT and XA signals, co-registered, as inputs for the DNN model leads to an important increase in overall accuracy.

Diagnostic performances of Nonhyperemic Pressure Ratios and Coronary Angiography-Based Fractional Flow Reserve against conventional Wire-Based Fractional Flow Reserve

BACKGROUND

Nonhyperemic pressure ratios (NHPRs) have been proposed as alternatives to fractional flow reserve (FFR) without induction of hyperemia. More recently, imaging based-FFR estimation, especially coronary angiography-derived FFR (Angio-FFR) measurement, is proposed to estimate wire-based FFR. However, little is known about the diagnostic performance of these indices against conventional FFR.

AIMS

We aimed to assess and compare the diagnostic performance of both NHPRs and coronary Angio-FFR against wire-based conventional FFR.

METHODS

PubMed and Embase databases were systematically searched for peer-reviewed original articles up to 08/2022. The primary outcomes were the pooled sensitivity and specificity as well as the area under the curve (AUC) of the summary receiver-operating characteristic curve of those indices.

RESULTS

A total of 6693 records were identified after a literature search, including 37 reports for NHPRs and 34 for Angio-FFR. Overall, NHPRs have a lower diagnostic performance in estimating wire-based FFR with an AUC of 0.85 (0.81, 0.88) when compared with Angio-FFR of 0.95 (0.93, 0.97). When all four modalities of NHPRs (iFR, Pd/Pa, DPR, RFR) were compared, those had overlapping AUCs without major differences among each other. Similarly, when the two most commonly used Angio-FFR (QFR, FFR angio ) were compared, those had overlapping AUCs without major differences among each other.

CONCLUSION

Angio-FFR may offer a better estimation of wire-based FFR than NHPRs. Our results support a wider use of Angio-FFR in the cardiac catheterization laboratory to streamline our workflow for coronary physiologic assessment.

CLASSIFICATIONS

FFR,, stable ischemic disease and non-ST elevation acute coronary syndrome.

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.

Comparison of 2D-QCA, 3D-QCA and coronary angiography derived FFR in predicting myocardial ischemia assessed by CZT-SPECT MPI

BACKGROUND

Angiography derived fractional flow reserve (angio-FFR) has been proposed. This study aimed to assess its diagnostic performance with cadmium-zinc-telluride single emission computed tomography (CZT-SPECT) as reference.

METHODS AND RESULTS

Patients underwent CZT-SPECT within 3 months of coronary angiography were included. Angio-FFR computation was performed using computational fluid dynamics. Percent diameter (%DS) and area stenosis (%AS) were measured by quantitative coronary angiography. Myocardial ischemia was defined as a summed difference score ≥ 2 in a vascular territory. Angio-FFR ≤ 0.80 was considered abnormal. 282 coronary arteries in 131 patients were analyzed. Overall accuracy of angio-FFR to detect ischemia on CZT-SPECT was 90.43%, with a sensitivity of 62.50% and a specificity of 98.62%. The diagnostic performance (= area under ROC = AUC) of angio-FFR [AUC = 0.91, 95% confidence intervals (CI) 0.86-0.95] was similar as those of %DS (AUC = 0.88, 95% CI 0.84-0.93, p = 0.326) and %AS (AUC = 0.88, 95% CI 0.84-0.93 p = 0.241) by 3D-QCA, but significantly higher than those of %DS (AUC = 0.59, 95% CI 0.51-0.67, p < 0.001) and %AS (AUC = 0.59, 95% CI 0.51-0.67, p < 0.001) by 2D-QCA. However, in vessels with 50-70% stenoses, AUC of angio-FFR was significantly higher than those of %DS (0.80 vs. 0.47, p < 0.001) and %AS (0.80 vs. 0.46, p < 0.001) by 3D-QCA and %DS (0.80 vs. 0.66, p = 0.036) and %AS (0.80 vs. 0.66, p = 0.034) by 2D-QCA.

CONCLUSION

Angio-FFR had a high accuracy in predicting myocardial ischemia assessed by CZT-SPECT, which is similar as 3D-QCA but significantly higher than 2D-QCA. While in intermediate lesions, angio-FFR is better than 3D-QCA and 2D-QCA in assessing myocardial ischemia.

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.

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.

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.

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.

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.

Angiography-Based Fractional Flow Reserve: State of the Art

Purpose of Review

Three-dimensional quantitative coronary angiography-based methods of fractional flow reserve (FFR) derivation have emerged as an appealing alternative to conventional pressure-wire-based physiological lesion assessment and have the potential to further extend the use of physiology in general. Here, we summarize the current evidence related to angiography-based FFR and perspectives on future developments.

Recent Findings

Growing evidence suggests good diagnostic performance of angiography-based FFR measurements, both in chronic and acute coronary syndromes, as well as in specific lesion subsets, such as long and calcified lesions, left main coronary stenosis, and bifurcations. More recently, promising results on the superiority of angiography-based FFR as compared to angiography-guided PCI have been published.

Summary

Currently available angiography -FFR indices proved to be an excellent alternative to invasive pressure wire-based FFR. Dedicated prospective outcome data comparing these indices to routine guideline recommended PCI including the use of FFR are eagerly awaited.

Prognostic Value of Coronary Angiography-Derived Fractional Flow Reserve Immediately After Stenting

Objectives

The aim of this study was to investigate the potential prognostic value of post-percutaneous coronary intervention (PCI) angiography-derived fractional flow reserve (FFR) and its gradient across the stent.

Background

Post-PCI FFR and its gradient across the stent have been proved to be associated with clinical outcomes. However, little is known about the prognostic value of post-PCI coronary angiography-derived FFR and its gradient across the stent.

Methods

Patients diagnosed with coronary heart disease and participated in drug-eluting stent (DES) clinical trials for stent implantation in a single center were included for this retrospective analysis. A novel coronary angiography-derived FFR (caFFR) and its gradient across the stent were calculated offline using two projections from coronary angiography performed after PCI. Clinical follow-up was completed at 9 months after the index procedure and the primary outcome was target vessel failure (TVF), defined as a composite of target vessel-related myocardial infarction (MI), target vessel-related revascularization (TVR), and cardiac death. Coronary angiography was also performed at the 9 months follow-up time to get data of late lumen loss (LLL) and percent diameter stenosis (%DS).

Results

A total of 159 vessels in 136 patients were analyzed. The mean value of post-PCI caFFR was 0.90 ± 0.06. The median value of trans-stent caFFR gradient (ΔcaFFR_stent) was 0.04 (interquartile range 0.02-0.08). ΔcaFFR_stent>0 was demonstrated in 147 vessels (92.45%). The TVF rate was significantly higher in patients with post-PCI caFFR < 0.90 (4 [8.16%] vs. 1 [1.15%], P = 0.037), which was mainly achieved by the difference between the TVR rate. In the subgroup with lesions located in the left anterior descending coronary artery (LAD), post-PCI caFFR was an independent predictor of LLL (β = -1.07, 95% CI: -1.74 to -0.39, P = 0.002) and %DS at follow-up (β = -30.24, 95% CI: -56.44 to -4.04, P = 0.025), ΔcaFFR_stent was an independent predictor of LLL (β=0.98, 95% CI:0.13-1.83, P = 0.026).

Conclusion

Suboptimal post-PCI caFFR and trans-stent caFFR gradient were common among vessels immediately after stenting. Lower post-PCI caFFR was associated with a higher rate of 9-month TVF. After LAD PCI, both post-PCI caFFR and its gradient across stent were independent predictors of the neointimal proliferation of the target vessel evaluated by LLL and %DS at follow-up.

Diagnostic performance of AccuFFRangio in the functional assessment of coronary stenosis compared with pressure wire-derived fractional flow reserve

BACKGROUND

Non-invasive fractional flow reserve (FFR) has been increasingly used in the clinical workflow to assist clinical decision-making for percutaneous coronary intervention (PCI). This clinical study evaluates the diagnostic accuracy of coronary stenosis assessed by a non-invasive FFR analysis method (termed AccuFFRangio) based on invasive coronary angiography (ICA). It is a blinded, self-controlled, retrospective, and dual-center clinical investigation study.

METHODS

Coronary angiography data and the related information of 320 patients with 320 vessels were collected, and AccuFFRangio was used to assess the FFR for these patients. Compared with the wire-measured FFR values, we evaluated AccuFFRangio performance by its accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

The diagnostic accuracy, sensitivity, specificity, PPV, and NPV for AccuFFRangio in identifying hemodynamically significant coronary stenosis were 93.3%, 92.6%, 93.5%, 84.3%, and 97.1%, respectively. The direct correlation between computed AccuFFRangio and measured FFR was 0.812 (P<0.001), and the area under the receiver operating characteristic curve (AUC) value of AccuFFRangio was 0.96.

CONCLUSIONS

This clinical study demonstrates the efficiency and accuracy of AccuFFRangio for clinical implementation when using invasive wire-measured FFR as a reference. Further validation is required in a large prospective multicenter study.

Fractional Flow Reserve: Patient Selection and Perspectives

The aim of this review was to discuss the current practice and patient selection for invasive FFR, new techniques to estimate invasive FFR and future of coronary physiology tests. We elaborate on the indication and application of FFR and on the contraindications and concerns in certain patient populations.

Image-based computational fluid dynamics for estimating pressure drop and fractional flow reserve across iliac artery stenosis: A comparison with in-vivo measurements

Computational Fluid Dynamics (CFD) and time-resolved phase-contrast magnetic resonance imaging (PC-MRI) are potential non-invasive methods for the assessment of the severity of arterial stenoses. Fractional flow reserve (FFR) is the current "gold standard" for determining stenosis severity in the coronary arteries but is an invasive method requiring insertion of a pressure wire. CFD derived FFR (vFFR) is an alternative to traditional catheter derived FFR now available commercially for coronary artery assessment, however, it can potentially be applied to a wider range of vulnerable vessels such as the iliac arteries. In this study CFD simulations are used to assess the ability of vFFR in predicting the stenosis severity in a patient with a stenosis of 77% area reduction (>50% diameter reduction) in the right iliac artery. Variations of vFFR, overall pressure drop and flow split between the vessels were observed by using different boundary conditions. Correlations between boundary condition parameters and resulting flow variables are presented. The study concludes that vFFR has good potential to characterise iliac artery stenotic disease.

Agreement between single plane and biplane derived angiographic fractional flow reserve in patients with intermediate coronary artery stenosis

Fractional flow reserve (FFR) is often used to evaluate the physiological severity of intermediate coronary stenoses, but less-invasive assessment methods are desirable. We evaluated the feasibility of angiographic FFR (angioFFR) calculated from two projections acquired simultaneously by a biplane C-arm system and angioFFR calculated from two projections acquired independently by one plane of the same biplane C-arm system. AngioFFR was validated against FFR in terms of detection of hemodynamically relevant coronary artery stenoses. Twenty-two Patients who underwent angiography and FFR for coronary artery disease were included. We used a non-commercial prototype to calculate biplane angioFFR for 22 vessels (19 LAD, 1 LCx, 2 RCA) and single plane angioFFR for 17 of the same 22 vessels. FFR < 0.8 was measured in 8 vessels. The Pearson correlation coefficients with FFR were 0.55 for single plane angioFFR and 0.61 for biplane angioFFR and the diagnostic accuracies were 88% (95% CI 73-100%) for single plane angioFFR and 86% (95% CI 72-100%) for biplane angioFFR. Bland-Altman plots revealed that compared with FFR, the limits of agreement for single plane angioFFR were  - 0.07 to 0.19 (mean difference 0.06, p = 0.002) and the limits of agreement for biplane FFR were  - 0.09 to 0.15 (mean difference 0.03, p = 0.03). In conclusion, angioFFR calculated from single or biplane acquisitions by a biplane C-arm is feasible and may evolve to a tool for less invasive imaging-based assessment of myocardial ischemia.

Operator-dependent variability of angiography-derived fractional flow reserve and the implications for treatment

AIMS

To extend the benefits of physiologically guided percutaneous coronary intervention to many more patients, angiography-derived, or 'virtual' fractional flow reserve (vFFR) has been developed, in which FFR is computed, based upon the images, instead of being measured invasively. The effect of operator experience with these methods upon vFFR accuracy remains unknown. We investigated variability in vFFR results based upon operator experience with image-based computational modelling techniques.

METHODS AND RESULTS

Virtual fractional flow reserve was computed using a proprietary method (VIRTUheart) from the invasive angiograms of patients with coronary artery disease. Each case was processed by an expert (>100 vFFR cases) and a non-expert (<20 vFFR cases) operator and results were compared. The primary outcome was the variability in vFFR between experts and non-experts and the impact this had upon treatment strategy (PCI vs. conservative management). Two hundred and thirty-one vessels (199 patients) were processed. Mean non-expert and expert vFFRs were similar overall [0.76 (0.13) and 0.77 (0.16)] but there was significant variability between individual results (variability coefficient 12%, intraclass correlation coefficient 0.58), with only moderate agreement (κ = 0.46), and this led to a statistically significant change in management strategy in 27% of cases. Variability was significantly lower, and agreement higher, for expert operators; a change in their recommended management occurred in 10% of repeated expert measurements and 14% of inter-expert measurements.

CONCLUSION

Virtual fractional flow reserve results are influenced by operator experience of vFFR processing. This had implications for treatment allocation. These results highlight the importance of training and quality assurance to ensure reliable, repeatable vFFR results.

The new role of diagnostic angiography in coronary physiological assessment

The role of 'stand-alone' coronary angiography (CAG) in the management of patients with chronic coronary syndromes is the subject of debate, with arguments for its replacement with CT angiography on the one hand and its confinement to the interventional cardiac catheter laboratory on the other. Nevertheless, it remains the standard of care in most centres. Recently, computational methods have been developed in which the laws of fluid dynamics can be applied to angiographic images to yield 'virtual' (computed) measures of blood flow, such as fractional flow reserve. Together with the CAG itself, this technology can provide an 'all-in-one' anatomical and functional investigation, which is particularly useful in the case of borderline lesions. It can add to the diagnostic value of CAG by providing increased precision and reduce the need for further non-invasive and functional tests of ischaemia, at minimal cost. In this paper, we place this technology in context, with emphasis on its potential to become established in the diagnostic workup of patients with suspected coronary artery disease, particularly in the non-interventional setting. We discuss the derivation and reliability of angiographically derived fractional flow reserve (CAG-FFR) as well as its limitations and how CAG-FFR could be integrated within existing national guidance. The assessment of coronary physiology may no longer be the preserve of the interventional cardiologist.

Non-invasive characterization of complex coronary lesions

Conventional invasive diagnostic imaging techniques do not adequately resolve complex Type B and C coronary lesions, which present unique challenges, require personalized treatment and result in worsened patient outcomes. These lesions are often excluded from large-scale non-invasive clinical trials and there does not exist a validated approach to characterize hemodynamic quantities and guide percutaneous intervention for such lesions. This work identifies key biomarkers that differentiate complex Type B and C lesions from simple Type A lesions by introducing and validating a coronary angiography-based computational fluid dynamic (CFD-CA) framework for intracoronary assessment in complex lesions at ultrahigh resolution. Among 14 patients selected in this study, 7 patients with Type B and C lesions were included in the complex lesion group including ostial, bifurcation, serial lesions and lesion where flow was supplied by collateral bed. Simple lesion group included 7 patients with lesions that were discrete, [Formula: see text] long and readily accessible. Intracoronary assessment was performed using CFD-CA framework and validated by comparing to clinically measured pressure-based index, such as FFR. Local pressure, endothelial shear stress (ESS) and velocity profiles were derived for all patients. We validates the accuracy of our CFD-CA framework and report excellent agreement with invasive measurements ([Formula: see text]). Ultra-high resolution achieved by the model enable physiological assessment in complex lesions and quantify hemodynamic metrics in all vessels up to 1mm in diameter. Importantly, we demonstrate that in contrast to traditional pressure-based metrics, there is a significant difference in the intracoronary hemodynamic forces, such as ESS, in complex lesions compared to simple lesions at both resting and hyperemic physiological states [n = 14, [Formula: see text]]. Higher ESS was observed in the complex lesion group ([Formula: see text] Pa) than in simple lesion group ([Formula: see text] Pa). Complex coronary lesions have higher ESS compared to simple lesions, such differential hemodynamic evaluation can provide much the needed insight into the increase in adverse outcomes for such patients and has incremental prognostic value over traditional pressure-based indices, such as FFR.

A simple coronary blood flow model to study the collateral flow index

In this work, we present a novel modeling framework to investigate the effects of collateral circulation into the coronary blood flow physiology. A prototypical model of the coronary tree, integrated with the concept of Collateral Flow Index (CFI), is employed to gain insight about the role of model parameters associated with the collateral circuitry, which results in physically-realizable solutions for specific CFI data. Then, we discuss the mathematical feasibility of pressure-derived CFI, anatomical implications and practical considerations involving the estimation of model parameters in collateral connections. A sensitivity analysis is carried out, and the investigation of the impact of the collateral circulation on FFR values is also addressed.

Diagnostic accuracy of quantitative flow ratio (QFR) and vessel fractional flow reserve (vFFR) estimated retrospectively by conventional radiation saving X-ray angiography

Background

Angiography derived FFR reveals good performance in assessing intermediate coronary stenosis. However, its performance under contemporary low X-ray frame and pulse rate settings is unknown. We aim to validate the feasibility and performance of quantitative flow ratio (QFR) and vessel fractional flow reserve (vFFR) under such angiograms.

Methods

This was an observational, retrospective, single center cohort study. 134 vessels in 102 patients, with angiograms acquired under 7.5fps and 7pps mode, were enrolled. QFR (fQFR and cQFR) and vFFR were validated with FFR as the gold standard. A conventional manual and a newly developed algorithmic exclusion method (M and A group) were both evaluated for identification of poor-quality angiograms.

Results

Good agreement between QFR/vFFR and FFR were observed in both M and A group, except for vFFR in the M group. The correlation coefficients between fQFR/cQFR/vFFR and FFR were 0.6242, 0.5888, 0.4089 in the M group, with r_vFFR significantly lower than r_fQFR (p = 0.0303), and 0.7055, 0.6793, 0.5664 in the A group, respectively. AUCs of detecting lesions with FFR ≤ 0.80 were 0.852 (95% CI 0.722–0.913), 0.858 (95% CI 0.778–0.917), 0.682 (95% CI 0.586–0.768), for fQFR/cQFR/vFFR in the M group, while vFFR performed poorer than fQFR (p = 0.0063) and cQFR (p = 0.0054). AUCs were 0.898 (95% CI 0.811–0.945), 0.892 (95% CI 0.803–0.949), 0.843 (95% CI 0.746–0.914) for fQFR/cQFR/vFFR in the A group. AUC_vFFR was significantly higher in the A group than that in the M group (p = 0.0399).

Conclusions

QFR/vFFR assessment is feasible under 7.5fps and 7pps angiography, where cQFR showed no advantage compared to fQFR. Our newly developed algorithmic exclusion method could be a better method of selecting angiograms with adequate quality for angiography derived FFR assessment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10554-020-02133-8.

Virtual (Computed) Fractional Flow Reserve: Future Role in Acute Coronary Syndromes

The current management of acute coronary syndromes (ACS) is with an invasive strategy to guide treatment. However, identifying the lesions which are physiologically significant can be challenging. Non-invasive imaging is generally not appropriate or timely in the acute setting, so the decision is generally based upon visual assessment of the angiogram, supplemented in a small minority by invasive pressure wire studies using fractional flow reserve (FFR) or related indices. Whilst pressure wire usage is slowly increasing, it is not feasible in many vessels, patients and situations. Limited evidence for the use of FFR in non-ST elevation (NSTE) ACS suggests a 25% change in management, compared with traditional assessment, with a shift from more to less extensive revascularisation. Virtual (computed) FFR (vFFR), which uses a 3D model of the coronary arteries constructed from the invasive angiogram, and application of the physical laws of fluid flow, has the potential to be used more widely in this situation. It is less invasive, fast and can be integrated into catheter laboratory software. For severe lesions, or mild disease, it is probably not required, but it could improve the management of moderate disease in 'real time' for patients with non-ST elevation acute coronary syndromes (NSTE-ACS), and in bystander disease in ST elevation myocardial infarction. Its practicability and impact in the acute setting need to be tested, but the underpinning science and potential benefits for rapid and streamlined decision-making are enticing.

Fractional-order modeling and control of pneumatic-hydraulic upper limb rehabilitation training system1

In this paper, by replacing the integral mass flow equation to fractional-order mass flow equation, the fractional-order mathematical model of 2DOF pneumatic-hydraulic upper limb rehabilitation training system is established. A new 2DOF fractional-order fuzzy PID (FOFPID) controller is designed, to provides a new reference for improving the control accuracy of the pneumatic system. In the design of the controller, the weight parameters of the input terms are transformed into the weight parameters of the error, and the input, which are analyzed to improve the accuracy of the controller design. The parameters of the control system are determined by multi-objective particle swarm optimization. To prove the effectiveness of the proposed control method, the experimental research was carried out by building the experimental platform of pneumatic-hydraulic upper limb rehabilitation training system. The results show that the 2DOF FOFPID controller has better performance than other designed controllers under different working conditions.

Diagnostic accuracy of two-dimensional coronary angiographic-derived fractional flow reserve-Preliminary results

AIM

Noninvasive fractional flow reserve (NiFFR) is an emerging method for evaluating the functional significance of a coronary lesion during diagnostic coronary angiography (CAG). The method relies on the computational flow dynamics and the three-dimensional (3D) reconstruction of the vessel extracted from CAG. In the present study, we sought to evaluate the diagnostic performance and applicability of 2D-based NiFFR.

METHODS

In this prospective observational study, we evaluated 2D-based NiFFR in 279 candidates for invasive CAG and invasive fractional flow reserve (FFR). NiFFR was calculated via two methods: variable NiFFR, in which the contrast transport time was extracted from the angiographic view, and fixed NiFFR, in which a prespecified frame count was applied.

RESULTS

The final analysis was performed on 245 patients (250 lesions). Variable NiFFR had an area under the receiver operating characteristic curve of 81.5%, an accuracy of 80.0%, a sensitivity of 82.2%, a specificity of 82.2%, a negative predictive value of 91.4%, and a positive predictive value of 63.6%. The mean difference between FFR and NiFFR was -0.0244 ±.0616 (p ≤.0001). A pressure wire-free hybrid strategy was possible in 68.8% of our population with variable NiFFR.

CONCLUSIONS

Our 2D-based NiFFR yielded results comparable to those derived from 3D-based software. Our findings should; however, be confirmed in larger trials.

Blood residence time to assess significance of coronary artery stenosis

Coronary artery stenosis is a narrowing of coronary lumen space caused by an atherosclerotic lesion. Fractional flow reserve (FFR) is the gold standard metric to assess physiological significance of coronary stenosis, but requires an invasive procedure. Computational modeling in conjunction with patient-specific imaging demonstrates formation of regions of recirculatory flow distal to a stenosis, increasing mean blood residence time relative to uninhibited flow. A new computational parameter, mean blood residence time (BloodRT), was computed for 100 coronary artery segments for which FFR was known. A threshold for BloodRT was determined to assess the physiological significance of a stenosis, analogous to diagnostic threshold for FFR. Model sensitivity and specificity of BloodRT for diagnosis of hemodynamically significant coronary stenosis was 98% and 96% respectively, compared with FFR. When applied to clinical practice, this could potentially allow practicing cardiologists to accurately assess the severity of coronary stenosis without resorting to invasive techniques.

Deep neural networks for ECG-free cardiac phase and end-diastolic frame detection on coronary angiographies

Invasive coronary angiography (ICA) is the gold standard in Coronary Artery Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detection on each temporal frame of a coronary angiography acquisition is of significant importance for the anatomical and non-invasive functional assessment of CAD. This task is generally performed via manual frame selection or semi-automated selection based on simultaneously acquired ECG signals - thus introducing the requirement of simultaneous ECG recordings. In this paper, we evaluate the performance of a purely image based workflow relying on deep neural networks for fully automated cardiac phase and EDF detection on coronary angiographies. A first deep neural network (DNN), trained to detect coronary arteries, is employed to preselect a subset of frames in which coronary arteries are well visible. A second DNN predicts cardiac phase labels for each frame. Only in the training and evaluation phases for the second DNN, ECG signals are used to provide ground truth labels for each angiographic frame. The networks were trained on 56,655 coronary angiographies from 6820 patients and evaluated on 20,780 coronary angiographies from 6261 patients. No exclusion criteria related to patient state (stable or acute CAD), previous interventions (PCI or CABG), or pathology were formulated. Cardiac phase detection had an accuracy of 98.8 %, a sensitivity of 99.3 % and a specificity of 97.6 % on the evaluation set. EDF prediction had a precision of 98.4 % and a recall of 97.9 %. Several sub-group analyses were performed, indicating that the cardiac phase detection performance is largely independent from acquisition angles, the heart rate of the patient, and the angiographic view (LCA / RCA). The average execution time of cardiac phase detection for one angiographic series was on average less than five seconds on a standard workstation. We conclude that the proposed image based workflow potentially obviates the need for manual frame selection and ECG acquisition, representing a relevant step towards automated CAD assessment.

Coronary Physiology Derived from Invasive Angiography: Will it be a Game Changer?

There is a large body of evidence suggesting that having knowledge of the presence and extent of coronary atheroma and whether it is causing downstream myocardial ischaemia facilitates optimal diagnosis and management for patients presenting with chest pain. Despite this, the use of coronary pressure wire in routine practice is surprisingly low and routine assessment of all diseased vessels before making a bespoke management plan is rare. The advent of angiogram-derived models of physiology could change diagnostic practice completely. By offering routine assessment of the physiology of all the major epicardial coronary vessels, angiogram-derived physiology has the potential to radically modify current practice by facilitating more accurate patient-level, vessel-level, and even lesion-level decision-making. In this article, the authors review the current state of angiogram-derived physiology and speculate on its potential impact on clinical practice.

Applying Deep Neural Networks over Homomorphic Encrypted Medical Data

In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regulations on the confidentiality of patient health information have in many cases hindered the adoption of deep learning-based solutions in clinical workflows. To allow for the processing of sensitive health information without disclosing the underlying data, we propose a solution based on fully homomorphic encryption (FHE). The considered encryption scheme, MORE (Matrix Operation for Randomization or Encryption), enables the computations within a neural network model to be directly performed on floating point data with a relatively small computational overhead. We consider the well-known MNIST digit recognition problem to evaluate the feasibility of the proposed method and show that performance does not decrease when deep learning is applied on MORE homomorphic data. To further evaluate the suitability of the method for healthcare applications, we first train a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) hemodynamic model and then provide a solution for classifying encrypted X-ray coronary angiography medical images. The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models. Lastly, we discuss the security implications of the encryption scheme and show that while the considered cryptosystem promotes efficiency and utility at a lower security level, it is still applicable in certain practical use cases.

Accuracy of computational pressure-fluid dynamics applied to coronary angiography to derive fractional flow reserve: FLASH FFR

Aims

Conventional fractional flow reserve (FFR) is measured invasively using a coronary guidewire equipped with a pressure sensor. A non-invasive derived FFR would eliminate risk of coronary injury, minimize technical limitations, and potentially increase adoption. We aimed to evaluate the diagnostic performance of a computational pressure-flow dynamics derived FFR (caFFR), applied to coronary angiography, compared to invasive FFR.

Methods and results

The FLASH FFR study was a prospective, multicentre, single-arm study conducted at six centres in China. Eligible patients had native coronary artery target lesions with visually estimated diameter stenosis of 30–90% and diagnosis of stable or unstable angina pectoris. Using computational pressure-fluid dynamics, in conjunction with thrombolysis in myocardial infarction (TIMI) frame count, applied to coronary angiography, caFFR was measured online in real-time and compared blind to conventional invasive FFR by an independent core laboratory. The primary endpoint was the agreement between caFFR and FFR, with a pre-specified performance goal of 84%. Between June and December 2018, matched caFFR and FFR measurements were performed in 328 coronary arteries. Total operational time for caFFR was 4.54 ± 1.48 min. caFFR was highly correlated to FFR (R = 0.89, P = 0.76) with a mean bias of −0.002 ± 0.049 (95% limits of agreement −0.098 to 0.093). The diagnostic performance of caFFR vs. FFR was diagnostic accuracy 95.7%, sensitivity 90.4%, specificity 98.6%, positive predictive value 97.2%, negative predictive value 95.0%, and area under the receiver operating characteristic curve of 0.979.

Conclusions

Using wire-based FFR as the reference, caFFR has high accuracy, sensitivity, and specificity. caFFR could eliminate the need of a pressure wire, technical error and potentially increase adoption of physiological assessment of coronary artery stenosis severity.

Clinical Trial Registration

URL: http://www.chictr.org.cn Unique Identifier: ChiCTR1800019522.

Computational instantaneous wave-free ratio (IFR) for patient-specific coronary artery stenoses using 1D network models

In this work, we estimate the diagnostic threshold of the instantaneous wave-free ratio (iFR) through the use of a one-dimensional haemodynamic framework. To this end, we first compared the computed fractional flow reserve (cFFR) predicted from a 1D computational framework with invasive clinical measurements. The framework shows excellent promise and utilises minimal patient data from a cohort of 52 patients with a total of 66 stenoses. The diagnostic accuracy of the cFFR model was 75.76%, with a sensitivity of 71.43%, a specificity of 77.78%, a positive predictive value of 60%, and a negative predictive value of 85.37%. The validated model was then used to estimate the diagnostic threshold of iFR. The model determined a quadratic relationship between cFFR and the ciFR. The iFR diagnostic threshold was determined to be 0.8910 from a receiver operating characteristic curve that is in the range of 0.89 to 0.9 that is normally reported in clinical studies.

Non-invasive coronary CT angiography-derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies

Non-invasive coronary computed tomography (CT) angiography-derived fractional flow reserve (cFFR) is an emergent approach to determine the functional relevance of obstructive coronary lesions. Its feasibility and diagnostic performance has been reported in several studies. It is unclear if differences in sensitivity and specificity between these studies are due to study design, population, or "computational methodology." We evaluate the diagnostic performance of four different computational workflows for the prediction of cFFR using a limited data set of 10 patients, three based on reduced-order modelling and one based on a 3D rigid-wall model. The results for three of these methodologies yield similar accuracy of 6.5% to 10.5% mean absolute difference between computed and measured FFR. The main aspects of modelling which affected cFFR estimation were choice of inlet and outlet boundary conditions and estimation of flow distribution in the coronary network. One of the reduced-order models showed the lowest overall deviation from the clinical FFR measurements, indicating that reduced-order models are capable of a similar level of accuracy to a 3D model. In addition, this reduced-order model did not include a lumped pressure-drop model for a stenosis, which implies that the additional effort of isolating a stenosis and inserting a pressure-drop element in the spatial mesh may not be required for FFR estimation. The present benchmark study is the first of this kind, in which we attempt to homogenize the data required to compute FFR using mathematical models. The clinical data utilised in the cFFR workflows are made publicly available online.

The importance of side branches in modeling 3D hemodynamics from angiograms for patients with coronary artery disease

Genesis of atherosclerotic lesions in the human arterial system is critically influenced by the fluid mechanics. Applying computational fluid dynamic tools based on accurate coronary physiology derived from conventional biplane angiogram data may be useful in guiding percutaneous coronary interventions. The primary objective of this study is to build and validate a computational framework for accurate personalized 3-dimensional hemodynamic simulation across the complete coronary arterial tree and demonstrate the influence of side branches on coronary hemodynamics by comparing shear stress between coronary models with and without these included. The proposed novel computational framework based on biplane angiography enables significant arterial circulation analysis. This study shows that models that take into account flow through all side branches are required for precise computation of shear stress and pressure gradient whereas models that have only a subset of side branches are inadequate for biomechanical studies as they may overestimate volumetric outflow and shear stress. This study extends the ongoing computational efforts and demonstrates that models based on accurate coronary physiology can improve overall fidelity of biomechanical studies to compute hemodynamic risk-factors.

Angiogram based fractional flow reserve in patients with dual/triple vessel coronary artery disease

OBJECTIVE

To assess the performance of angiography derived Fractional Flow Reserve (FFRangio) in multivessel disease (MVD) patients undergoing angiography.

BACKGROUND

FFR is the reference standard for physiologic assessment of coronary stenosis and guidance of revascularization, especially in patients with MVD, yet it remains grossly underutilized. The non-wire based FFRangio performs well in non-MVD patients, but its accuracy in MVD is unknown.

METHODS

A prospective clinical study was conducted at Gifu Heart Centre, Japan. Patients underwent physiologic assessment of all relevant coronary lesions using wire-based FFR (wbFFR) and FFRangio. Primary outcome was diagnostic performance (sensitivity, specificity, accuracy) for FFRangio with wbFFR as reference. Other outcomes were the correlation between wbFFR/FFRangio, time required for wbFFR/FFRangio measurements, and the effect of wbFFR/FFRangio on the reclassification of coronary disease severity.

RESULTS

Fifty patients (118 lesions in total) were included. Mean age was 72 ± 9 years, 72% were male, 36% had triple vessel disease and the average SYNTAX score was 13. The mean measurement of wbFFR and FFRangio were 0.83 ± 0.12 and 0.81 ± 0.11, respectively. Accuracy, sensitivity and specificity for FFRangio were 92.3% (95% CI 79.1-98.4%), 92.4% (95% CI 84.3-97.2%) and 92.4% (95% CI 87.4-97.3%), respectively. Pearson's r between wbFFR and FFRangio was 0.83. FFRangio measurement was faster than wbFFR (9.6 ± 3.4 vs. 15.0 ± 8.9 min, p < 0.001).

CONCLUSIONS

In patients with MVD, FFRangio shows good correlation and excellent diagnostic performance compared to wbFFR, and measuring FFRangio is faster than wbFFR. These results highlight the potential clinical benefits of utilizing FFRangio among patients with MVD.

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.

Coronary fractional flow reserve derived from intravascular ultrasound imaging: Validation of a new computational method of fusion between anatomy and physiology

OBJECTIVES

To evaluate the diagnostic performance of a novel computational algorithm based on three-dimensional intravascular ultrasound (IVUS) imaging in estimating fractional flow reserve (IVUS_FR ), compared to gold-standard invasive measurements (FFR_INVAS ).

BACKGROUND

IVUS provides accurate anatomical evaluation of the lumen and vessel wall and has been validated as a useful tool to guide percutaneous coronary intervention. However, IVUS poorly represents the functional status (i.e., flow-related information) of the imaged vessel.

METHODS

Patients with known or suspected stable coronary disease scheduled for elective cardiac catheterization underwent FFR_INVAS measurement and IVUS imaging in the same procedure to evaluate intermediate lesions. A processing methodology was applied on IVUS to generate a computational mesh condensing the geometric characteristics of the vessel. Computation of IVUS_FR was obtained from patient-level morphological definition of arterial districts and from territory-specific boundary conditions. FFR_INVAS measurements were dichotomized at the 0.80 threshold to define hemodynamically significant lesions.

RESULTS

A total of 24 patients with 34 vessels were analyzed. IVUS_FR significantly correlated (r = 0.79; P < 0.001) and showed good agreement with FFR_INVAS , with a mean difference of -0.008 ± 0.067 (P = 0.47). IVUS_FR presented an overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 91%, 89%, 92%, 80%, and 96%, respectively, to detect significant stenosis.

CONCLUSION

The computational processing of IVUS_FR is a new method that allows the evaluation of the functional significance of coronary stenosis in an accurate way, enriching the anatomical information of grayscale IVUS.

State of the art: coronary angiography

In the early days of coronary angiography, the precise quantification of luminal narrowing was challenging. The introduction of balloon angioplasty (percutaneous transluminal coronary angioplasty [PTCA]) by Andreas Grüntzig in 1977 was perhaps the greatest incentive to the development of quantitative coronary angiography (QCA). QCA has played a crucial role in evaluating interventional techniques and assessing the results of new technologies. With the advent of drug-eluting stents (DES), QCA metrics such as late lumen loss and diameter stenosis (restenosis) proved to be instrumental in assessing new technologies. Refinements in QCA with the advent of dedicated bifurcation analysis and three-dimensional (3D) QCA have broadened the application of QCA. Beyond angiographic metrics, new developments in the field of QCA have introduced the functional component in the assessment of coronary lesions. Angiography-derived fractional flow reserve (FFR) may be a good tool for diagnosing ischaemia-producing lesions in patients with non-complex coronary artery disease. Furthermore, the incremental functional information can be used to expand the traditional late lumen loss (LLL) and restenosis concepts.

Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR)

Image-based noninvasive fractional flow reserve (FFR) is an emergent approach to determine the functional relevance of coronary stenoses. The present work aimed to determine the feasibility of using a method based on coronary computed tomography angiography (CCTA) and reduced-order models (0D-1D) for the evaluation of coronary stenoses. The reduced-order methodology (cFFR_RO ) was kept as simple as possible and did not include pressure drop or stenosis models. The geometry definition was incorporated into the physical model used to solve coronary flow and pressure. cFFRRO was assessed on a virtual cohort of 30 coronary artery stenoses in 25 vessels and compared with a standard approach based on 3D computational fluid dynamics (cFFR_3D ). In this proof-of-concept study, we sought to investigate the influence of geometry and boundary conditions on the agreement between both methods. Performance on a per-vessel level showed a good correlation between both methods (Pearson's product-moment R=0.885, P<0.01), when using cFFR_3D  as the reference standard. The 95% limits of agreement were -0.116 and 0.08, and the mean bias was -0.018 (SD =0.05). Our results suggest no appreciable difference between cFFR_RO  and cFFR_3D with respect to lesion length and/or aspect ratio. At a fixed aspect ratio, however, stenosis severity and shape appeared to be the most critical factors accounting for differences in both methods. Despite the assumptions inherent to the 1D formulation, asymmetry did not seem to affect the agreement. The choice of boundary conditions is critical in obtaining a functionally significant drop in pressure. Our initial data suggest that this approach may be part of a broader risk assessment strategy aimed at increasing the diagnostic yield of cardiac catheterisation for in-hospital evaluation of haemodynamically significant stenoses.

Diagnostic Accuracy of Angiography-Based Quantitative Flow Ratio Measurements for Online Assessment of Coronary Stenosis

BACKGROUND

Quantitative flow ratio (QFR) is a novel angiography-based method for deriving fractional flow reserve (FFR) without pressure wire or induction of hyperemia. The accuracy of QFR when assessed online in the catheterization laboratory has not been adequately examined to date.

OBJECTIVES

The goal of this study was to assess the diagnostic performance of QFR for the diagnosis of hemodynamically significant coronary stenosis defined by FFR ≤0.80.

METHODS

This prospective, multicenter trial enrolled patients who had at least 1 lesion with a diameter stenosis of 30% to 90% and a reference diameter ≥2 mm according to visual estimation. QFR, quantitative coronary angiography (QCA), and wire-based FFR were assessed online in blinded fashion during coronary angiography and re-analyzed offline at an independent core laboratory. The primary endpoint was that QFR would improve the diagnostic accuracy of coronary angiography such that the lower boundary of the 2-sided 95% confidence interval (CI) of this estimate exceeded 75%.

RESULTS

Between June and July 2017, a total of 308 patients were consecutively enrolled at 5 centers. Online QFR and FFR results were both obtained in 328 of 332 interrogated vessels. Patient- and vessel-level diagnostic accuracy of QFR was 92.4% (95% CI: 88.9% to 95.1%) and 92.7% (95% CI: 89.3% to 95.3%), respectively, both of which were significantly higher than the pre-specified target value (p < 0.001). Sensitivity and specificity in identifying hemodynamically significant stenosis were significantly higher for QFR than for QCA (sensitivity: 94.6% vs. 62.5%; difference: 32.0% [p < 0.001]; specificity: 91.7% vs. 58.1%; difference: 36.1% [p < 0.001]). Positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for QFR were 85.5%, 97.1%, 11.4, and 0.06. Offline analysis also revealed that vessel-level QFR had a high diagnostic accuracy of 93.3% (95% CI: 90.0% to 95.7%).

CONCLUSIONS

The study met its prespecified primary performance goal for the level of diagnostic accuracy of QFR in identifying hemodynamically significant coronary stenosis. (The FAVOR [Functional Diagnostic Accuracy of Quantitative Flow Ratio in Online Assessment of Coronary Stenosis] II China study]; NCT03191708).