Invasive Fractional Flow Reserve Research Articles

Quantitative but not Qualitative Computed Tomography Coronary Angiography High Risk Plaque Characteristics are Associated with Major Adverse Cardiovascular Events in Patients with Non-Significant Invasive Fractional Flow Reserve

Background: Functionally non-significant coronary stenoses by fractional flow reserve assessment (FFR >0.8) are not routinely intervened upon. These patients may still remain at risk of major adverse cardiovascular events (MACE). Computed tomography coronary angiography (CTCA) allows non-invasive assessment of qualitative and quantitative high-risk plaque (HRP) morphology which are associated with future MACE. We seek to determine if HRP was associated with MACE in patients with functionally non-significant FFR. Methods: Consecutive patients with stable chest pain who underwent CTCA and FFR within a 3-month interval, between 2009–2017, at MonashHeart were examined. Qualitative HRP were low attenuation plaque (LAP, <30 Hounsfield units), positive remodelling and spotty calcification. Quantitative HRP was regarded as LAP volume normalised to vessel length analysed (distal plaque to ostium). MACE was defined as cardiovascular death, myocardial infarction (MI) or unstable angina. Results: 54 patients (age 63 ± 11, 70% males) were evaluated. There were 39 vessels in 39 patients (72%) with qualitative HRP. Seven patients (13%) experienced MACE over 5.4-year median follow-up with 5 demonstrating qualitative HRP. The mean interval between CTCA and MACE was 576 days. Qualitative HRP features were not associated with MACE, LAP (p = 0.2), positive remodelling (p = 0.99), spotty calcification (p = 0.69). Normalised LAP volume was predictive of MACE (Hazard Ratio 1.10, 95% confidence interval 1.01–1.10, p = 0.02). Traditional anatomic predictors of MACE, including total plaque burden (p = 0.91), minimal luminal area <4 mm (p = 0.99) and total plaque volume (p = 0.4) were not significant predictors. Conclusions: In patients with functionally non-significant FFR, there remains a risk of MACE and quantitative but not qualitative HRP characteristics appear to associate with prognosis.

Current Evidence in Cardiothoracic Imaging: Computed Tomography-derived Fractional Flow Reserve in Stable Chest Pain.

High-accuracy diagnostic imaging is needed to diagnose and manage coronary artery disease as well as to allow risk stratification for future events. Advancements in multidetector computed tomography and image postprocessing allow for routine computed tomography coronary angiography to provide anatomic luminal assessment similar to invasive coronary angiography, and, similarly, computational fractional flow reserve derived from computed tomography facilitates determination of hemodynamically relevant stenosis comparable to invasive fractional flow reserve. In this review article, we describe the diagnostic performance and the potential impact of fractional flow reserve derived from computed tomography in clinical practice.

Instantaneous wave-free ratio (iFR®) to determine hemodynamically significant coronary stenosis: A comprehensive review.

Coronary angiography is considered to be the gold standard in the morphological evaluation of coronary artery stenosis. The morphological assessment of the severity of a coronary lesion is very subjective. Thus, the invasive fractional flow reserve (FFR) measurement represents the current standard for estimation of the hemodynamic significance of coronary artery stenosis. The FFR-guided revascularization strategy was initially classified as a Class-IA-recommendation in the 2014 European Society of Cardiology/European Association for Cardio-Thoracic Surgery guidelines on myocardial revascularization. Both the Deferral vs Performance of Percutaneous Coronary Intervention of Functionally Non-Significant Coronary Stenosis and Flow Reserve vs Angiography for Multivessel Evaluation studies showed no treatment advantage for hemodynamically insignificant stenoses. With the help of FFR (and targeted interventions), clinical results could be improved; however, the use in clinical practice is still limited due to the need of adenosine administration and a significant prolongation of the length of the procedure. Instantaneous wave-free ratio (iFR®) is a new innovative approach for the determination of the hemodynamic significance of coronary stenosis, which can be obtained at rest without the use of vasodilators. Regarding the periprocedural complications as well as prognosis, iFR® showed non-inferiority to FFR in the SWEDEHEART and DEFINE-FLAIR trials. Furthermore, iFR®, enhanced by iFR®-pullback, provides the possibility to display the iFR®-change over the course of the vessel to create a hemodynamic map.

Referral of patients for fractional flow reserve using quantitative flow ratio.

Quantitative flow ratio (QFR) is a recently developed technique to calculate fractional flow reserve (FFR) based on 3D quantitative coronary angiography and computational fluid dynamics, obviating the need for a pressure-wire and hyperaemia induction. QFR might be used to guide patient selection for FFR and subsequent percutaneous coronary intervention (PCI) referral in hospitals not capable to perform FFR and PCI. We aimed to investigate the feasibility to use QFR to appropriately select patients for FFR referral. Patients who underwent invasive coronary angiography in a hospital where FFR and PCI could not be performed and were referred to our hospital for invasive FFR measurement, were included. Angiogram images from the referring hospitals were retrospectively collected for QFR analysis. Based on QFR cut-off values of 0.77 and 0.86, our patient cohort was reclassified to 'no referral' (QFR ≥0.86), referral for 'FFR' (QFR 0.78-0.85), or 'direct PCI' (QFR ≤0.77). In total, 290 patients were included. Overall accuracy of QFR to detect an invasive FFR of ≤0.80 was 86%. Based on a QFR cut-off value of 0.86, a 50% reduction in patient referral for FFR could be obtained, while only 5% of these patients had an invasive FFR of ≤0.80 (thus, these patients were incorrectly reclassified to the 'no referral' group). Furthermore, 22% of the patients that still need to be referred could undergo direct PCI, based on a QFR cut-off value of 0.77. QFR is feasible to use for the selection of patients for FFR referral.

Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.

BackgroundInvasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%–65%) for the prediction of FFR < 0.80. One of the reasons for the visual–functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus ≥ 0.80.Methods and findingsA retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiography, FFR, and CCTA at the Asan Medical Center, Seoul, Korea, between 1 May 2012 and 30 November 2015. The mean age was 63 ± 10 years, 76% were men, and 72% of the patients presented with stable angina. Of these, 932 patients (assessed before 31 January 2015) constituted the training set for the algorithm, and 200 patients (assessed after 1 February 2015) served as a test cohort to validate its diagnostic performance. Additionally, external validation with 79 patients from two centers (CHA University, Seongnam, Korea, and Ajou University, Suwon, Korea) was conducted. After automatic contour calibration using the caliber of guiding catheter, quantitative coronary angiography was performed using the edge-detection algorithms (CAAS-5, Pie-Medical). Clinical information was provided by the Asan BiomedicaL Research Environment (ABLE) system. The CCTA-based myocardial segmentation (CAMS)-derived myocardial volume supplied by each vessel (right coronary artery [RCA], left anterior descending [LAD], left circumflex [LCX]) and the myocardial volume subtended to a stenotic segment (CAMS-%Vsub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%Vsub and (2) identifying the lesions with an FFR < 0.80 was constructed. Angiography-based ML, employing a light gradient boosting machine (GBM), showed mean absolute errors (MAEs) of 5.42%, 8.57%, and 4.54% for predicting CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA, respectively. The percent myocardial volumes predicted by ML were used to predict the CAMS-%Vsub. With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%Vsub) and CAMS-%Vsub were minimized by the elastic net (6.26% ± 0.55% for LAD, 5.79% ± 0.68% for LCX, and 2.95% ± 0.14% for RCA lesions). Using all attributes (age, sex, involved vessel segment, and angiographic features affecting the myocardial territory and stenosis degree), the ML classifiers (L2 penalized logistic regression, support vector machine, and random forest) predicted an FFR < 0.80 with an accuracy of approximately 80% (area under the curve [AUC] = 0.84–0.87, 95% confidence intervals 0.71–0.94) in the test set, which was greater than that of diameter stenosis (DS) > 53% (66%, AUC = 0.71, 95% confidence intervals 0.65–0.78). The external validation showed 84% accuracy (AUC = 0.89, 95% confidence intervals 0.83–0.95). The retrospective design, single ethnicity, and the lack of clinical outcomes may limit this prediction model’s generalized application.ConclusionWe found that angiography-based ML is useful to predict subtended myocardial territories and ischemia-producing lesions by mitigating the visual–functional mismatch between angiographic and FFR. Assessment of clinical utility requires further validation in a large, prospective cohort study.

Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis

ObjectivesTo evaluate the added value of deep learning (DL) analysis of the left ventricular myocardium (LVM) in resting coronary CT angiography (CCTA) over determination of coronary degree of stenosis (DS), for identification of patients with functionally significant coronary artery stenosis.MethodsPatients who underwent CCTA prior to an invasive fractional flow reserve (FFR) measurement were retrospectively selected. Highest DS from CCTA was used to classify patients as having non-significant (≤ 24% DS), intermediate (25–69% DS), or significant stenosis (≥ 70% DS). Patients with intermediate stenosis were referred for fully automatic DL analysis of the LVM. The DL algorithm characterized the LVM, and likely encoded information regarding shape, texture, contrast enhancement, and more. Based on these encodings, features were extracted and patients classified as having a non-significant or significant stenosis. Diagnostic performance of the combined method was evaluated and compared to DS evaluation only. Functionally significant stenosis was defined as FFR ≤ 0.8 or presence of angiographic high-grade stenosis (≥ 90% DS).ResultsThe final study population consisted of 126 patients (77% male, 59 ± 9 years). Eighty-one patients (64%) had a functionally significant stenosis. The proposed method resulted in improved discrimination (AUC = 0.76) compared to classification based on DS only (AUC = 0.68). Sensitivity and specificity were 92.6% and 31.1% for DS only (≥ 50% indicating functionally significant stenosis), and 84.6% and 48.4% for the proposed method.ConclusionThe combination of DS with DL analysis of the LVM in intermediate-degree coronary stenosis may result in improved diagnostic performance for identification of patients with functionally significant coronary artery stenosis.Key Points• Assessment of degree of coronary stenosis on CCTA has consistently high sensitivity and negative predictive value, but has limited specificity for identifying the functional significance of a stenosis.• Deep learning algorithms are able to learn complex patterns and relationships directly from the images without prior specification of which image features represent presence of disease, and thereby may be more sensitive to subtle changes in the LVM caused by functionally significant stenosis.• Addition of deep learning analysis of the left ventricular myocardium to the evaluation of degree of coronary artery stenosis improves diagnostic performance and increases specificity of resting CCTA. This could potentially decrease the number of patients undergoing invasive coronary angiography.

Abstract 16924: Diagnostic Accuracy of Quantification Myocardial Perfusion Ratio Derived From Dynamic CT Perfusion Imaging for Detection of Functionally Significant Stenosis

Introduction: Quantification perfusion ratio (QPR) is defined as the ratio between myocardial blood flow (MBF) in the stenosis-related territory and MBF in the non-stenosis-related territory derived from stress dynamic CT myocardial perfusion (CTP) imaging. Hypothesis: We sought to evaluate whether QPR can be used as a novel non-invasive method for detection of functionally significant coronary artery stenosis compared with fractional flow reserve (FFR). Methods: This study was retrospectively included 25 consecutive patients (32 vessels) with coronary artery disease and examined with CT angiography, CTP, coronary angiography, and FFR. QPR and stress MBF results were compared with invasive FFR using a threshold of 0.80. Results: The QPRvalues were significantly correlated to FFR values (R=0.865,P &lt;0.0001, Fig 1). The area under the receiver operator curve was larger for QPR (0.937) compared with stress MBF (0.812, Fig 2). The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of QPR were 93.8%, 86.7%, 100%, 100%, and 89.5%, respectively. Conclusions: QPR was feasible and provided high diagnostic value for detecting abnormal FFR in patients with stable coronary artery disease.

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

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

Optimized interpretation of fractional flow reserve derived from computed tomography: Comparison of three interpretation methods

BackgroundAn optimal system for interpreting fractional flow reserve (FFR) values derived from CT (FFRCT) is lacking. We sought to evaluate performance of three FFRCT measurements in detecting ischemia by comparing them with invasive FFR. MethodsFor 73 vessels in 50 patients who underwent coronary CT angiography (CCTA) and FFRCT analysis followed by invasive FFR, the greatest diameter stenosis on CCTA, FFRCT difference between distal and proximal to the stenosis (ΔFFRCT), FFRCT 2 cm distal to the stenosis (lesion-specific FFRCT), and the lowest FFRCT in distal vessel tip were calculated. Significant obstruction (≥50% diameter stenosis) and ischemia (lesion-specific FFRCT ≤0.80, the lowest FFRCT ≤0.80, or ΔFFRCT ≥0.12 based on the greatest Youden index) were compared with invasive FFR (≤0.80). ResultsForty (55%) vessels demonstrated ischemia during invasive FFR. On multivariable generalized estimating equations, ΔFFRCT (odds ratio [OR] 10.2, p < 0.01) remained a predictor of ischemia over CCTA (OR 2.9), lesion-specific FFRCT (OR 3.1), and the lowest FFRCT (OR 0.9) (p > 0.05 for all). Area under the curve (AUC) of ΔFFRCT (0.86) was higher than CCTA (0.66), lesion-specific FFRCT (0.71), and the lowest FFRCT (0.65) (p < 0.01 for all). Addition of each FFRCT measure to CCTA showed improvement of AUC and significant net reclassification improvement (NRI): ΔFFRCT (AUC 0.84, NRI 1.24); lesion-specific FFRCT (AUC 0.77, NRI 0.83); and the lowest FFRCT (AUC 0.76, NRI 0.59) (p < 0.01 for all). ConclusionsCompared with diameter stenosis, ΔFFRCT, lesion-specific FFRCT, and the lowest FFRCT improved ischemia discrimination and reclassification, with ΔFFRCT being superior in identifying and discriminating ischemia.

Correlation of FFR-derived from CT and stress perfusion CMR with invasive FFR in intermediate-grade coronary artery stenosis

Only one-third of intermediate-grade coronary artery stenosis (i.e. 40-70% diameter narrowing) causes myocardial ischemia, requiring most often additional invasive work-up with invasive fractional flow reserve (FFR). To evaluate the correlations between FFR estimates derived from computed tomography (FFRCT) and adenosine perfusion cardiac magnetic resonance (CMR) with invasive FFR in intermediate-grade stenosis. Thirty-seven patients (mean age 61 ± 9 years; 25 men) who underwent adenosine perfusion CMR, quantitative coronary angiography and FFR in the work-up for intermediate-grade stenoses (n = 39) diagnosed at coronary CT angiography were retrospectively evaluated. Blinded FFRCT analysis was computed on each intermediate-grade lesion and correlated to the FFR values. On adenosine CMR, subendocardial time-enhancement maximal upslopes, normalized by respective left ventricle cavity upslopes, were obtained distal to a coronary stenosis (RISK area) and in remote myocardium (REMOTE area). The perfusion was subsequently assessed without (uncorrected RISK) and after correction for remote perfusion (relative myocardial perfusion index = REMOTE/RISK ratio), and then correlated to the FFR values. Differences in correlations were tested with z statistics and considered statistically significant different at a p < 0.05 level. The average FFR value was 0.85 ± 0.10 (0.60-0.98 range), 28% (n = 11) was ≤ 0.80. FFR value correlated poorly with uncorrected RISK upslopes (r = 0.151; p = 0.36), but equally strongly with FFRCT (r = 0.675; p < 0.001) and the relative myocardial perfusion index (r = - 0.63) (p < 0.001; z = 6.72) for assessment of lesion-specific ischemia. Both FFRCT and adenosine perfusion CMR strongly correlate with invasive FFR measurements for intermediate-grade stenosis. These preliminary findings pave the way for further studies evaluating non-invasively intermediate coronary stenosis in clinical practice.

Less invasive fractional flow reserve measurement from 3-dimensional quantitative coronary angiography and classic fluid dynamic equations.

The aim of this study was to develop a simplified model of FFR calculation (FFRsim) derived from three-dimensional (3D) coronary angiographic data and classic fluid dynamic equations without using finite element analysis. Intracoronary pressure measurements were performed by pressure wire sensors. The lumens of the interrogated vessel segments were reconstructed in 3D. The coronary artery volumetric flow was calculated based on the velocity of the contrast material. Pressure gradients were computed by classic fluid dynamic equations. The diagnostic power of the simplified computation of the FFR (FFRsim) was assessed by comparing the results with standard invasive FFR measurements (FFRmeas) in 68 vessels with a single stenosis. We found a strong correlation between the FFRsim and the FFRmeas (r=0.86, p<0.0001). The sensitivity and specificity for predicting the abnormal FFR of ≤0.80 (indicating haemodynamically significant stenosis) were 90% and 100%, respectively. The area under the curve (AUC) was 0.96. To achieve 100% negative and positive predictive values we defined the FFRsim >0.88 and the FFRsim ≤0.8 ranges. In our patient population, these ranges were found in 69% of the cases. According to our simplified model, the invasive FFR measurement can be omitted without misclassification in pre-specified ranges of the calculated FFRsim.

GW29-e1123 Computed Tomography Angiography-Derived Fractional Flow Reserve (CT-FFR) for the Detection of Myocardial Ischemia-with Invasive Fractional Flow Reserve as Reference: Systematic Review and Meta-Analysis

Carry out an updated meta-analysis to comprehensively search and review evidence available heretofore and derive reliable assessment of the diagnostic performances of computed tomography angiography-derived fractional flow reserve (CT-FFR) using fractional flow reserve (FFR) as the reference

Diagnostic accuracy of simultaneous evaluation of coronary arteries and myocardial perfusion with single stress cardiac computed tomography acquisition compared to invasive coronary angiography plus invasive fractional flow reserve

BackgroundCoronary computed tomography angiography (cCTA) has limited diagnostic accuracy in patients with intermediate to high pre-test likelihood of coronary artery disease (CAD) that may have large amounts of coronary calcium. Stress computed tomography myocardial perfusion (CTP) has emerged as a valuable strategy, combining anatomical and functional assessment of CAD. Purpose of the study is to evaluate the diagnostic accuracy of combining coronary artery imaging and myocardial perfusion in a single stress dataset versus invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR) as reference standard. MethodsOne-hundred-thirty consecutive symptomatic patients (age: 65 ± 9 years; men: 70%) scheduled for clinically indicated ICA plus invasive FFR were prospectively enrolled. cCTA + CTP were simultaneously evaluated in a single stress-dataset by blinded readers and compared to ICA and invasive FFR findings. ResultsCTP was successfully performed in all patients. The most common artifacts observed in the stress dataset for coronary artery imaging were blooming effect and motion effect related. Overall evaluability of coronary arteries by using cCTA stress dataset was 93%. In a vessel and patient-based model, stress cCTA + stress CTP showed sensitivity, specificity, negative predictive value, positive predictive value and diagnostic accuracy of 93%, 94%, 97%, 85%, 94%, and 98%, 86%, 98%, 85%, 92%, respectively. The overall effective dose (ED) of stress protocol acquisition alone was 2.5 ± 1.1 mSv. ConclusionsSimultaneous evaluation of coronary arteries and myocardial perfusion with single stress acquisition is feasible and it has diagnostic accuracy and low ED to identify functionally significant stenosis in patients with intermediate to high risk for CAD.

Anatomically and functionally relevant coronary stenoses in patients with normal single-photon emission computed tomography but persistent stable angina.

Single-photon emission computed tomography (SPECT) is widely used for the assessment of coronary artery disease and for decision making regarding revascularization. Concerns about possible false negative findings exist. Our aim was to assess the prevalence of stenoses which are both functionally and anatomically significant in patients referred for invasive fractional flow reserve (FFR) measurements following a normal SPECT, because of persistent complaints. One hundred and thirty-three consecutive patients with normal SPECT were included, with a total of 180 FFR measurements. Luminal narrowing of ≥70% (≥50% for left main) together with a FFR ≤0.80 denoted an anatomically and functionally significant coronary artery stenosis. Separate analyses were performed for FFR <0.75. Mean age of the patients was 65, 40% were women. Sixteen percent of the study population had both anatomically and functionally significant stenoses. Besides the use of nitrate, no differences in baseline characteristics, symptoms, coronary history, or pre-test likelihood could be identified for the prediction of functionally relevant obstructive coronary disease. If FFR <0.75 was used, only 7.5% of the patients had both anatomically and functionally significant stenoses. In patients with normal SPECT who undergo FFR measurements because of persistent complaints, the prevalence of stenoses which are both anatomically and functionally significant is low. This suggests that the prevalence of false-negative SPECT is (very) low.

Fractional Flow Reserve-Guided Hybrid Coronary Revascularization in Multi-Vessel Coronary Arteries Disease

Coronary artery bypass grafting without cardiopulmonary bypass (OPCABG) as a minimally invasive cardiac surgery (MICS) procedure has been used increasingly to treat coronary artery disease. We present our experience using off-pump minimally – invasive direct coronary artery bypass grafting (OPMIDCAB) and fractional flow reserve (FFR) – guided percutaneous coronary interventions (PCI) strategy for coronary revascularization.&#x0D; Purpose. Presentation of our experience in FFR – guided hybrid coronary revascularization in patients with multi – vessel coronary artery disease.&#x0D; Conclusion. A hybrid procedure that changes a conventional CABG of all stenotic arteries, into a FFR – guided PCI is an appealing way to minimize the risk of complex heart surgery, combining positive outcomes of CABG along with low stent restenosis and thrombosis rates compared to venous grafts.

Assessment of lesion-specific ischemia using fractional flow reserve (FFR) profiles derived from coronary computed tomography angiography (FFRCT) and invasive pressure measurements (FFRINV): Importance of the site of measurement and implications for patient referral for invasive coronary angiography and percutaneous coronary intervention

BackgroundFractional flow reserve (FFR)-derived from computed tomography angiography (CTA; FFRCT) and invasive FFR (FFRINV) are used to assess the need for invasive coronary angiography (ICA) and percutaneous coronary intervention (PCI). The optimal location for measuring FFR and the impact of measurement location have not been well defined. Methods930 patients (age 60.7 + 10 years, 59% male) were included in this study. Normal and diseased coronary arteries were classified into stenosis grades 0–4 in the left anterior descending artery (LAD, n = 518), left circumflex (LCX, n = 112) and right coronary artery (RCA, n = 585). FFRCT (n = 1215 arteries) and FFRINV (n = 26 LAD) profiles were developed by plotting FFR values (y-axis) versus site of measurement (x-axis: ostium, proximal, mid, distal segments). The best location to measure FFR was defined relative to the distal end of the stenosis. FFR ≤0.8 was considered positive for ischemia. ResultsIn normal and stenotic coronary arteries there are significant declines in FFRCT and FFRINV from the ostium to the distal vessel (p < 0.001), due to lesion-specific ischemia and to effects unrelated to the lesion. A reliable location (distal to the stenosis) is 10.5 mm [IQR 7.3–14.8 mm] for FFRCT and within 20–30 mm for FFRINV. Rates of positive FFR (from the distal vessel) reclassified to negative FFR (distal to the stenosis) are 61% (FFRCT) and 33% (FFRINV). ConclusionFFRCT and FFRINV values are influenced by stenosis severity and the site of measurement. FFR measurements from the distal vessel may over-estimate lesion-specific ischemia and result in unnecessary referrals for ICA and PCI.

TCT-383 Clinical Utility of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System: Comparison with Invasive Fractional Flow Reserve

TCT-383 Clinical Utility of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System: Comparison with Invasive Fractional Flow Reserve

TCT-578 Assessment of Lesion-Specific Ischemia Using Fractional Flow Reserve (FFR) Profiles Derived from Coronary Computed Tomography Angiography (FFRCT) and Invasive Measurements (FFRINV): Importance of the Site of Measurement and Implications for Patient Referral for Invasive Coronary Angiography

Fractional flow reserve (FFR)-derived from computed tomography angiography (CTA; FFRCT) and invasive FFR (FFRINV) are used to assess the need for invasive coronary angiography (ICA) and percutaneous coronary intervention (PCI). The optimal location for measuring FFR and the impact of measurement

Computationally simulated fractional flow reserve from coronary computed tomography angiography based on fractional myocardial mass.

Computed tomography angiography (CCTA)-based calculations of fractional flow reserve (FFR) can improve the diagnostic performance of CCTA for physiologically significant stenosis but the computational resource requirements are high. This study aimed at establishing a simple and efficient algorithm for computing simulated FFR (S-FFR). A total of 107 patients who underwent CCTA and invasive FFR measurements were enrolled in the study. S-FFR was calculated using 145 evaluable coronary arteries with off-the-shelf softwares. FFR ≤ 0.80 was a reference threshold for diagnostic performance of diameter stenosis (DS) ≥ 50%, DS ≥ 70%, or S-FFR ≤ 0.80. FFR ≤ 0.80 was identified in 78 vessels (54%). In per-vessel analysis, S-FFR showed good correlation (r = 0.83) and agreement (mean difference = 0.02 ± 0.08) with FFR. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of S-FFR ≤ 0.80 for FFR ≤ 0.80 were 84%, 92%, 92%, 83%, and 88%, respectively. S-FFR ≤ 0.80 showed much higher predictive performance for FFR ≤ 0.80 compared with DS ≥ 50% or DS ≥ 70% (c-statistics = 0.92 vs. 0.58 or 0.65, p < 0.001, all). The classification agreement between FFR and S-FFR was > 80% when the average of FFR and S-FFR was < 0.76 or > 0.86. Per-patient analysis showed consistent results. In this study, a simple and computationally efficient simulated FFR (S-FFR) algorithm is designed and tested using non-proprietary off-the-shelf software. This algorithm may expand the accessibility of clinical applications for non-invasive coronary physiology study.

Computational quantitative flow ratio to assess functional severity of coronary artery stenosis

BackgroundComputational quantitative flow ratio (QFR) based on 3-dimensional quantitative coronary angiography (3D QCA) analysis offers the opportunity to assess the significance of coronary artery disease (CAD) without using an invasive pressure wire or inducing hyperemia. This study aimed to evaluate the diagnostic performance of QFR compared to wire-based fractional flow reserve (FFR) and to validate the previously reported QFR cut-off value of >0.90 to safely rule out functionally significant CAD. MethodsQFR was retrospectively derived from standard-care coronary angiograms. Correlation and agreement of fixed-flow QFR (fQFR) and contrast-flow QFR (cQFR) models with invasive wire-based FFR was calculated. Diagnostic performance of QFR was evaluated at different QFR cut-off values defining significant CAD (FFR ≤ 0.80). Results101 vessels in 96 patients who underwent FFR were studied. Mean FFR was 0.87 ± 0.08 and 21 of 101 (21%) vessels had an FFR ≤ 0.80. Correlation of fQFR and cQFR with FFR was r = 0.71 (p < 0.001) and r = 0.70 (p < 0.001), respectively. Sensitivity and specificity were 57% and 93% for fQFR and 67% and 96% for cQFR at a QFR cut-off value >0.80 defining non-significant CAD, respectively. fQFR > 0.90 was present in 34 (34%) and cQFR > 0.90 in 39 (39%) vessels. For both QFR models, none of the vessels with QFR > 0.90 had an FFR ≤ 0.80. ConclusionsQFR appears to be a safe and effective gatekeeper to wire-based FFR when applying a QFR threshold of >0.90 to rule out significant CAD. Further prospective research is required to establish QFR in the real-life setting of functional CAD assessment in the catheterization laboratory.