Angiography-derived Fractional Flow Reserve Research Articles

Validation of virtual fractional flow reserve pullback curves.

Angiography-derived fractional flow reserve (virtual FFR) has shown excellent diagnostic performance compared with wire-based FFR. However, virtual FFR pullback curves have not been validated yet. To validate the accuracy of virtual FFR pullback curves compared to wire-based FFR pullbacks and to assess their clinical utility using patient-reported outcomes. Pooled analysis of two prospective studies, including patients with hemodynamically significant (FFR ≤ 0.80) coronary artery disease (CAD). Virtual and wire-based FFR pullbacks were compared to assess the accuracy of virtual pullbacks to characterize CAD as focal or diffuse. Pullbacks were analyzed visually and quantitatively using the pullback pressure gradient (PPG). Patients underwent PCI, and the Seattle Angina Questionnaire (SAQ) was administered at 3-month follow-up. A total of 298 patients (300 vessels) with both virtual and wire-based pullbacks who underwent PCI were included in the analysis. The mean age was 61.8 ± 8.8, and 15% were female. The agreement on the visual adjudication of the CAD pattern was fair (Cohen's Kappa: 0.31, 95% confidence interval: 0.18-0.45). The mean PPG were 0.65 ± 0.18 from virtual pullbacks and 0.65 ± 0.13 from wire-based pullbacks (r = 0.68, mean difference 0, limits of agreement -0.27 to 0.28). At follow-up, patients with high virtual PPG (>0.67) had higher SAQ angina frequency scores (i.e., less angina) than those with low virtual PPG (SAQ scores 92.0 ± 14.3 vs. 85.5 ± 23.1, p = 0.022). Virtual FFR pullback curves showed moderate agreement with wire-based FFR pullbacks. Nonetheless, patients with focal disease based on virtual PPG reported greater improvement in angina after PCI.

AngioPy segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation

BackgroundQuantitative coronary angiography (QCA) typically employs traditional edge detection algorithms that often require manual correction. This has important implications for the accuracy of downstream 3D coronary reconstructions and computed haemodynamic indices (e.g. angiography-derived fractional flow reserve).We developed AngioPy, a deep-learning model for coronary segmentation that employs user-defined ground-truth points to boost performance and minimise manual correction. We compared its performance without correction with an established QCA system. MethodsDeep learning models integrating user-defined ground-truth points were developed using 2455 images from the Fractional Flow Reserve versus Angiography for Multivessel Evaluation 2 (FAME 2) study. External validation was performed on a dataset of 580 images. Vessel dimensions from 203 images with mild/moderate stenoses segmented by AngioPy (without correction) and an established QCA system (Medis QFR®) were compared (609 diameters). ResultsThe top-performing model had an average F1 score of 0.927 (pixel accuracy 0.998, precision 0.925, sensitivity 0.930, specificity 0.999) with 99.2 % of masks exhibiting an F1 score > 0.8. Similar results were seen with external validation (F1 score 0.924, pixel accuracy 0.997, precision 0.921, sensitivity 0.929, specificity 0.999). Vessel dimensions from AngioPy exhibited excellent agreement with QCA (r = 0.96 [95 % CI 0.95–0.96], p < 0.001; mean difference − 0.18 mm [limits of agreement (LOA): −0.84 to 0.49]), including the minimal luminal diameter (r = 0.93 [95 % CI 0.91–0.95], p < 0.001; mean difference − 0.06 mm [LOA: −0.70 to 0.59]). ConclusionAngioPy, an open-source tool, performs rapid and accurate coronary segmentation without the need for manual correction. It has the potential to increase the accuracy and efficiency of QCA.

Diagnostic performance of angiography-derived fractional flow reserve compared to pressure wire-derived fractional flow reserve: Rationale and design of MPFFR pivotal trial

BackgroundCardiovascular disease remains the leading cause of death and the use of percutaneous coronary intervention (PCI) is steadily increasing. Current guidelines advocate the use of the fractional flow reserve (FFR) to assess coronary stenosis and treatment strategies; however, invasive FFR has some limitations. Angiography-derived FFR is a potential alternative for calculating FFR from two-dimensional (2D) angiographic images, thereby reducing invasiveness and complications. A novel artificial intelligence (AI)-based angiography-derived FFR, named “MPFFR,” offers automated operator-independent hemodynamic calculations; this phase 3 trial aims to validate its diagnostic performance against 2D-quantitative coronary angiography (QCA). Methods and analysisThis pivotal MPFFR trial is a prospective, multicenter, single-blind study. This trial involves patients with coronary artery disease (CAD) from eight cardiovascular centers. Invasive FFR will be performed according to standard guidelines and defined as the reference standard. Angiography-derived FFR will be computed using a proprietary method and 2D-QCA will be performed using validated software. The primary endpoint is the area under the curve for identifying physiologically significant coronary stenosis (FFR ≤0.80), with secondary endpoints including diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and correlations between angiography-derived and invasive FFR. This study is designed to demonstrate the superiority of angiography-derived FFR over 2D-QCA and is powered to achieve this with a sample size of 240 patients. Medipixel Inc. supports the trial and is not involved in the data analysis or management.

The predictive value of coronary computed tomography angiography-derived fractional flow reserve for perioperative cardiac events in lung cancer surgery.

As a non-invasive coronary functional examination, coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) showed predictive value in several non-cardiac surgeries. This study aimed to evaluate the predictive value of CT-FFR in lung cancer surgery. We retrospectively collected 227 patients from January 2017 to June 2022 and used machine learning-based CT-FFR to evaluate the stable coronary artery disease (CAD) patients undergoing lung cancer surgery. The major adverse cardiac event (MACE) was defined as perioperative myocardial injury (PMI), myocardial infarction, heart failure, atrial and ventricular arrhythmia with hemodynamic disorder, cardiogenic shock and cardiac death. The multivariate logistic regression analysis was performed to identify risk factors for MACE and PMI. The discriminative capacity, goodness-of-fit, and reclassification improvement of prediction model were determined before and after the addition of CT-FFR≤0.8. The incidence of MACE was 20.7% and PMI was 15.9%. CT-FFR significantly outperformed CCTA in terms of accuracy for predicting MACE (0.737 vs 0.524). In the multivariate regression analysis, CT-FFR≤0.8 was an independent risk factor for both MACE [OR=10.77 (4.637, 25.016), P<0.001] and PMI [OR=8.255 (3.372, 20.207), P<0.001]. Additionally, we found that the performance of prediction model for both MACE and PMI improved after the addition of CT-FFR. CT-FFR can be used to assess the risk of perioperative MACE and PMI in patients with stable CAD undergoing lung cancer surgery. It adds prognostic information in the cardiac evaluation of patients undergoing lung cancer surgery.

Angiography-derived physiological patterns of coronary artery disease: implications with post-stenting physiology and long-term clinical outcomes.

Physiological patterns of coronary artery disease (CAD) have emerged as potential determinants of functional results of percutaneous coronary interventions (PCI) and of vessel-oriented clinical outcomes (VOCE). In this study, we evaluated the impact of angiography-derived physiological patterns of CAD on post-PCI functional results and long-term clinical outcomes. Pre-PCI angiography-derived fractional flow reserve (FFR) virtual pullbacks were quantitatively interpreted and used to determine the physiological patterns of CAD. Suboptimal post-PCI physiology was defined as an angiography-derived FFR value ≤ 0.91. The primary endpoint was the occurrence of VOCE at the longest available follow-up. Six hundred fifteen lesions from 516 patients were stratified into predominantly focal (n = 322, 52.3%) and predominantly diffuse (n = 293, 47.7%). Diffuse pattern of CAD was associated with lower post-PCI angiography-derived FFR values (0.91 ± 0.05 vs. 0.94 ± 0.05; p = 0.001) and larger rate of suboptimal post-PCI physiology (43.0 vs. 22.7%; p = 0.001), as compared to focal CAD. At the median follow-up time of 37months (33-58), post-PCI suboptimal physiology was related to a higher risk of VOCE (16.2% vs. 7.6%; HR: 2.311; 95% CI 1.410-3.794; p = 0.0009), while no significant difference was noted according to baseline physiological pattern. In diffuse disease, the use of intracoronary imaging was associated with a lower incidence of long-term VOCE (5.1% vs 14.8%; HR: 0.313, 95% CI 0.167-0.614, p = 0.030). Suboptimal post-PCI physiology is observed more often in diffusely diseased arteries and it is associated with higher risk of VOCE at follow-up. The use of intravascular imaging might improve clinical outcomes in the setting of diffuse CAD.

Diagnostic Performance Of On-site Automatic Coronary Computed Tomography Angiography-derived Fractional Flow Reserve

Diagnostic Performance Of On-site Automatic Coronary Computed Tomography Angiography-derived Fractional Flow Reserve

Pre-PCI CT-FFR Predicts Target Vessel Failure After Stent Implantation.

To investigate the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) before percutaneous coronary intervention (PCI) to predict target vessel failure (TVF) after stent implantation. This retrospective study included 429 patients (429 vessels) who underwent PCI and stent implantation after CCTA within 3 months. All patients underwent coronary stent implantation between January 2012 and December 2019. A dedicated workstation (Syngo Via, Siemens) was used to analyze and measure the CT-FFR value. The cut-off values of pre-PCI CT-FFR for predicting TVF were defined as 0.80 and the value using the log-rank maximization method, respectively. The primary outcome was TVF, defined as a composite of cardiac death, target vessel myocardial infarction, and clinically driven target vessel revascularization (TVR), which was a secondary outcome. During a median 64.0 months follow-up, the cumulative incidence of TVF was 7.9% (34/429). The cutoff value of pre-PCI CT-FFR based on the log-rank maximization method was 0.74, which was the independent predictor for TVF [hazard ratio (HR): 2.61 (95% CI: 1.13, 6.02); P =0.024] and TVR [HR: 3.63 (95%CI: 1.25, 10.51); P =0.018]. Compared with the clinical risk factor model, pre-PCI CT-FFR significantly improved the reclassification ability for TVF [net reclassification improvement (NRI), 0.424, P <0.001; integrative discrimination index (IDI), 0.011, P =0.022]. Adding stent information to the prediction model resulted in an improvement in reclassification for the TVF (C statistics: 0.711, P =0.001; NRI: 0.494, P <0.001; IDI: 0.020, P =0.028). Pre-PCI CT-FFR ≤0.74 was an independent predictor for TVF or TVR, and integration of clinical, pre-PCI CT-FFR, and stent information models can provide a better risk stratification model in patients with stent implantation.

Quantitative flow ratio versus fractional flow reserve for Heart Team decision-making in multivessel disease: the randomised, multicentre DECISION QFR trial.

Vessel-level physiological data derived from pressure wire measurements are one of the important determinant factors in the optimal revascularisation strategy for patients with multivessel disease (MVD). However, these may result in complications and a prolonged procedure time. The feasibility of using the quantitative flow ratio (QFR), an angiography-derived fractional flow reserve (FFR), in Heart Team discussions to determine the optimal revascularisation strategy for patients with MVD was investigated. Two Heart Teams were randomly assigned either QFR- or FFR-based data of the included patients. They then discussed the optimal revascularisation mode (percutaneous coronary intervention [PCI] or coronary artery bypass grafting [CABG]) for each patient and made treatment recommendations. The primary endpoint of the trial was the level of agreement between the treatment recommendations of both teams as assessed using Cohen's kappa. The trial included 248 patients with MVD from 10 study sites. Cohen's kappa in the recommended revascularisation modes between the QFR and FFR approaches was 0.73 [95% confidence interval {CI} : 0.62-0.83]. As for the revascularisation planning, agreements in the target vessels for PCI and CABG were substantial for both revascularisation modes (Cohen's kappa=0.72 [95% CI: 0.66-0.78] and 0.72 [95% CI: 0.66-0.78], respectively). The team assigned to the QFR approach provided consistent recommended revascularisation modes even after being made aware of the FFR data (Cohen's kappa=0.95 [95% CI:0.90-1.00]). QFR provided feasible physiological data in Heart Team discussions to determine the optimal revascularisation strategy for MVD. The QFR and FFR approaches agreed substantially in terms of treatment recommendations.

Clinical Value of Single-Projection Angiography-Derived FFR in Noninfarct-Related Artery.

The Murray law-based quantitative flow ratio (μFR) is an emerging technique that requires only 1 projection of coronary angiography with similar accuracy to quantitative flow ratio (QFR). However, it has not been validated for the evaluation of noninfarct-related artery (non-IRA) in acute myocardial infarction (AMI) settings. Therefore, our study aimed to evaluate the diagnostic accuracy of μFR and the safety of deferring non-IRA lesions with μFR >0.80 in the setting of AMI. μFR and QFR were analyzed for non-IRA lesions of patients with AMI enrolled in the FRAME-AMI trial (Fractional Flow Reserve Versus Angiography-Guided Strategy for Management of Non-Infarction Related Artery Stenosis in Patients With Acute Myocardial Infarction), consisting of fractional flow reserve (FFR)-guided percutaneous coronary intervention and angiography-guided percutaneous coronary intervention groups. The diagnostic accuracy of μFR was compared with QFR and FFR. Patients were classified by the non-IRA μFR value of 0.80 as a cutoff value. The primary outcome was a vessel-oriented composite outcome, a composite of cardiac death, non-IRA-related myocardial infarction, and non-IRA-related repeat revascularization. μFR and QFR analyses were feasible in 443 patients (552 lesions). μFR showed acceptable correlation with FFR (R=0.777; P<0.001), comparable C-index with QFR to predict FFR ≤0.80 (μFR versus QFR: 0.926 versus 0.961, P=0.070), and shorter total analysis time (mean, 32.7 versus 186.9 s; P<0.001). Non-IRA with μFR >0.80 and deferred percutaneous coronary intervention had a significantly lower risk of vessel-oriented composite outcome than non-IRA with performed percutaneous coronary intervention (3.4% versus 10.5%; hazard ratio, 0.37 [95% CI, 0.14-0.99]; P=0.048). In patients with multivessel AMI, μFR of non-IRA showed acceptable diagnostic accuracy comparable to that of QFR to predict FFR ≤0.80. Deferred non-IRA with μFR >0.80 showed a lower risk of vessel-oriented composite outcome than revascularized non-IRA. URL: https://www.clinicaltrials.gov; Unique identifier: NCT02715518.

Complete revascularization based on angiography derived fractional flow reserve versus incomplete revascularization in patients with ST-segment elevation myocardial infarction.

Nearly half of ST-segment elevation myocardial infarction (STEMI) patients present with significant multivessel coronary artery disease, they are at high risk of subsequent adverse events. Whether complete revascularization guided by coronary angiography-derived fractional flow reserve (caFFR) further reduces such events risk is not fully investigated. In this study, 367 consecutive STEMI patients who underwent successful primary percutaneous coronary intervention (PCI) were enrolled. caFFR of all three coronary vessels were measured, including 367 culprit vessels and 703 non-culprit vessels. Complete revascularization was defined as post-PCI caFFR > 0.8 of all three coronary vessels. The primary endpoint was major adverse cardiovascular events (MACE, a composite of cardiovascular death, non-fatal recurrent myocardial infarction, ischemia-driven revascularization and non-fatal stroke/transient ischemic attacks) during follow-up. At a median follow-up of 3.8 years, MACE had occurred in 39 patients of the 220 (17.7%) in the complete revascularization group as compared with 49 patients of the 131 (37.4%) in the incomplete revascularization group (hazard ratio [HR] 1.9; 95% confidence interval [CI] 1.2-3.0; p = 0.005). The incomplete revascularization in culprit vessels evaluated by caFFR showed the highest risk for MACE occurrence. In STEMI patients with multivessel coronary artery disease, incomplete revascularization based on caFFR might contribute to identifying patients at high-risk.

Coronary Artery Tree Description and Lesion Evaluation (CatLet) score for functional evaluation of coronary stenosis: a comparison study with pressure wire fractional flow reserve.

Pressure wire fractional flow reserve (FFR) and its derivatives, such as quantitative flow ratio (QFR), computational pressure flow-derived FFR (caFFR), coronary angiography-derived FFR (FFRangio), and computed tomography-derived FFR (FFRCT), have been validated for identifying functionally significant stenosis and guiding revascularization strategy. The limitations of using these methods include the side effects of hyperemia-induced agents, additional costs, and vulnerability to microvascular resistance. FFR is related both to the degree of a stenotic coronary artery and to its subtended myocardial territory. Coronary Artery Tree Description and Lesion Evaluation (CatLet) score (also known as Hexu) is a product of the degree of a stenosis and the weighting of the affected coronary artery (myocardial territory). Hence, we hypothesized that the CatLet score could predict hemodynamically significant coronary stenosis. We retrospectively enrolled consecutive patients with stable coronary artery disease. They attended Sichuan Science City Hospital with at least one lesion of 50-90% diameter stenosis in a coronary artery of 2 mm or larger. FFR measurement was obtained during invasive coronary angiography. The CatLet score was obtained by multiplying a fixed value of 2.0 (for non-occlusive lesions) and the weight of the affected coronary artery. The primary endpoint was the CatLet score's diagnostic accuracy in identifying hemodynamically significant coronary stenosis, with a pressure wire FFR of ≤0.80 being used as reference. We analyzed the FFR and CatLet scores from 206 vessels in 175 patients with stable coronary disease and intermediate coronary lesions. The per-vessel analysis revealed an overall good correlation between the CatLet score and the FFR [r=-0.61; 95% confidence interval (95% CI): -0.69 to -0.52; P<0.01]. We also noted a significant CatLet score-FFR correlation for each of the left anterior descending artery (LAD), left circumflex (LCX), and right coronary artery (RCA). With a CatLet score ≥10 as a predictor of FFR ≤0.80, the overall diagnostic accuracy included a sensitivity of 78.80% (95% CI: 67.00-87.90%), a specificity of 85.00% (95% CI: 78.00-90.50%), a positive likelihood ratio of 5.25, a negative likelihood ratio of 0.25, and an area under the curve of 0.90 (95% CI: 0.85-0.94). Equivalent vessel-specific results were also achieved for each of the LAD, LCX, and RCA. The CatLet score, solely based on visual estimation of the results of coronary angiography, demonstrated good diagnostic performance with respect to myocardial ischemia. Its clinical values in guiding revascularization warrant further investigation.

Long-term prognostic implications of CT angiography-derived fractional flow reserve: Results from the DISCOVER-FLOW study

Background & ObjectivesThe long-term prognostic implications of CT angiography-derived fractional flow reserve (FFRCT) remains unclear. We aimed to explore the long-term outcomes of FFRCT in the first-in-human study of it. Materials & methodsA total of 156 vessels from 102 patients with stable coronary artery disease, who underwent coronary CT angiography (CCTA) and invasive FFR measurement, were followed. The primary endpoint was target vessel failure (TVF), including cardiovascular death, target vessel myocardial infarction, and target vessel revascularization. Outcome analysis with FFRCT was performed on a per-vessel basis using a marginal Cox proportional hazard model. ResultsDuring median 9.9 years of follow-up, TVF occurred in 20 (12.8%) vessels. FFRCT ​≤0.80 discriminated TVF (hazard ratio [HR] 2.61, 95% confidence interval [CI] 1.06, 6.45). Among 94 vessels with deferral of percutaneous coronary intervention (PCI), TVF risk was inversely correlated with FFRCT ​(HR 0.62 per 0.1 increase, 95% CI 0.44, 0.86), with the cumulative incidence of TVF being 2.6%, 15.2%, and 28.6% for vessels with FFRCT ​>0.90, 0.81–0.90, and ≤0.80, respectively (p-for-trend 0.005). Predictive value for clinical outcomes of FFRCT was similar to that of invasive FFR (c-index 0.79 vs 0.71, P ​= ​0.28). The estimated TVF risk was higher in the deferral of PCI group than the PCI group for vessels with FFRCT ≤0.81. ConclusionFFRCT showed improved long-term risk stratification and displayed a risk continuum similar to invasive FFR. Clinical trial registrationNCT01189331.

Diagnostic performance of angiography-derived fractional flow reserve and CT-derived fractional flow reserve: A systematic review and Bayesian network meta-analysis.

Accumulating evidence has demonstrated that fractional flow reserves (FFRs) derived from invasive coronary angiograms (CA-FFRs) and coronary computed tomography angiography-derived FFRs (CT-FFRs) are promising alternatives to wire-based FFRs. However, it remains unclear which method has better diagnostic performance. This systematic review and meta-analysis aimed to compare the diagnostic performances of the two approaches. The Cochrane Library, PubMed, Embase, Medline (Ovid), the Chinese China National Knowledge Infrastructure Database (CNKI), VIP, and WanFang Data databases were searched for relevant studies that included comparisons between CA-FFR and CT-FFR, from their respective database inceptions until January 1, 2023. Studies where both noninvasive FFR (including CA-FFR and CT-FFR) and invasive FFR (as a reference standard) were performed for the diagnosis of ischemic coronary artery disease and were designed as prospective, paired diagnostic studies, were pulled. The diagnostic test accuracy method and Bayesian hierarchical summary receiver operating characteristic (ROC) model for network meta-analysis (NMA) of diagnostic tests (HSROC-NMADT) were both used to perform a meta-analysis on the data. Twenty-six studies were included in this NMA. The results from both the diagnostic test accuracy and HSROC-NMADT methods revealed that the diagnostic accuracy of CA-FFR was higher than that of CT-FFR, in terms of sensitivity (Se; 0.86vs. 0.84), specificity (Sp; 0.90vs. 0.78), positive predictive value (PPV; 0.83vs. 0.70), and negative predictive value (NPV; 0.91vs. 0.89) for the detection of myocardial ischemia. A cumulative ranking curve analysis indicated that CA-FFR had a higher diagnostic accuracy than CT-FFR in the context of this study, with a higher area under the ROC curve (AUC; 0.94vs. 0.87). Although both of these two commonly used virtual FFR methods showed high levels of diagnostic accuracy, we demonstrated that CA-FFR had a better Se, Sp, PPV, NPV, and AUC than CT-FFR. However, this study provided only indirect comparisions; therefore, larger studies are warranted to directly compare the diagnostic performances of these two approaches.

Diagnostic Performance of Noninvasive Coronary Computed Tomography Angiography-Derived FFR for Coronary Lesion-Specific Ischemia Based on Deep Learning Analysis.

The noninvasive computed tomography angiography-derived fractional flow reserve (CT-FFR) can be used to diagnose coronary ischemia. With advancements in associated software, the diagnostic capability of CT-FFR may have evolved. This study evaluates the effectiveness of a novel deep learning-based software in predicting coronary ischemia through CT-FFR. In this prospective study, 138 subjects with suspected or confirmed coronary artery disease were assessed. Following indication of 30%-90% stenosis on coronary computed tomography (CT) angiography, participants underwent invasive coronary angiography and fractional flow reserve (FFR) measurement. The diagnostic performance of the CT-FFR was determined using the FFR as the reference standard. With a threshold of 0.80, the CT-FFR displayed an impressive diagnostic accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) of 97.1%, 96.2%, 97.7%, 0.98, 96.2%, and 97.7%, respectively. At a 0.75 threshold, the CT-FFR showed a diagnostic accuracy, sensitivity, specificity, AUC, PPV, and NPV of 84.1%, 78.8%, 85.7%, 0.95, 63.4%, and 92.8%, respectively. The Bland-Altman analysis revealed a direct correlation between the CT-FFR and FFR (p 0.001), without systematic differences (p = 0.085). The CT-FFR, empowered by novel deep learning software, demonstrates a strong correlation with the FFR, offering high clinical diagnostic accuracy for coronary ischemia. The results underline the potential of modern computational approaches in enhancing noninvasive coronary assessment.

Diagnostic Performance of On-Site Automatic Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve.

Fractional flow reserve (FFR) is an invasive standard method to identify ischemia-causing coronary artery disease (CAD). With the advancement of technology, FFR can be noninvasively computed from coronary computed tomography angiography (CCTA). Recently, a novel simpler method has been developed to calculate on-site CCTA-derived FFR (CT-FFR) with a commercially available workstation. A total of 319 CAD patients who underwent CCTA, invasive coronary angiography, and FFR measurement were included. The primary outcome was the accuracy of CT-FFR for defining myocardial ischemia evaluated with an invasive FFR as a reference. The presence of ischemia was defined as FFR ≤0.80. Anatomical obstructive stenosis was defined as diameter stenosis on CCTA ≥50%, and the diagnostic performance of CT-FFR and CCTA stenosis for ischemia was compared. Among participants (mean age 64.7±9.4 years, male 77.7%), mean FFR was 0.82±0.10, and 126 (39.5%) patients had an invasive FFR value of ≤0.80. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of CT-FFR were 80.6% (95% confidence interval [CI], 80.5-80.7%), 88.1% (95% CI, 82.4-93.7%), 75.6% (95% CI, 69.6-81.7%), 70.3% (95% CI, 63.1-77.4%), and 90.7% (95% CI, 86.2-95.2%), respectively. CT-FFR had higher diagnostic accuracy (80.6% vs. 59.1%, p<0.001) and discriminant ability (area under the curve from receiver operating characteristic curve 0.86 vs. 0.64, p<0.001), compared with anatomical obstructive stenosis on CCTA. This novel CT-FFR obtained from an on-site workstation demonstrated clinically acceptable diagnostic performance and provided better diagnostic accuracy and discriminant ability for identifying hemodynamically significant lesions than CCTA alone.

Head-to-head comparison of two angiography-derived fractional flow reserve techniques in patients with high-risk acute coronary syndrome: A multicenter prospective study

BackgroundFFRangio and QFR are angiography-based technologies that have been validated in patients with stable coronary artery disease. No head-to-head comparison to invasive fractional flow reserve (FFR) has been reported to date in patients with acute coronary syndromes (ACS). MethodsThis study is a subset of a larger prospective multicenter, single-arm study that involved patients diagnosed with high-risk ACS in whom 30–70% stenosis was evaluated by FFR. FFRangio and QFR – both calculated offline by 2 different and blinded operators – were calculated and compared to FFR. The two co-primary endpoints were the comparison of the Pearson correlation coefficient between FFRangio and QFR with FFR and the comparison of their inter-observer variability. ResultsAmong 134 high-risk ACS screened patients, 59 patients with 84 vessels underwent FFR measurements and were included in this study. The mean FFR value was 0.82 ± 0.40 with 32 (38%) being ≤0.80. The mean FFRangio was 0.82 ± 0.20 and the mean QFR was 0.82 ± 0.30, with 27 (32%) and 25 (29%) being ≤0.80, respectively. The Pearson correlation coefficient was significantly better for FFRangio compared to QFR, with R values of 0.76 and 0.61, respectively (p = 0.01). The inter-observer agreement was also significantly better for FFRangio compared to QFR (0.86 vs 0.79, p < 0.05). FFRangio had 91% sensitivity, 100% specificity, and 96.8% accuracy, while QFR exhibited 86.4% sensitivity, 98.4% specificity, and 93.7% accuracy. ConclusionIn patients with high-risk ACS, FFRangio and QFR demonstrated excellent diagnostic performance. FFRangio seems to have better correlation to invasive FFR compared to QFR but further larger validation studies are required.

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

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. We aimed to assess and compare the diagnostic performance of both NHPRs and coronary Angio-FFR against wire-based conventional FFR. 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. 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. 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. FFR,, stable ischemic disease and non-ST elevation acute coronary syndrome.

Rationale and design of a comparison of angiography-derived fractional flow reserve-guided and intravascular ultrasound-guided intervention strategy for clinical outcomes in patients with coronary artery disease: a randomised controlled trial (FLAVOUR II)

IntroductionPercutaneous coronary intervention (PCI) guided by coronary angiography-derived fractional flow reserve (FFR) or intravascular ultrasound (IVUS) has shown improved clinical outcomes compared with angiography-only-guided PCI. In patients with intermediate stenoses,...

A study on the first-order interaction of diagnostic performance of coronary CT angiography-derived fractional flow reserve

Objective: To investigate the effect of image quality, degree of stenosis, calcification, and their first-order interactions on diagnostic performance of coronary computed tomography (CT) angiography-derived fractional flow reserve (CT-FFR). Methods: This is a reanalysis of data from a multi-center retrospective cross-sectional study of CT-FFR in China. A total of 522 patients with suspected or known coronary heart disease [mean age: 61.6 (34.0-83.0) years, 71.8% (354/493) were male] from 11 medical centers including the General Hospital of Eastern Theater Command from May 2015 to October 2019 were enrolled. All patients underwent coronary CT angiography (CCTA), CT-FFR, and invasive FFR examination. Subjective image quality scores of target vessels were recorded on CCTA images, and stenosis was visually assessed at the lesion level. Calcification arc and calcification remodeling index (CRI) were recorded for each lesion. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were compared. Two-way analysis of variance was used to analyze the first-order interaction effects of image quality, degree of stenosis, and calcification. Results: A total of 493 patients with 629 lesions with invasive FFR as a reference were included in the study. The overall sensitivity, specificity, and accuracy of CT-FFR were 80.4%, 93.8%, and 88.6%, respectively. The specificity (95.0% vs. 87.3%, χ2=4.11, P=0.043); accuracy (90.1% vs. 81.9%, χ2=6.22, P=0.013); and NPV (89.7% vs. 80.9%, χ2=4.25, P=0.039) of the group with image quality ≥3 was higher than the group with image quality <3. The degree of stenosis affected the sensitivity, PPV, and NPV of CT-FFR and the calcification arc affected the specificity of CT-FFR (all P>0.05). The specificity (95.8% vs. 90.5%, χ2=4.23, P=0.040); accuracy (91.0% vs. 86.1%, χ2=4.01, P=0.045); and NPV (91.1% vs. 83.8%, χ2=5.10, P=0.024) of the group with CRI<1 were higher than that of the group with CRI≥1. In the subgroup of mild and severe stenosis, no calcification, and CRI<1, the accuracy of CT-FFR with image quality ≥3 points were higher than that with image quality <3 points. The accuracy of CT-FFR in the moderate stenosis group was mainly affected by CRI; the accuracy of CT-FFR in the group with CRI<1 was higher than that in the group with CRI≥1 (after Bonferroni correction, P values between groups were statistically significant). Conclusion: Subjective image quality, degree of stenosis, calcification of lesions, and their first-order interactions can all negatively affect the diagnostic performance of CT-FFR.

Quantitative flow ratio for non culprit lesions in ST elevation myocardial infarction and multi vessel disease - a direct comparison with fractional flow reserve

Abstract Objectives In acute myocardial infarction and multivessel disease (MVD), current guidelines recommend treatment of the culprit lesion followed by staged full revascularization of functionally relevant stenoses. We sought to analyze the hemodynamic relevance of intermediate stenoses using quantitative flow ratio (QFR, an angiography-derived fractional flow reserve [FFR]) in a population presenting with ST elevation myocardial infarction (STEMI) and MVD on index procedure. Methods We retrospectively analyzed 36 coronary lesions in 34 Patients presenting with STEMI and MVD from January 2017 to September 2022. 31 (86%) patients were male, mean age 63 ± 7, mean ejection fraction (EF %) 50 ± 8, arterial hypertension 30 (88%), diabetes 13 (38%). The non culprit lesion location was left anterior descending (LAD) in 24 (67%), circumflex artery (RCX) in 8 (22%) and right coronary artery (RCA) in 4 (11%). All patients received an invasive assessment of non culprit lesions using FFR within 3 months after STEMI. A QFR assessment of non culprit lesions in an acute invasive coronary angiography has been compared with FFR results in a staged procedure. Results QFR showed an excellent accuracy in ruling out functionally relevant stenosis with no false negative results (sensitivity 100% compared to FFR). Three coronary lesions (8,3%) were graded false positive (functionally relevant). Due to poor image acquisition, we were unable to perform QFR analysis in ten coronary lesions (28%). Overall specificity (56,5%) was lower than reported in previous trials. After excluding the lesions that could not be analyzed due to poor acquisition we achieved specificity of 81,2% (Fig. 1). Concusion: QFR showed an excellent correlation with FFR (Fig. 1). Performing QFR in STEMI Patients with MVD could reduce the need of repeated invasive coronary angiographies and optimize the clinical therapy management in this high-risk population. Further studies on diagnostic performance and outcomes are needed.Fig. 1: Correlation plot