Tomography Angiography-derived Fractional Flow Reserve Research Articles

Prognostic Significance of Computed Tomography-Derived Fractional Flow Reserve for Long-Term Outcomes in Individuals With Coronary Artery Disease.

Data on the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) for long-term outcomes are limited. A retrospective pooled analysis of individual patient data was performed. Deep-learning-based CT-FFR was calculated. All patients enrolled were followed-up for at least 5 years. The primary outcome was major adverse cardiovascular events. The secondary outcome was death or nonfatal myocardial infarction. Predictive abilities for outcomes were compared among 3 models (model 1, constructed using clinical variables; model 2, model 1+coronary computed tomography angiography-derived anatomical parameters; and model 3, model 2+CT-FFR). A total of 2566 patients (median age, 60 [53-65] years; 56.0% men) with coronary artery disease were included. During a median follow-up time of 2197 (2127-2386) days, 237 patients (9.2%) experienced major adverse cardiovascular events. In multivariable-adjusted Cox models, CT-FFR≤0.80 (hazard ratio [HR], 5.05 [95% CI, 3.64-7.01]; P<0.001) exhibited robust predictive value. The discriminant ability was higher in model 2 than in model 1 (Harrell's C-statistics, 0.79 versus 0.64; P<0.001) and was further promoted by adding CT-FFR to model 3 (Harrell's C-statistics, 0.83 versus 0.79; P<0.001). Net reclassification improvement was 0.264 (P<0.001) for model 2 beyond model 1. Of note, compared with model 2, model 3 also exhibited improvement (net reclassification improvement=0.085; P=0.001). As for predicting death or nonfatal myocardial infarction, only incorporating CT-FFR into model 3 showed improved reclassification (net reclassification improvement=0.131; P=0.021). CT-FFR provides strong and incremental prognostic information for predicting long-term outcomes. The combined models incorporating CT-FFR exhibit modest improvement of prediction abilities, which may aid in risk stratification and decision-making.

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.

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.

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.

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.

Clinical Outcomes Based on Coronary Computed Tomography-Derived Fractional Flow Reserve and Plaque Characterization

BackgroundCoronary computed tomography angiography (CTA) followed by computed tomography angiography–derived fractional flow reserve (FFRCT) is now commonly used for the management of chronic coronary syndrome (CCS). CTA-verified high-risk plaque (HRP) characteristics have also been reported to be associated with a greater likelihood of adverse cardiac events but have not been used for management decisions. ObjectivesThe aim of this study was to evaluate clinical outcomes based on a combination of point-of-care computed tomography angiography–derived fractional flow reserve (POC-FFRCT) and the presence of HRP in CCS patients initially treated medically or with revascularization based on invasive coronary angiography findings. MethodsCTA was performed as the initial test in 5,483 patients presenting with CCS between September 2015 and December 2020 followed by invasive coronary angiography and revascularization as necessary. POC-FFRCT assessment and HRP characterization were obtained subsequently in 745 consecutive patients. We investigated how HRP and POC-FFRCT, which were not available during the original clinical decision making, correlated with the endpoint defined as a composite of cardiac death, acute coronary syndrome, and a need for unplanned revascularization. ResultsCardiac events occurred in 20 patients (2.7%) during a median follow-up of 744 days. The event rate was significantly higher in patients with POC-FFRCT <0.80 compared with POC-FFRCT ≥0.8 (5.4 vs 0.5 per 100 vessel years; log-rank P < 0.0001) and in patients with HRP compared to those without HRP (3.6 vs 0.8 per 100 vessel years; log-rank P = 0.0001). POC-FFRCT <0.80 and the presence of HRP were the independent predictors of cardiac events (HR: 16.67; 95% CI: 2.63-105.39; P = 0.002) compared with POC-FFRCT ≥0.8 and absent HRP. For the vessels with POC-FFRCT <0.80 and HRP, a significantly higher rate of adverse events was observed in patients who did not undergo revascularization compared with those revascularized (16.4 vs 1.4 per 100 vessel years; log-rank P = 0.006). ConclusionsPOC-FFRCT <0.80 and the presence of HRP were the independent predictors of cardiac events, and revascularization of HRP lesions with abnormal POC-FFRCT was associated with a lower event rate.

Retracted: Research on individualized distribution approach of coronary resting blood flow for noninvasive calculation of fractional flow reserve

Background and ObjectivesThe distribution of coronary resting blood flow is critical for accurately calculating the computed tomography (CT) angiography-derived fractional flow reserve (FFRCT). However, the diagnostic accuracy of FFRCT calculated by the fixed exponents between two risk factors and coronary resting blood flow, including myocardial mass and diameter of the coronary artery branch, was insufficient compared with invasive fractional flow reserve (FFR). In this study, we proposed the individualized distribution of coronary resting blood flow based on coronary ultrasound blood flow measurement, to improve the diagnostic accuracy of FFRCT calculation. MethodsFive risk factors and an unknown coefficient K were integrated to calculate the individualized distribution of coronary resting blood flow. The K value was fit using the least square method based on coronary ultrasound blood flow measurement results of 30 volunteers. We developed a novel approach for calculating the individualized distribution of coronary resting blood flow and applied it to calculate FFRCT (FFRCTI). Then, we tested the performance of the individualized distribution approach by comparing it with the approach proposed by Taylor based on coronary ultrasound blood flow measurement results of 13 volunteers. Finally, we tested the diagnostic accuracy of FFRCT calculated by two approaches in invasive FFR of 121 vessels with coronary stenosis. ResultsWe identified five risk factors and 6.83×10−5 for K value, including cardiac output, mean arterial pressure, myocardial mass, coronary artery volume, and diameter of the coronary artery branch, to calculate the individualized distribution of coronary resting blood flow. The mean square error of the individualized distribution approach (0.0088) was significantly less than that of the approach proposed by Taylor (0.0799). The diagnostic accuracy of FFRCTI calculated by the individualized distribution approach (91.74%) was higher than that of the approach proposed by Taylor (FFRCTT) (82.64%). ConclusionsThe individualized distribution approach of coronary resting blood flow can significantly improve the diagnostic accuracy of FFRCT calculation compared with invasive FFR, and support its wide clinical application for diagnosing myocardial ischemia caused by coronary stenosis.

Clinical value of perivascular fat attenuation index and computed tomography derived fractional flow reserve in identification of culprit lesion of subsequent acute coronary syndrome.

To explore the potential of perivascular fat attenuation index (FAI) and coronary computed tomography angiography (CCTA) derived fractional flow reserve (CT-FFR) in the identification of culprit lesion leading to subsequent acute coronary syndrome (ACS). Thirty patients with documented ACS event who underwent invasive coronary angiography (ICA) from February 2019 to February 2021 and had received CCTA in the previous 6 months were collected retrospectively. 40 patients with stable angina pectoris (SAP) were matched as control group according to sex, age and risk factors. The study population has a mean age of 59.3 ± 12.3 years, with a male prevalence of 81.4%. The plaque characteristics, perivascular fat attenuation index (FAI), and coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) of 32 culprit lesions and 30 non-culprit lesions in ACS patients and 40 highest-grade stenosis lesions in SAP patients were statistically analyzed. FAI around culprit lesions was increased significantly (-72.4 ± 3.2 HU vs. -79.0 ± 7.7 HU, vs. -80.4 ± 7.0HU, all p < 0.001) and CT-FFR was decreased for culprit lesions of ACS patients [0.7(0.1) vs. 0.8(0.1), vs.0.8(0.1), p < 0.001] compared to other lesions. According to multivariate analysis, diameter stenosis (DS), FAI, and CT-FFR were significant predictors for identification of the culprit lesion. The integration model of DS, FAI, and CT-FFR showed the significantly highest area under the curve (AUC) of 0.917, compared with other single predictors (all p < 0.05). This study proposes a novel integrated prediction model of DS, FAI, and CT-FFR that enhances the diagnostic accuracy of traditional CCTA for identifying culprit lesions that trigger ACS. Furthermore, this model also provides improved risk stratification for patients and offers valuable insights for predicting future cardiovascular events.

Effect of 320-Row Computed Tomography Acquisition Technology on Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve Based on Machine Learning: Systolic and Diastolic Scan Acquisition.

The aim of the study is to investigate the performance of coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) in the same patient evaluated by different systolic and diastolic scans, aiming to explore whether 320-slice CT scanning acquisition protocol has an impact on CT-FFR value. One hundred forty-six patients with suspected coronary artery stenosis who underwent CCTA examination were included into the study. The prospective electrocardiogram gated trigger sequence scan was performed and electrocardiogram editors selected 2 optimal phases of systolic phase (preset collection trigger at 25% of R-R interval) and diastolic phase (preset collection trigger at 75% of R-R interval) for reconstruction. The lowest CT-FFR value (the CT-FFR value at the distal end of each vessel) and the lesion CT-FFR value (at 2 cm distal to the stenosis) after coronary artery stenosis were calculated for each vessel. The difference of CT-FFR values between the 2 scanning techniques was compared using paired Wilcoxon signed-rank test. Pearson correlation value and Bland-Altman were performed to evaluate the consistency of CT-FFR values. A total of 366 coronary arteries from the remaining 122 patients were analyzed. There was no significant difference regarding the lowest CT-FFR values between systole phase and diastole phase across all vessels. In addition, there was no significant difference in the lesion CT-FFR value after coronary artery stenosis between systole phase and diastole phase across all vessels. The CT-FFR value between the 2 reconstruction techniques had excellent correlation and minimal bias in all groups. The correlation coefficient of the lesion CT-FFR values for left anterior descending branch, left circumflex branch, and right coronary artery were 0.86, 0.84, and 0.76, respectively. Coronary computed tomography angiography-derived fractional flow reserve based on artificial intelligence deep learning neural network has stable performance, is not affected by the acquisition phase technology of 320-slice CT scan, and has high consistency with the evaluation of hemodynamics after coronary artery stenosis.

Influence of Left Ventricular Diastolic Dysfunction on the Diagnostic Performance of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve

Our study aimed to demonstrate the influence of left ventricular (LV) diastolic dysfunction on the diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR). One hundred vessels from 90 patients were retrospectively analyzed. All patients underwent echocardiography, coronary computed tomography angiography (CCTA), CT-FFR, invasive coronary angiography (ICA), and fractional flow reserve (FFR). The study population was divided into normal and dysfunction groups according to the LV diastolic function, and the diagnostic performance in both groups was assessed. There was a good correlation between CT-FFR and FFR (R = 0.768 p < 0.001) on a per-vessel basis. The sensitivity, specificity, and accuracy were 82.3%, 81.8%, and 82%, respectively. The sensitivity, specificity, and accuracy were 84.6%, 88.5%, and 87.2% in the normal group and 81%, 77.5%, and 78.7% in the dysfunction group, respectively. CT-FFR showed no statistically significant difference in the AUC in the normal group vs. the dysfunction group (AUC: 0.920 [95% CI 0.787-0.983] vs. 0.871 [95% CI 0.761-0.943], Z = 0.772 p = 0.440). However, there was still a good correlation between CT-FFR and FFR in the normal group (R = 0.767, p < 0.001) and dysfunction group (R = 0.767 p < 0.001). LV diastolic dysfunction had no effect on the diagnostic accuracy of CT-FFR. CT-FFR has good diagnostic performance in both LV diastolic dysfunction and the normal group and can be used as an effective tool for finding lesion-specific ischemia while screening for arterial disease in patients.

Impact of coronary computed tomography angiography-derived fractional flow reserve based on deep learning on clinical management.

To examine the value of coronary computed tomography angiography (CCTA)-derived fractional flow reserve based on deep learning (DL-FFRCT) on clinical practice and analyze the limitations of the application of DL-FFRCT. This is an observational, retrospective, single-center study. Patients with suspected coronary artery disease (CAD) were enrolled. The patients underwent invasive coronary angiography (ICA) examination within 1 months after CCTA examination. And quantitative coronary angiography (QCA) was performed to evaluate the area stenosis rate. The CCTA data of these patients were retrospectively analyzed to calculate the FFRCT value. A total of 485 lesions of coronary arteries in 229 patients were included in the analysis. Of the lesions, 275 (56.7%) were ICA-positive, and 210 (43.3%) were FFRCT-positive. The discordance rate of the risk stratification of FFRCT for ICA-positive lesions was 33.1% (91) and that for ICA-negative lesions was 12.4% (26). 14.6% (7/48) patients with mild to moderate coronary stenosis in ICA have functional ischemia according to FFRCT positive indications. In addition, hemodynamic analysis of severely calcified, occluded, or small (< 2 mm in diameter) coronary arteries by DL-FFRCT is not so reliable. This study revealed that most patients with ICA negative did not require further invasive FFR. Besides, some patients with mild to moderate coronary stenosis in ICA may also have functional ischemia. However, for severely calcified, occluded, or small coronary arteries, treatment strategy should be selected based on ICA in combination with clinical practice.

Synergistic value of fractional flow reserve and low‑density non‑calcified plaque based on coronary computed tomography angiography for the identification of lesion‑specific ischemia.

Increasing evidence has suggested that plaque characteristics are closely associated with ischemia, and coronary computed tomography (CT) angiography-derived fractional flow reserve (FFRCT) based on deep machine learning algorithms has also been used to identify lesion-specific ischemia. Therefore, the aim of the present study was to explore the predictive ability of plaque characteristics in combination with deep learning-based FFRCT for lesion-specific ischemia. To meet this end, invasive FFR was used as a reference standard, with the joint aims of the early prediction of ischemic lesions and guiding clinical treatment. In the present study, the plaque characteristics, including non-calcified plaque (NCP), low-density NCP (LD-NCP), plaque length, total plaque volume (TPV), remodeling index, calcified plaque, fibrous plaque and plaque burden, were obtained using a semi-automated program. The FFRCT values were derived based on a deep machine learning algorithm. On the basis of the data obtained, differences among the values between the atopic ischemia and the non-significant lesions groups were analyzed to further determine the predictive value of independent predictors for atopic ischemia. Of the plaque features, FFRCT, LD-NCP, NCP, TPV and plaque length differed significantly when comparing between the lesion-specific ischemia and no hemodynamic abnormality groups, and LD-NCP and FFRCT were both independent predictors for ischemia. Additionally, FFRCT combined with LD-NCP showed a greater ability at discriminating ischemia compared with FFRCT or LD-NCP alone. Taken together, the findings of the present study suggest that the combination of FFRCT and LD-NCP has a synergistic effect in terms of predicting ischemia, thereby facilitating the identification of specific ischemia in patients with coronary artery disease.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology.

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in patients before liver transplantation using CT-FFR machine learning algorithm

ObjectivesLiver transplantation (LT) is associated with high stress on the cardiovascular system. Ruling out coronary artery disease (CAD) is an important part of evaluation for LT. The aim of our study was to assess whether CT-derived fractional flow reserve (CT-FFR) allows for differentiation of hemodynamically significant and non-significant coronary stenosis in patients evaluated for LT.MethodsIn total, 201 patients undergoing LT evaluation were included in the study. The patients received coronary computed tomography angiography (CCTA) to rule out CAD and invasive coronary angiography (ICA) to further evaluate coronary lesions found in CCTA if a significant (≥ 50 % on CCTA) stenosis was suspected. CT-FFR was computed from CCTA datasets using a machine learning–based algorithm and compared to ICA as a standard of reference. Coronary lesions with CT-FFR ≤ 0.80 were defined as hemodynamically significant.ResultsIn 127 of 201 patients (63%), an obstructive CAD was ruled out by CCTA. In the remaining 74 patients (37%), at least one significant stenosis was suspected in CCTA. Compared to ICA, sensitivity, specificity, PPV, and NPV of the CT-FFR measurements were 71% (49–92%), 90% (82–98%), 67% (45–88%), and 91% (84–99%), respectively. The diagnostic accuracy was 85% (85–86%). In 69% of cases (52 of 75 lesions), additional analysis by CT-FFR correctly excluded the hemodynamic significance of the stenosis.ConclusionsMachine learning–based CT-FFR seems to be a very promising noninvasive approach for exclusion of hemodynamic significant coronary stenoses in patients undergoing evaluation for LT and could help to reduce the rate of invasive coronary angiography in this high-risk population.Key Points• Machine learning–based computed tomography-derived fractional flow reserve (CT-FFR) seems to be a very promising noninvasive approach for exclusion of hemodynamic significance of coronary stenoses in patients undergoing evaluation for liver transplantation and could help to reduce the rate of invasive coronary angiography in this high-risk population.

Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

To evaluate feasibility and diagnostic performance of coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) for detection of significant coronary artery disease (CAD) and decision-making in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR) to potentially avoid additional pre-TAVR invasive coronary angiography (ICA). Consecutive patients with severe AS (n = 95, 78.6 ± 8.8 years, 53% female) undergoing pre-procedural TAVR-CT followed by ICA with quantitative coronary angiography were retrospectively analyzed. CCTA datasets were evaluated using CAD Reporting and Data System (CAD-RADS) classification. CT-FFR measurements were computed using an on-site machine-learning algorithm. A combined algorithm was developed for decision-making to determine if ICA is needed based on pre-TAVR CCTA: [1] all patients with CAD-RADS ≥ 4 are referred for ICA; [2] patients with CAD-RADS 2 and 3 are evaluated utilizing CT-FFR and sent to ICA if CT-FFR ≤ 0.80; [3] patients with CAD-RADS < 2 or CAD-RADS 2-3 and normal CT-FFR are not referred for ICA. Twelve patients (13%) had significant CAD (≥ 70% stenosis) on ICA and were treated with PCI. Twenty-eight patients (30%) showed CT-FFR ≤ 0.80 and 24 (86%) of those were reported to have a maximum stenosis ≥ 50% during ICA. Using the proposed algorithm, significant CAD could be identified with a sensitivity, specificity, and positive and negative predictive value of 100%, 78%, 40%, and 100%, respectively, potentially decreasing the number of necessary ICAs by 65 (68%). Combination of CT-FFR and CAD-RADS is able to identify significant CAD pre-TAVR and bears potential to significantly reduce the number of needed ICAs. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) using machine learning together with the CAD Reporting and Data System (CAD-RADS) classification safely identifies significant coronary artery disease based on quantitative coronary angiography in patients prior to transcatheter aortic valve replacement. • The combination of CT-FFR and CAD-RADS enables decision-making and bears the potential to significantly reduce the number of needed invasive coronary angiographies.

Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve

BackgroundFractional flow reserve (FFR) is a widely used gold standard to evaluate ischemia-causing lesions. A new method of non-invasive approach, termed as AccuFFRct, for calculating FFR based on coronary computed tomography angiography (CCTA) and computational fluid dynamics (CFD) has been proposed. However, its diagnostic accuracy has not been validated.ObjectivesThis study sought to present a novel approach for non-invasive computation of FFR and evaluate its diagnostic performance in patients with coronary stenosis.MethodsA total of 54 consecutive patients with 78 vessels from a single center who underwent CCTA and invasive FFR measurement were retrospectively analyzed. The CT-derived FFR values were computed using a novel CFD-based model (AccuFFRct, ArteryFlow Technology Co., Ltd., Hangzhou, China). Diagnostic performance of AccuFFRct and CCTA in detecting hemodynamically significant coronary artery disease (CAD) was evaluated using the invasive FFR as a reference standard.ResultsDiagnostic accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for AccuFFRct in detecting FFR ≤ 0.8 on per-patient basis were 90.7, 89.5, 91.4, 85.0 and 94.1%, respectively, while those of CCTA were 38.9, 100.0, 5.71, 36.5 and 100.0%, respectively. The correlation between AccuFFRct and FFR was good (r = 0.76 and r = 0.65 on per-patient and per-vessel basis, respectively, both p < 0.0001). Area under the curve (AUC) values of AccuFFRct for identifying ischemia per-patient and per-vessel basis were 0.945 and 0.925, respectively. There was much higher accuracy, specificity and AUC for AccuFFRct compared with CCTA.ConclusionsAccuFFRct computed from CCTA images alone demonstrated high diagnostic performance for detecting lesion-specific ischemia, it showed superior diagnostic power than CCTA and eliminated the risk of invasive tests, which could be an accurate and time-efficient computational tool for diagnosing ischemia and assisting clinical decision-making.

Relationship of Stress Test Findings to Anatomic or Functional Extent of Coronary Artery Disease Assessed by Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve.

Background In the United States, functional stress testing is the primary imaging modality for patients with stable symptoms suspected to represent coronary artery disease (CAD). Coronary computed tomography angiography (CTA) is excellent at identifying anatomic coronary artery disease (CAD). The application of computational fluid dynamics to coronary CTA allows fractional flow reserve (FFR) to be calculated noninvasively (FFRCT). The relationship of noninvasive stress testing to coronary CTA and FFRCT in real-world clinical practice has not been studied. Methods We evaluated 206 consecutive patients at Loyola University Chicago with suspected CAD who underwent noninvasive stress testing followed by coronary CTA and FFRCT when indicated. Patients were categorized by stress test results (positive, negative, indeterminate, and equivocal). Duke treadmill score (DTS), METS, exercise duration, and chest pain with exercise were analyzed. Lesions ≥ 50%stenosis were considered positive by coronary CTA. FFRCT < 0.80 was considered diagnostic of ischemia. Results Two hundred and six patients had paired noninvasive stress test and coronary CTA/FFRCT results. The median time from stress test to coronary CTA was 49 days. Average patient age was 60.3 years, and 42% were male. Of the 206 stress tests, 75% were exercise (70% echocardiographic, 26% nuclear, and 4% EKG). There were no associations of stress test results with CAD > 50% or FFRCT < 0.80 (p = 0.927 and p = 0.910, respectively). Of those with a positive stress test, only 30% (3/10) had CAD > 50% and only 50% (5/10) had FFRCT < 0.80. Chest pain with exercise did not correlate with CAD > 50% or FFRCT < 0.80 (p = 0.66 and p = 0.12, respectively). There were no significant correlations between METS, DTS, or exercise duration and FFRCT (r = 0.093, p = 0.274; r = 0.012, p = 0.883; and r = 0.034, p = 0.680; respectively). Conclusion Noninvasive stress testing, functional capacity, chest pain with exercise, and DTS are not associated with anatomic or functional CAD using a diagnostic strategy of coronary CTA and FFRCT.

Real world utilization of CT derived fractional flow reserve in stable angina from contemporary practice: impact on downstream utilization of invasive coronary angiogram and clinical decision making

Abstract Background/Introduction - Fractional flow reserve (FFR), a pressure wire-based index used during coronary angiography to assess the severity of potential coronary stenosis, is considered as the reference standard for evaluating the severity of stenosis in coronary artery disease (CAD). Recently, computed tomography angiography-derived fractional flow reserve (FFRct) has been recommended for evaluating functional severity of stenoses as it improves diagnostic accuracy and reduces the need for invasive coronary angiography. Purpose To determine whether non-invasive FFRct predicts severity of coronary artery disease and whether its addition improves efficiency of proceeding to revascularisation and invasive coronary angiography (ICA) compared to coronary computed tomography angiography (CTA) without FFR. Methods This observational retrospective single center study included two cohorts of patients who presented in a District General Hospital in UK. The first group consisted of all patients who underwent coronary CTA for chest pain from January 2013 to December 2014. The second cohort consisted of all patients who proceeded to have measurement of FFRct from April 2018 to June 2019 after routine coronary CTA for chest pain. The two groups showed similar demographics. FFRct was analysed using the software HeartFlow. We determined the agreement of FFRct (positive if &amp;lt;0.80) with stenosis on CTA and ICA (positive if &amp;gt;50% left main or &amp;gt;70% other coronary artery) and whether it correlated with need for revascularisation. We also assessed if adding FFRct &amp;lt;0.80 improved efficiency of referral to ICA, defined as decreased diagnosis of mild or moderate stenosis (&amp;lt;70%) and higher yield of severe disease (&amp;gt;70%). The two cohorts were compared to determine the above specific end-points. Results In the first cohort, data was collected for 915 patients. 240 (26.2%) of these patients proceeded to ICA, which showed severe disease in 31 (3.3%) patients needing revascularisation. In the second cohort of patients, 824 patients underwent coronary CTA and 201 (24.4%) proceeded to have FFRct measurements. 99 (49%) of these patients had a negative FFR and 65 (32%) patients had a positive result (&amp;lt;0.80). There was agreement between FFRct and invasive coronary angiography/stress echo in 44 (77%) patients, with regards to severity /revascularisation. The need for ICA was significantly reduced if coronary CTA and FFRct were both done (240/915; 26.2% vs 54/824; 6.5%: p value &amp;lt;0.00001). Conclusion Reserving ICA for patients with a positive FFRct (&amp;lt;0.80) could reduce the number of ICA after coronary CTA and augment the number of ICA leading to revascularisation. Use of FFRct as a gatekeeper to ICA will improve appropriate selection of patients referred and this in-turn will reduce the burden of complications associated with invasive procedures, reduce costs and ensure better utilization of Cath-lab resources. Funding Acknowledgement Type of funding source: None