Utility of FFRCT in Patients with Chest Pain.

PURPOSE OF REVIEW: The goal of this article is to review the data supporting the use of fractional flow reserve derived from coronary computed tomography angiography (FFRCT) in patients with chest pain. REVIEW FINDINGS: Numerous clinical trials have demonstrated that the diagnostic accuracy of coronary computed tomography angiography (CCTA) can be improved with the use of FFRCT, primarily due to its superior specificity when compared to CCTA alone. This promising development may help reduce the need for invasive angiography in patients presenting with chest pain. Furthermore, some studies have indicated that incorporating FFRCT into decision-making is safe, with an FFRCT value of ≥ 0.8 being associated with favorable outcomes. While FFRCT has been shown to be feasible in patients with acute chest pain, further large-scale studies are warranted to confirm its utility. The emergence of FFRCT as a tool for the management of patients with chest pain is promising. However, potential limitations require the interpretation of FFRCT in conjunction with clinical context.

Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia.

BACKGROUND: Machine-Learning Computed Tomography-Based Fractional Flow Reserve (CT-FFRML) is a novel tool for the assessment of hemodynamic relevance of coronary artery stenoses. We examined the diagnostic performance of CT-FFRML compared to stress perfusion cardiovascular magnetic resonance (CMR) and tested if there is an additional value of CT-FFRML over coronary computed tomography angiography (cCTA). METHODS: Our retrospective analysis included 269 vessels in 141 patients (mean age 67 ± 9 years, 78% males) who underwent clinically indicated cCTA and subsequent stress perfusion CMR within a period of 2 months. CT-FFRML values were calculated from standard cCTA. RESULTS: CT-FFRML revealed no hemodynamic significance in 79% of the patients having ≥ 50% stenosis in cCTA. Chi2 values for the statistical relationship between CT-FFRML and stress perfusion CMR was significant (p < 0.0001). CT-FFRML and cCTA (≥ 70% stenosis) provided a per patient sensitivity of 88% (95%CI 64-99%) and 59% (95%CI 33-82%); specificity of 90% (95%CI 84-95%) and 85% (95%CI 78-91%); positive predictive value of 56% (95%CI 42-69%) and 36% (95%CI 24-50%); negative predictive value of 98% (95%CI 94-100%) and 94% (95%CI 90-96%); accuracy of 90% (95%CI 84-94%) and 82% (95%CI 75-88%) when compared to stress perfusion CMR. The accuracy of cCTA (≥ 50% stenosis) was 19% (95%CI 13-27%). The AUCs were 0.89 for CT-FFRML and 0.74 for cCTA (≥ 70% stenosis) and therefore significantly different (p < 0.05). CONCLUSION: CT-FFRML compared to stress perfusion CMR as the reference standard shows high diagnostic power in the identification of patients with hemodynamically significant coronary artery stenosis. This could support the role of cCTA as gatekeeper for further downstream testing and may reduce the number of patients undergoing unnecessary invasive workup.

Valuable Predictors for Non-measurability of Fractional Flow Reserve Derived From Coronary Computed Tomography Angiography.

Background The fractional flow reserve (FFR) derived from coronary computed tomography (CT) angiography (FFRCT) is a variable tool for coronary disease diagnosis that non-invasively provides the value of FFR. It can add physiological information to coronary CT angiography (CCTA) and reduce unnecessary invasive coronary angiography (CAG). However, it cannot be analyzed in some cases, which is also called "non-measurability." While FFRCT has become globally widespread, the current data on non-measurability are lacking. This study aimed to determine the rate of non-measurability and identify predictors thereof in routine clinical settings to explore potential approaches to reduce the non-measurability rate. Methods and results This retrospective observational single-center study included consecutive patients who underwent FFRCTanalysis in Japan. The mean age of the overall population was 71.3 ± 10.6, and an FFRCTof ≤0.8 was seen in 47.6% of patients with a measurable FFRCT. Of the 307 enrolled patients, FFRCT analysis was not feasible in 21 cases (6.8%). Heart rate (HR) at a CT scan and coronary calcium scores (CCS) were significantly higher in patients with non-measurability than those in patients whose FFRCT was appropriately analyzed (HR: 69.6±8.9 bpm vs. 61.0±11.1 bpm; p < 0.01; CCS; 931.2 (290.8, 1451.3) vs. 322.9 (100.7, 850.0); p < 0.01). Multiple logistic regression showed that HR was an independent predictor for non-measurability (odds ratio: 1.05; 95% confidential interval: 1.02, 1.09; p < 0.01)). Based on the receiver operating characteristic curve analysis, the optimal cut-off value of HR and CCS was 63 bpm (specificity: 67.1%; sensitivity: 76.2%) and 729.2 (specificity: 71.3%; sensitivity: 66.7%). In addition, the combination of two features (HR > 63 bpm and CCS > 729.2) showed a high negative predictive value (99.3%) for FFRCT non-measurability. Conclusions In this study, the rate of FFRCTnon-measurability was 6.8%. Higher HR at a CT scan and CCS were significantly associated with non-measurability, and in cases with both HR and CCS below a specified threshold, the likelihood of ruling out non-measurability could be significantly high. Our findings suggest that reducing the HR to ideally under 63 bpm at the time of the CT scan significantly ensures feasibility. Further study on large-scale cohorts is warranted.

Comparison of Machine Learning Computed Tomography-Based Fractional Flow Reserve and Coronary CT Angiography-Derived Plaque Characteristics with Invasive Resting Full-Cycle Ratio.

BACKGROUND: The aim is to compare the machine learning-based coronary-computed tomography fractional flow reserve (CT-FFRML) and coronary-computed tomographic morphological plaque characteristics with the resting full-cycle ratio (RFRTM) as a novel invasive resting pressure-wire index for detecting hemodynamically significant coronary artery stenosis. METHODS: In our single center study, patients with coronary artery disease (CAD) who had a clinically indicated coronary computed tomography angiography (cCTA) and subsequent invasive coronary angiography (ICA) with pressure wire-measurement were included. On-site prototype CT-FFRML software and on-site CT-plaque software were used to calculate the hemodynamic relevance of coronary stenosis. RESULTS: We enrolled 33 patients (70% male, mean age 68 ± 12 years). On a per-lesion basis, the area under the receiver operating characteristic curve (AUC) of CT-FFRML (0.90) was higher than the AUCs of the morphological plaque characteristics length/minimal luminal diameter4 (LL/MLD4; 0.80), minimal luminal diameter (MLD; 0.77), remodeling index (RI; 0.76), degree of luminal diameter stenosis (0.75), and minimal luminal area (MLA; 0.75). CONCLUSION: CT-FFRML and morphological plaque characteristics show a significant correlation to detected hemodynamically significant coronary stenosis. Whole CT-FFRML had the best discriminatory power, using RFRTM as the reference standard.

Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis.

BACKGROUND: Fractional flow reserve based on coronary CT angiography (CT-FFR) is gaining importance for non-invasive hemodynamic assessment of coronary artery disease (CAD). We evaluated the on-site CT-FFR with a machine learning algorithm (CT-FFRML) for the detection of hemodynamically significant coronary artery stenosis in comparison to the invasive reference standard of instantaneous wave free ratio (iFR®). METHODS: This study evaluated patients with CAD who had a clinically indicated coronary computed tomography angiography (cCTA) and underwent invasive coronary angiography (ICA) with iFR®-measurements. Standard cCTA studies were acquired with third-generation dual-source computed tomography and analyzed with on-site prototype CT-FFRML software. RESULTS: We enrolled 40 patients (73% males, mean age 67 ± 12 years) who had iFR®-measurement and CT-FFRML calculation. The mean calculation time of CT-FFRML values was 11 ± 2 min. The CT-FFRML algorithm showed, on per-patient and per-lesion level, respectively, a sensitivity of 92% (95% CI 64-99%) and 87% (95% CI 59-98%), a specificity of 96% (95% CI 81-99%) and 95% (95% CI 84-99%), a positive predictive value of 92% (95% CI 64-99%), and 87% (95% CI 59-98%), and a negative predictive value of 96% (95% CI 81-99%) and 95% (95% CI 84-99%). The area under the receiver operating characteristic curve for CT-FFRML on per-lesion level was 0.97 (95% CI 0.91-1.00). Per lesion, the Pearson's correlation between the CT-FFRML and iFR® showed a strong correlation of r = 0.82 (p < 0.0001; 95% CI 0.715-0.920). CONCLUSION: On-site CT-FFRML correlated well with the invasive reference standard of iFR® and allowed for the non-invasive detection of hemodynamically significant coronary stenosis.