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Published on: August 25, 2023
Noninvasive CT-Derived FFR Based on Structural and Fluid Analysis: A Comparison With Invasive FFR for Detection of
Brian S Ko1, James D Cameron1, Ravi K Munnur1
1Monash Cardiovascular Research Centre, MonashHEART, Department of Medicine, Monash Medical Centre, Monash Health, and Monash University, Melbourne, Victoria, Australia.
Objectives:
This study describes the feasibility and accuracy of a novel computed tomography (CT) fractional flow reserve (FFR) technique based on alternative boundary conditions.
Background:
Techniques used to compute FFR based on images acquired from coronary computed tomography angiography (CTA) are described. Boundary conditions were typically determined by allometric scaling laws and assumptions regarding microvascular resistance. Alternatively, boundary conditions can be derived from the structural deformation of coronary lumen and aorta, although its accuracy remains unknown.
Methods:
Forty-two patients (78 vessels) in a single institution prospectively underwent 320-detector coronary CTA and FFR. Deformation of coronary cross-sectional lumen and aorta, computed from coronary CTA images acquired over diastole, was used to determine the boundary conditions based on hierarchical Bayes modeling. CT-FFR was derived using a reduced order model performed using a standard desktop computer and dedicated software. First, 12 patients (20 vessels) formed the derivation cohort to determine optimal CT-FFR threshold with which to detect functional stenosis, defined as FFR of ≤0.8, which was validated in the subsequent 30 patients (58 vessels).
Results:
Derivation cohort results demonstrated optimal threshold for CT-FFR was 0.8 with 67% sensitivity and 91% specificity. In the validation cohort, CT-FFR was successfully computed in 56 of 58 vessels (97%). Compared with coronary CTA, CT-FFR at ≤0.8 demonstrated a higher specificity (87% vs. 74%, respectively) and positive predictive value (74% vs. 60%, respectively), with comparable sensitivity (78% vs. 79%, respectively), negative predictive value (89% vs. 88%, respectively), and accuracy (area under the curve: 0.88 vs. 0.77, respectively; p = 0.22). Based on Bland-Altman analysis, mean intraobserver and interobserver variability values for CT-FFR were, respectively, -0.02 ± 0.05 (95% limits of agreement: -0.12 to 0.08) and 0.03 ± 0.06 (95% limits: 0.07 to 0.19). Mean time per patient for CT-FFR analysis was 27.07 ± 7.54 min.
Conclusions:
CT-FFR based on alternative boundary conditions and reduced-order fluid model is feasible, highly reproducible, and may be accurate in detecting FFR ≤ 0.8. It requires a short processing time and can be completed at point-of-care. Further validation is required in large prospective multicenter settings.
