Diagnostic Performance of CT FFR With a New Parameter Optimized Computational Fluid Dynamics Algorithm From the CT-FFR-CHINA Trial: Characteristic Analysis of Gray Zone Lesions and Misdiagnosed Lesions

Abstract

To assess the diagnostic performance of fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) (CT-FFR) obtained by a new computational fluid dynamics (CFD) algorithm to detect ischemia, using FFR as a reference, and analyze the characteristics of "gray zone" and misdiagnosed lesions. This prospective multicenter clinical trial (NCT03692936, https://clinicaltrials.gov/) analyzed 317 patients with coronary stenosis between 30 and 90% in 366 vessels from five centers undergoing CTA and FFR between November 2018 and March 2020. CT-FFR were obtained from a CFD algorithm (Heartcentury Co., Ltd., Beijing, China). Diagnostic performance of CT-FFR and CTA in detecting ischemia was assessed. Coronary atherosclerosis characteristics of gray zone and misdiagnosed lesions were analyzed. Per-vessel sensitivity, specificity and accuracy for CT-FFR and CTA were 89.9, 87.8, 88.8% and 89.3, 35.5, 60.4%, respectively. Accuracy of CT-FFR was 80.0% in gray zone lesions. In gray zone lesions, lumen area and diameter were significantly larger than lesions with FFR < 0.76 (both p < 0.001), lesion length, non-calcified and calcified plaque volume were all significantly higher than non-ischemic lesions (all p < 0.05). In gray zone lesions, Agatston score (OR = 1.009, p = 0.044) was the risk factor of false negative results of CT-FFR. In non-ischemia lesions, coronary stenosis >50% (OR = 2.684, p = 0.03) was the risk factor of false positive results. Lumen area (OR = 0.567, p = 0.02) and diameter (OR = 0.296, p = 0.03) had a significant negative effect on the risk of false positive results of CT-FFR. In conclusion, CT-FFR based on the new parameter-optimized CFD model provides better diagnostic performance for lesion-specific ischemia than CTA. For gray zone lesions, stenosis degree was less than those with FFR < 0.76, and plaque load was heavier than non-ischemic lesions.

Keywords: CT angiography; CT-FFR; computational fluid dynamics; diagnostic performance; gray zone.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Gao, Zhao, Song, Hu, Jiang, Chen, Zhang, Dou, Mu, Yang, Fu, Xu, Li, Fan, An, Wang, Li, Xu and Lu.

Figures

FIGURE 1

Study flowchart. CTA, computed tomography angiography; FFR, fractional flow reserve.

FIGURE 2

Correlation between CT-FFR and invasive FFR. Measurements of CT-FFR and FFR are shown as a scatter plot (black lines show the 0.8 cutoffs and yellow line shows the fitted line).

FIGURE 3

Bland–Altman plot of CT-FFR and FFR. Bland–Altman analysis show a very mild systematic underestimation of CT-FFR with FFR values in all lesions (A), and in lesions with FFR < 0.76 (B), a relatively large underestimation of CT-FFR with FFR in gray zone lesions (C), and a nearly equal value of CT-FFR and FFR in non-ischemia lesions (D).

FIGURE 4

Representative case from the study. A 45-year-old man with atypical angina. CTA showed a 50–70% stenosis in the middle segment of the left anterior descending artery (LAD) (A), and CT-FFR value (0.77) was measured in the distal of target lesion (B). Invasive coronary angiography (ICA) demonstrated the severe stenosis of 70% (C). FFR measured at the corresponding location was 0.78 (D).

FIGURE 5

Diagnostic accuracy of CT-FFR in different FFR categories. The diagnostic accuracy of CT-FFR among the different FFR groups in correctly identifying the hemodynamically ischemic lesions is shown. The diagnostic accuracy of lesions in “gray zone” lesions (FFR = 0.75–0.80) was the lowest, and the farther from the gray zone, the higher the accuracy.