Performance of a Novel CT-Derived Fractional Flow Reserve Measurement to Detect Hemodynamically Significant Coronary Stenosis
10.3346/jkms.2023.38.e254
- Author:
Si-Hyuck KANG
1
;
Soo-Hyun KIM
;
Sun-Hwa KIM
;
Eun Ju CHUN
;
Woo-Young CHUNG
;
Chang-Hwan YOON
;
Sang-Don PARK
;
Chang-Wook NAM
;
Ki-Hwan KWON
;
Joon-Hyung DOH
;
Young-Sup BYUN
;
Jang-Whan BAE
;
Tae-Jin YOUN
;
In-Ho CHAE
Author Information
1. Division of Cardiology, Department of Internal Medicine, College of Medicine, Seoul National University and Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, Korea
- Publication Type:Original Article
- From:Journal of Korean Medical Science
2023;38(32):e254-
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background:Fractional flow reserve (FFR) based on computed tomography (CT) has been shown to better identify ischemia-causing coronary stenosis. However, this current technology requires high computational power, which inhibits its widespread implementation in clinical practice. This prospective, multicenter study aimed at validating the diagnostic performance of a novel simple CT based fractional flow reserve (CT-FFR) calculation method in patients with coronary artery disease.
Methods:Patients who underwent coronary CT angiography (CCTA) within 90 days and invasive coronary angiography (ICA) were prospectively enrolled. A hemodynamically significant lesion was defined as an FFR ≤ 0.80, and the area under the receiver operating characteristic curve (AUC) was the primary measure. After the planned analysis for the initial algorithm A, we performed another set of exploratory analyses for an improved algorithm B.
Results:Of 184 patients who agreed to participate in the study, 151 were finally analyzed.Hemodynamically significant lesions were observed in 79 patients (52.3%). The AUC was 0.71 (95% confidence interval [CI], 0.63–0.80) for CCTA, 0.65 (95% CI, 0.56–0.74) for CT-FFR algorithm A (P = 0.866), and 0.78 (95% CI, 0.70–0.86) for algorithm B (P = 0.112). Diagnostic accuracy was 0.63 (0.55–0.71) for CCTA alone, 0.66 (0.58–0.74) for algorithm A, and 0.76 (0.68–0.82) for algorithm B.
Conclusion:This study suggests the feasibility of automated CT-FFR, which can be performed on-site within several hours. However, the diagnostic performance of the current algorithm does not meet the a priori criteria for superiority. Future research is required to improve the accuracy.
