Comparison of systolic and diastolic CT-FFR for myocardial ischemia diagnosis.
Authors
Affiliations (14)
Affiliations (14)
- Department Radiology, Shanghai Geriatric Medical Center, 2560 Chunshen Road, Shanghai, 200237, China.
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
- Department of Radiology, Ningbo No.2 Hospital, Ningbo, 315000, China.
- Shanghai Municipal Institute for Cardiovascular Disease, Shanghai, 200032, China.
- Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- Department of Echocardiography, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. [email protected].
- Shanghai Medical College, Fudan University, Shanghai, 200032, China. [email protected].
- Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. [email protected].
- School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China. [email protected].
- Department Radiology, Shanghai Geriatric Medical Center, 2560 Chunshen Road, Shanghai, 200237, China. [email protected].
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. [email protected].
Abstract
CT-derived fractional flow reserve (CT-FFR) is a powerful tool for identifying hemodynamic ischemia. Coronary CT angiography (CCTA) images with the best quality in a cardiac cycle are conventionally reconstructed in clinical practice. To compare the diagnostic performance of machine learning (ML)-based CT-FFR between systolic and diastolic phases in identifying myocardial ischemia, using invasive fractional flow reserve (FFR) as a reference standard. From December 2020 to October 2021, consecutive coronary artery disease (CAD) patients who underwent coronary computed tomography angiography (CCTA) and invasive FFR were prospectively enrolled. Prospective electrocardiographic (ECG)-triggered scan from 30% to 80% of R-R interval was applied on image extraction of all the patients. CT-FFR was implemented in systolic and diastolic phases for each target vessel. Ninety-six patients (mean age 62 ± 8, 31.3% female) with 98 vessels were successfully simulated in both systolic and diastolic phases. The area under the curve (AUC) was significantly higher in diastolic CT-FFR (0.885 vs. 0.790, p < 0.001). The limits of agreement (LoA) range was narrower in diastolic CT-FFR (-0.134 to 0.148), compared with systolic CT-FFR (-0.234 to 0.273). Diastolic CT-FFR performed better than systolic CT-FFR in sensitivity (81.5% vs. 66.7%), specificity (83.1% vs. 71.8%), and accuracy (82.7% vs. 70.4%, all p < 0.05). The LoA range was narrower for diastolic CT-FFR across all stenosis degree groups, with diagnostic performance better in sensitivity (84.0% vs. 68.0%) and accuracy (81.4% vs. 58.1%, p < 0.001) among severe stenosis group. In ML-based CT-FFR methods, diastolic CT-FFR showed better diagnostic performance and narrower LoA than systolic CT-FFR. The study has been registered through the Ethics Committee of Zhongshan Hospital, Fudan University, with the clinical trial number B2020-088R. The registration date is May 15, 2020.