Functional Assessment of Coronary Artery Stenosis from Coronary Angiography and Computed Tomography: Angio-FFR vs. CT-FFR.
Xueqiang GuanDan SongChangling LiYumeng HuXiaochang LengXiaosheng ShengLifang BaoYibin PanLiang DongJun JiangJianping XiangWenbing JiangPublished in: Journal of cardiovascular translational research (2023)
This study was designed to compare the diagnostic performance of angio-FFR and CT-FFR for detecting hemodynamically significant coronary stenosis. Angio-FFR and CT-FFR were measured in 110 patients (139 vessels) with stable coronary disease using invasive FFR as the reference standard. On per-patient basis, angio-FFR was highly correlated with FFR (r =0.78, p <0.001), while the correlation was moderate between CT-FFR and FFR (r =0.68, p <0.001). Diagnostic accuracy, sensitivity, and specificity for angio-FFR were 94.6%, 91.4%, and 96.0%, respectively; and those of CT-FFR were 91.8%, 91.4%, and 92%, respectively. Bland-Altman analysis showed that angio-FFR had a larger average difference and a smaller root mean squared deviation than CT-FFR compared with FFR (-0.014±0.056 vs. 0.0003±0.072). Angio-FFR had a slightly higher AUC than that of CT-FFR (0.946 vs. 0.935, p =0.750). Angio-FFR and CT-FFR computed from coronary images could be accurate and efficient computational tools for detecting lesion-specific ischemia of coronary artery stenosis. Angio-FFR and CT-FFR calculated based on the two types of images can both accurately diagnose functional ischemia of coronary stenosis. CT-FFR can act as a gatekeeper to the catheter room, assisting doctors in determining whether patients need to be screened by coronary angiography. Angio-FFR can be used in the catheter room to determine the functional significant stenosis for helping decision-making in revascularization.
Keyphrases
- computed tomography
- coronary artery
- dual energy
- image quality
- contrast enhanced
- positron emission tomography
- ejection fraction
- machine learning
- prognostic factors
- pulmonary artery
- deep learning
- mass spectrometry
- high intensity
- convolutional neural network
- pulmonary hypertension
- atrial fibrillation
- aortic stenosis
- case report
- ultrasound guided
- patient reported