Change in Computed Tomography-Derived Fractional Flow Reserve Across the Lesion Improve the Diagnostic Performance of Functional Coronary Stenosis
FRONTIERS IN CARDIOVASCULAR MEDICINE(2022)
摘要
Aims: This study sought to evaluate the diagnostic performance of change in computed tomography-derived fractional flow reserve (CT-FFR) across the lesion (Delta CT-FFR) for identifying ischemia lesions with FFR as the reference standard.Methods: Patients who underwent coronary CT angiography (CCTA) and FFR measurement within 1 week from December 2018 to December 2019 were retrospectively enrolled. CT-FFR within 2 cm distal to the lesion, Delta CT-FFR and plaque characteristics were analyzed. The diagnostic accuracy of CCTA (coronary stenosis >= 50%), CT-FFR <= 0.80, and Delta CT-FFR >= 0.15 (based on the largest Youden index) were assessed with FFR as the reference standard. The relationship between plaque characteristics and Delta CT-FFR was analyzed.Results: The specificity of Delta CT-FFR and CT-FFR were 70.8 and 67.4%, respectively, which were both higher than CCTA (39.3%) (both P < 0.001), while there were no statistical significance in sensitivity among the three (84.5, 77.4, 88.1%, respectively; P = 0.08). The area under the curves (AUCs) of Delta CT-FFR and CT-FFR were 0.803 and 0.743, respectively, which were both higher than that of CCTA (0.637) (both P < 0.05), and the AUC of Delta CT-FFR was higher than that of CT-FFR (P < 0.001). Multivariable analysis showed that low-attenuation plaque (LAP) volume (odds ratio [OR], 1.006) and plaque length (OR, 1.021) were independently correlated with Delta CT-FFR (both P < 0.05).Conclusions: CT-FFR and Delta CT-FFR and here especially the Delta CT-FFR could improve the diagnostic performance of ischemia compared with CCTA alone. LAP volume and plaque length were the independent risk factors of Delta CT-FFR.
更多查看译文
关键词
coronary artery disease,fractional flow reserve,coronary computed tomography angiography,computed tomography-derived fractional flow reserve,machine-learning
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要