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Change in Computed Tomography-Derived Fractional Flow Reserve Across the Lesion Improve the Diagnostic Performance of Functional Coronary Stenosis

Hankun Yan, Yang Gao, Na Zhao, Wenlei Geng, Zhihui Hou, Yunqiang An, Jie Zhang, Bin Lu

FRONTIERS IN CARDIOVASCULAR MEDICINE(2022)

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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.
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coronary artery disease,fractional flow reserve,coronary computed tomography angiography,computed tomography-derived fractional flow reserve,machine-learning
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