AUTHOR=Lee Yong-Joon , Kim Young Woo , Ha Jinyong , Kim Minug , Guagliumi Giulio , Granada Juan F. , Lee Seul-Gee , Lee Jung-Jae , Cho Yun-Kyeong , Yoon Hyuck Jun , Lee Jung Hee , Kim Ung , Jang Ji-Yong , Oh Seung-Jin , Lee Seung-Jun , Hong Sung-Jin , Ahn Chul-Min , Kim Byeong-Keuk , Chang Hyuk-Jae , Ko Young-Guk , Choi Donghoon , Hong Myeong-Ki , Jang Yangsoo , Lee Joon Sang , Kim Jung-Sun 

TITLE=Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography—Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.925414

DOI=10.3389/fcvm.2022.925414

ISSN=2297-055X

ABSTRACT=Background: Coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) provide additional functional information beyond the anatomy by applying computational fluid dynamics (CFD). This study sought to evaluate a novel approach for estimating computational fractional flow reserve (FFR) from coronary CTA-OCT fusion images.
Methods: Among patients who underwent coronary CTA, 148 patients who underwent both pressure wire-based FFR measurement and OCT during angiography to evaluate intermediate stenosis in the left anterior descending artery were included from the prospective registry. Coronary CTA-OCT fusion images were created, and CFD was applied to estimate computational FFR. Based on pressure wire-based FFR as a reference, the diagnostic performance of Fusion-FFR was compared with that of CT-FFR and OCT-FFR.
Results: Fusion-FFR was strongly correlated with FFR (r=0.836, P<0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR (r=0.682, P<0.001; z statistic, 5.42, P<0.001) and between FFR and OCT-FFR (r=0.705, P<0.001; z statistic, 4.38, P<0.001). Area under the receiver operating characteristics curve to assess functionally significant stenosis was higher for Fusion-FFR than for CT-FFR (0.90 vs. 0.83, P=0.024) and OCT-FFR (0.90 vs. 0.83, P=0.043). Fusion-FFR exhibited 84.5% accuracy, 84.6% sensitivity, 84.3% specificity, 80.9% positive predictive value, and 87.5% negative predictive value. Especially accuracy, specificity, and positive predictive value were superior for Fusion-FFR than for CT-FFR (73.0%, P=0.007; 61.4%, P<0.001; 64.0%, P<0.001) and OCT-FFR (75.7%, P=0.021; 73.5%, P=0.020; 69.9%, P=0.012).
Conclusions: CFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone.