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=9

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=<sec><title>Background</title><p>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.</p></sec><sec><title>Methods</title><p>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.</p></sec><sec><title>Results</title><p>Fusion-FFR was strongly correlated with FFR (<italic>r</italic> = 0.836, <italic>P</italic> &lt; 0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR (<italic>r</italic> = 0.682, <italic>P</italic> &lt; 0.001; z statistic, 5.42, <italic>P</italic> &lt; 0.001) and between FFR and OCT-FFR (<italic>r</italic> = 0.705, <italic>P</italic> &lt; 0.001; z statistic, 4.38, <italic>P</italic> &lt; 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, <italic>P</italic> = 0.024) and OCT-FFR (0.90 vs. 0.83, <italic>P</italic> = 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%, <italic>P</italic> = 0.007; 61.4%, <italic>P</italic> &lt; 0.001; 64.0%, <italic>P</italic> &lt; 0.001) and OCT-FFR (75.7%, <italic>P</italic> = 0.021; 73.5%, <italic>P</italic> = 0.020; 69.9%, <italic>P</italic> = 0.012).</p></sec><sec><title>Conclusion</title><p>CFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone.</p></sec><sec><title>Clinical Trial Registration</title><p>[<ext-link ext-link-type="uri" xlink:href="http://www.ClinicalTrials.gov" xmlns:xlink="http://www.w3.org/1999/xlink">www.ClinicalTrials.gov</ext-link>], identifier [NCT03298282].</p></sec>