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

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Author(s)
Yong-Joon LeeYoung Woo KimJinyong HaMinug KimGiulio GuagliumiJuan F. GranadaSeul-Gee LeeJung-Jae LeeYun-Kyeong ChoHyuck Jun YoonJung Hee LeeUng KimJi-Yong JangSeung-Jin OhSeung-Jun LeeSung-Jin HongChul-Min AhnByeong-Keuk KimHyuk-Jae ChangYoung-Guk KoDonghoon ChoiMyeong-Ki HongYangsoo JangJoon Sang LeeJung-Sun Kim
Keimyung Author(s)
Cho, Yun KyeongYoon, Hyuck Jun
Department
Dept. of Internal Medicine (내과학)
Journal Title
Front Cardiovasc Med
Issued Date
2022
Volume
9
Keyword
fractional flow reserve (FFR)coronary computed tomography angiography (coronary CTA)optical coherence tomography (OCT)fusion imagecomputational fluid dynamics (CFD)
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).

Conclusion:
CFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone.
Keimyung Author(s)(Kor)
조윤경
윤혁준
Publisher
School of Medicine (의과대학)
Type
Article
ISSN
2297-055X
Source
https://www.frontiersin.org/articles/10.3389/fcvm.2022.925414/full
DOI
10.3389/fcvm.2022.925414
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/44374
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Internal Medicine (내과학)
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