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

Authors
 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, Dong Hoon  ;  Hong, Myeong Ki  ;  Jang, Yang Soo  ;  Lee, Joon Sang  ;  Kim, Jung Sun 
Citation
 Frontiers in Cardiovascular Medicine, Vol.9, 2022-06 
Article Number
 925414 
Journal Title
FRONTIERS IN CARDIOVASCULAR MEDICINE
ISSN
 2297-055X 
Issue Date
2022-06
Keywords
fractional flow reserve (FFR) ; coronary computed tomography angiography (coronary CTA) ; optical coherence tomography (OCT) ; fusion image ; computational 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.
DOI
10.3389/fcvm.2022.925414
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Ko, Young Guk(고영국) ORCID logo https://orcid.org/0000-0001-7748-5788
Kim, Byeong Keuk(김병극) ORCID logo https://orcid.org/0000-0003-2493-066X
Kim, Jung Sun(김중선) ORCID logo https://orcid.org/0000-0003-2263-3274
Ahn, Chul-Min(안철민)
Lee, Seung-Jun(이승준) ORCID logo https://orcid.org/0000-0002-9201-4818
Lee, Yong Joon(이용준)
Jang, Yang Soo(장양수) ORCID logo https://orcid.org/0000-0002-2169-3112
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
Choi, Dong Hoon(최동훈) ORCID logo https://orcid.org/0000-0002-2009-9760
Hong, Myeong Ki(홍명기) ORCID logo https://orcid.org/0000-0002-2090-2031
Hong, Sung Jin(홍성진) ORCID logo https://orcid.org/0000-0003-4893-039X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/189282
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