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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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dc.contributor.author김중선-
dc.contributor.author이용준-
dc.contributor.author이승준-
dc.contributor.author홍성진-
dc.contributor.author안철민-
dc.contributor.author김병극-
dc.contributor.author장혁재-
dc.contributor.author고영국-
dc.contributor.author최동훈-
dc.contributor.author홍명기-
dc.contributor.author장양수-
dc.contributor.author김중선-
dc.date.accessioned2022-08-23T00:09:16Z-
dc.date.available2022-08-23T00:09:16Z-
dc.date.issued2022-06-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/189282-
dc.description.abstractBackground: 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. Clinical trial registration: [www.ClinicalTrials.gov], identifier [NCT03298282].-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherFrontiers Media S.A.-
dc.relation.isPartOfFRONTIERS IN CARDIOVASCULAR MEDICINE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleComputational Fractional Flow Reserve From Coronary Computed Tomography Angiography-Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorYong-Joon Lee-
dc.contributor.googleauthorYoung Woo Kim-
dc.contributor.googleauthorJinyong Ha-
dc.contributor.googleauthorMinug Kim-
dc.contributor.googleauthorGiulio Guagliumi-
dc.contributor.googleauthorJuan F Granada-
dc.contributor.googleauthorSeul-Gee Lee-
dc.contributor.googleauthorJung-Jae Lee-
dc.contributor.googleauthorYun-Kyeong Cho-
dc.contributor.googleauthorHyuck Jun Yoon-
dc.contributor.googleauthorJung Hee Lee-
dc.contributor.googleauthorUng Kim-
dc.contributor.googleauthorJi-Yong Jang-
dc.contributor.googleauthorSeung-Jin Oh-
dc.contributor.googleauthorSeung-Jun Lee-
dc.contributor.googleauthorSung-Jin Hong-
dc.contributor.googleauthorChul-Min Ahn-
dc.contributor.googleauthorByeong-Keuk Kim-
dc.contributor.googleauthorHyuk-Jae Chang-
dc.contributor.googleauthorYoung-Guk Ko-
dc.contributor.googleauthorDonghoon Choi-
dc.contributor.googleauthorMyeong-Ki Hong-
dc.contributor.googleauthorYangsoo Jang-
dc.contributor.googleauthorJoon Sang Lee-
dc.contributor.googleauthorJung-Sun Kim-
dc.identifier.doi10.3389/fcvm.2022.925414-
dc.contributor.localIdA00961-
dc.contributor.localIdA02984-
dc.relation.journalcodeJ04002-
dc.identifier.eissn2297-055X-
dc.identifier.pmid35770218-
dc.subject.keywordcomputational fluid dynamics (CFD)-
dc.subject.keywordcoronary computed tomography angiography (coronary CTA)-
dc.subject.keywordfractional flow reserve (FFR)-
dc.subject.keywordfusion image-
dc.subject.keywordoptical coherence tomography (OCT)-
dc.contributor.alternativeNameKim, Jung Sun-
dc.contributor.affiliatedAuthor김중선-
dc.contributor.affiliatedAuthor이용준-
dc.citation.volume9-
dc.citation.startPage925414-
dc.identifier.bibliographicCitationFRONTIERS IN CARDIOVASCULAR MEDICINE, Vol.9 : 925414, 2022-06-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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