46 110

Cited 0 times in

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

Authors
 Yong-Joon Lee  ;  Young Woo Kim  ;  Jinyong Ha  ;  Minug Kim  ;  Giulio Guagliumi  ;  Juan F Granada  ;  Seul-Gee Lee  ;  Jung-Jae Lee  ;  Yun-Kyeong Cho  ;  Hyuck Jun Yoon  ;  Jung Hee Lee  ;  Ung Kim  ;  Ji-Yong Jang  ;  Seung-Jin Oh  ;  Seung-Jun Lee  ;  Sung-Jin Hong  ;  Chul-Min Ahn  ;  Byeong-Keuk Kim  ;  Hyuk-Jae Chang  ;  Young-Guk Ko  ;  Donghoon Choi  ;  Myeong-Ki Hong  ;  Yangsoo Jang  ;  Joon Sang Lee  ;  Jung-Sun Kim 
Citation
 FRONTIERS IN CARDIOVASCULAR MEDICINE, Vol.9 : 925414, 2022-06 
Journal Title
FRONTIERS IN CARDIOVASCULAR MEDICINE
Issue Date
2022-06
Keywords
computational fluid dynamics (CFD) ; coronary computed tomography angiography (coronary CTA) ; fractional flow reserve (FFR) ; fusion image ; optical coherence tomography (OCT)
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.

Clinical trial registration: [www.ClinicalTrials.gov], identifier [NCT03298282].
Files in This Item:
T202202147.pdf Download
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) ORCID logo https://orcid.org/0000-0002-7071-4370
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
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links