Literature DB >> 27502209

Assessing Computational Fractional Flow Reserve From Optical Coherence Tomography in Patients With Intermediate Coronary Stenosis in the Left Anterior Descending Artery.

Jinyong Ha1, Jung-Sun Kim1, Jaeyeong Lim1, Gihoon Kim1, Seungwan Lee1, Joon Sang Lee1, Dong-Ho Shin1, Byeong-Keuk Kim1, Young-Guk Ko1, Donghoon Choi1, Yangsoo Jang1, Myeong-Ki Hong2.   

Abstract

BACKGROUND: Intravascular optical coherence tomography (OCT) imaging provides limited information on the functional assessment of coronary stenosis. We evaluated a new approach to OCT image-based computation modeling, which can be used to estimate the fractional flow reserve (FFR) in patients with intermediate coronary stenosis. METHODS AND
RESULTS: Ninety-two patients with intermediate diameter stenosis in the left anterior descending artery underwent both FFR measurement with pressure wires and OCT examination. Using the OCT data, a computational fluid dynamics algorithm was used to calculate the computational FFR (FFROCT). The diagnostic performance of the FFROCT was assessed based on the pressure wire-based FFR. The median FFR and FFROCT values were 0.86 (0.79-0.89) and 0.89 (0.82-0.94), respectively. The average diameter stenosis in quantitative coronary angiography and area stenosis in OCT were 58.1±13.4% and 67.5±13.5%, respectively. The FFROCT was better correlated to the FFR than were the anatomic variables (r=0.72; P<0.001 versus r=0.46; P<0.001 for minimal luminal diameter on quantitative coronary angiography or r=0.57; P<0.001 for minimal lumen area on OCT). When functionally significant stenosis was defined as an FFR cutoff value of ≤0.8, FFROCT resulted in 88.0% accuracy, 68.7% sensitivity, and 95.6% specificity. The positive and negative predictive values were 84.2% and 89.0%, respectively.
CONCLUSIONS: The computation of FFROCT enables assessment not only of anatomic information, but also of the functional significance of intermediate stenosis. This measurement may be a useful approach for the simultaneous evaluation of the functional and anatomic severity of coronary stenosis.
© 2016 American Heart Association, Inc.

Entities:  

Keywords:  coronary artery disease; coronary stenosis; coronary vessels; fractional flow reserve, myocardial; tomography, optical coherence

Mesh:

Year:  2016        PMID: 27502209     DOI: 10.1161/CIRCINTERVENTIONS.116.003613

Source DB:  PubMed          Journal:  Circ Cardiovasc Interv        ISSN: 1941-7640            Impact factor:   6.546


  9 in total

1.  Automated accurate lumen segmentation using L-mode interpolation for three-dimensional intravascular optical coherence tomography.

Authors:  Arsalan Akbar; T S Khwaja; Ammar Javaid; Jun-Sun Kim; Jinyong Ha
Journal:  Biomed Opt Express       Date:  2019-09-23       Impact factor: 3.732

2.  Diagnostic accuracy of intracoronary optical coherence tomography-derived fractional flow reserve for assessment of coronary stenosis severity.

Authors:  Wei Yu; Jiayue Huang; Dean Jia; Shaoliang Chen; Owen Christopher Raffel; Daixin Ding; Feng Tian; Jing Kan; Su Zhang; Fuhua Yan; Yundai Chen; Hiram G Bezerra; William Wijns; Shengxian Tu
Journal:  EuroIntervention       Date:  2019-06-20       Impact factor: 6.534

3.  Fractional flow reserve for coronary stenosis assessment derived from fusion of intravascular ultrasound and X-ray angiography.

Authors:  Jun Jiang; Li Feng; Changling Li; Yongqing Xia; Jingsong He; Xiaochang Leng; Liang Dong; Xinyang Hu; Jian'an Wang; Jianping Xiang
Journal:  Quant Imaging Med Surg       Date:  2021-11

4.  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
Journal:  Front Cardiovasc Med       Date:  2022-06-13

Review 5.  Optical Coherence Tomography: An Eye Into the Coronary Artery.

Authors:  Ankush Gupta; Abhinav Shrivastava; Rajesh Vijayvergiya; Sanya Chhikara; Rajat Datta; Atiya Aziz; Daulat Singh Meena; Ranjit Kumar Nath; J Ratheesh Kumar
Journal:  Front Cardiovasc Med       Date:  2022-05-11

Review 6.  Ischemia-based Coronary Revascularization: Beyond Anatomy and Fractional Flow Reserve.

Authors:  Hong Seok Lim; Kyoung Woo Seo; Myeong Ho Yoon; Hyoung Mo Yang; Seung Jea Tahk
Journal:  Korean Circ J       Date:  2017-11-06       Impact factor: 3.243

7.  Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve.

Authors:  P J Blanco; C A Bulant; L O Müller; G D Maso Talou; C Guedes Bezerra; P A Lemos; R A Feijóo
Journal:  Sci Rep       Date:  2018-11-22       Impact factor: 4.379

8.  Building a Fast Virtual Fractional Flow Reserve: Reductionists or Dreamers?

Authors:  Morton J Kern; Jeannie H Yu; Arnold H Seto
Journal:  JACC Basic Transl Sci       Date:  2017-08-28

9.  Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study.

Authors:  Jung-Joon Cha; Tran Dinh Son; Jinyong Ha; Jung-Sun Kim; Sung-Jin Hong; Chul-Min Ahn; Byeong-Keuk Kim; Young-Guk Ko; Donghoon Choi; Myeong-Ki Hong; Yangsoo Jang
Journal:  Sci Rep       Date:  2020-11-24       Impact factor: 4.379

  9 in total

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