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Three-dimensional modeling and numerical analysis of fractional flow reserve in human coronary arteries

INTRODUCTION: Noninvasive fractional flow reserve (FFR) computed from CT (FFR(CT)) is a novel method for determining the physiologic significance of coronary artery disease (CAD). Several clinical trials have been conducted, but its diagnostic performance varied among different trials. AIM: To deter...

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Autores principales: Dai, Neng, Lv, Hui-Jie, Xiang, Ya-Fei, Fan, Bing, Li, Wei-Ming, Xu, Ya-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Termedia Publishing House 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4777703/
https://www.ncbi.nlm.nih.gov/pubmed/26966446
http://dx.doi.org/10.5114/pwki.2016.56946
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author Dai, Neng
Lv, Hui-Jie
Xiang, Ya-Fei
Fan, Bing
Li, Wei-Ming
Xu, Ya-Wei
author_facet Dai, Neng
Lv, Hui-Jie
Xiang, Ya-Fei
Fan, Bing
Li, Wei-Ming
Xu, Ya-Wei
author_sort Dai, Neng
collection PubMed
description INTRODUCTION: Noninvasive fractional flow reserve (FFR) computed from CT (FFR(CT)) is a novel method for determining the physiologic significance of coronary artery disease (CAD). Several clinical trials have been conducted, but its diagnostic performance varied among different trials. AIM: To determine the cut-off value of FFR(CT) and its correlation with the gold standard used to diagnose CAD in clinical practice. MATERIAL AND METHODS: Forty patients with single vessel disease were included in our study. Computed tomography scan and coronary angiography with FFR were conducted for these patients. Three-dimensional geometric reconstruction and numerical analysis based on the computed tomographic angiogram (CTA) of coronary arteries were applied to obtain the values of FFR(CT). The correlation between FFR(CT) and the gold standard used in clinical practice was tested. RESULTS: For FFR(CT), the best cut-off value was 0.76, with the sensitivity, specificity, positive predictive value and negative predictive values of 84.6%, 92.9%, 88% and 73.3%, respectively. The area under the receiver-operator characteristics curve was 0.945 (p < 0.0001). There was a good correlation of FFR(CT) values with FFR values (r = 0.94, p < 0.0001), with a slight overestimation of FFR(CT) as compared with measured FFR (mean difference 0.01 ±0.11, p < 0.05). For inter-observer agreement, the mean κ value was 0.69 (0.61 to 0.78) and for intra-observer agreement the mean κ value was 0.61 (0.50 to 0.72). CONCLUSIONS: FFR(CT) derived from CT of the coronary artery is a reliable non-invasive way providing reliable functional information of coronary artery stenosis.
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spelling pubmed-47777032016-03-10 Three-dimensional modeling and numerical analysis of fractional flow reserve in human coronary arteries Dai, Neng Lv, Hui-Jie Xiang, Ya-Fei Fan, Bing Li, Wei-Ming Xu, Ya-Wei Postepy Kardiol Interwencyjnej Original Paper INTRODUCTION: Noninvasive fractional flow reserve (FFR) computed from CT (FFR(CT)) is a novel method for determining the physiologic significance of coronary artery disease (CAD). Several clinical trials have been conducted, but its diagnostic performance varied among different trials. AIM: To determine the cut-off value of FFR(CT) and its correlation with the gold standard used to diagnose CAD in clinical practice. MATERIAL AND METHODS: Forty patients with single vessel disease were included in our study. Computed tomography scan and coronary angiography with FFR were conducted for these patients. Three-dimensional geometric reconstruction and numerical analysis based on the computed tomographic angiogram (CTA) of coronary arteries were applied to obtain the values of FFR(CT). The correlation between FFR(CT) and the gold standard used in clinical practice was tested. RESULTS: For FFR(CT), the best cut-off value was 0.76, with the sensitivity, specificity, positive predictive value and negative predictive values of 84.6%, 92.9%, 88% and 73.3%, respectively. The area under the receiver-operator characteristics curve was 0.945 (p < 0.0001). There was a good correlation of FFR(CT) values with FFR values (r = 0.94, p < 0.0001), with a slight overestimation of FFR(CT) as compared with measured FFR (mean difference 0.01 ±0.11, p < 0.05). For inter-observer agreement, the mean κ value was 0.69 (0.61 to 0.78) and for intra-observer agreement the mean κ value was 0.61 (0.50 to 0.72). CONCLUSIONS: FFR(CT) derived from CT of the coronary artery is a reliable non-invasive way providing reliable functional information of coronary artery stenosis. Termedia Publishing House 2016-02-11 2016 /pmc/articles/PMC4777703/ /pubmed/26966446 http://dx.doi.org/10.5114/pwki.2016.56946 Text en Copyright © 2016 Termedia http://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
spellingShingle Original Paper
Dai, Neng
Lv, Hui-Jie
Xiang, Ya-Fei
Fan, Bing
Li, Wei-Ming
Xu, Ya-Wei
Three-dimensional modeling and numerical analysis of fractional flow reserve in human coronary arteries
title Three-dimensional modeling and numerical analysis of fractional flow reserve in human coronary arteries
title_full Three-dimensional modeling and numerical analysis of fractional flow reserve in human coronary arteries
title_fullStr Three-dimensional modeling and numerical analysis of fractional flow reserve in human coronary arteries
title_full_unstemmed Three-dimensional modeling and numerical analysis of fractional flow reserve in human coronary arteries
title_short Three-dimensional modeling and numerical analysis of fractional flow reserve in human coronary arteries
title_sort three-dimensional modeling and numerical analysis of fractional flow reserve in human coronary arteries
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4777703/
https://www.ncbi.nlm.nih.gov/pubmed/26966446
http://dx.doi.org/10.5114/pwki.2016.56946
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