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Computed Tomography Coronary Angiography and Computational Fluid Dynamics Based Fractional Flow Reserve Before and After Percutaneous Coronary Intervention
Invasive fractional flow reserve (FFR) is recommended to guide stent deployment. We previously introduced a non-invasive FFR calculation (FFR(B)) based on computed tomography coronary angiography (CTCA) with reduced-order computational fluid dynamics (CFD) and resistance boundary conditions. Current...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452917/ https://www.ncbi.nlm.nih.gov/pubmed/34557479 http://dx.doi.org/10.3389/fbioe.2021.739667 |
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author | Chandola, Gaurav Zhang, Jun-Mei Tan, Ru-San Chai, Ping Teo, Lynette Allen, John C. Low, Ris Huang, Weimin Leng, Shuang Fam, Jiang Ming Chin, Chee Yang Kassab, Ghassan S. Low, Adrian Fatt Hoe Tan, Swee Yaw Chua, Terrance Lim, Soo Teik Zhong, Liang |
author_facet | Chandola, Gaurav Zhang, Jun-Mei Tan, Ru-San Chai, Ping Teo, Lynette Allen, John C. Low, Ris Huang, Weimin Leng, Shuang Fam, Jiang Ming Chin, Chee Yang Kassab, Ghassan S. Low, Adrian Fatt Hoe Tan, Swee Yaw Chua, Terrance Lim, Soo Teik Zhong, Liang |
author_sort | Chandola, Gaurav |
collection | PubMed |
description | Invasive fractional flow reserve (FFR) is recommended to guide stent deployment. We previously introduced a non-invasive FFR calculation (FFR(B)) based on computed tomography coronary angiography (CTCA) with reduced-order computational fluid dynamics (CFD) and resistance boundary conditions. Current study aimed to assess the feasibility and accuracy of FFR(B) for predicting coronary hemodynamics before and after stenting, with invasive FFR as the reference. Twenty-five patients who had undergone CTCA were prospectively enrolled before invasive coronary angiography (ICA) and FFR-guided percutaneous coronary intervention (PCI) on 30 coronary vessels. Using reduced-order CFD with novel boundary conditions on three-dimensional (3D) patient-specific anatomic models reconstructed from CTCA, we calculated FFR(B) before and after virtual stenting. The latter simulated PCI by clipping stenotic segments from the 3D coronary models and replacing them with segments to mimic the deployed coronary stents. Pre- and post-virtual stenting FFR(B) were compared with FFR measured pre- and post-PCI by investigators blinded to FFR(B) results. Among 30 coronary lesions, pre-stenting FFR(B) (mean 0.69 ± 0.12) and FFR (mean 0.67 ± 0.13) exhibited good correlation (r = 0.86, p < 0.001) and agreement [mean difference 0.024, 95% limits of agreement (LoA): −0.11, 0.15]. Similarly, post-stenting FFR(B) (mean 0.84 ± 0.10) and FFR (mean 0.86 ± 0.08) exhibited fair correlation (r = 0.50, p < 0.001) and good agreement (mean difference 0.024, 95% LoA: −0.20, 0.16). The accuracy of FFR(B) for identifying post-stenting ischemic lesions (FFR ≤ 0.8) (residual ischemia) was 87% (sensitivity 80%, specificity 88%). Our novel FFR(B), based on CTCA with reduced-order CFD and resistance boundary conditions, accurately predicts the hemodynamic effects of stenting which may serve as a tool in PCI planning. |
format | Online Article Text |
id | pubmed-8452917 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84529172021-09-22 Computed Tomography Coronary Angiography and Computational Fluid Dynamics Based Fractional Flow Reserve Before and After Percutaneous Coronary Intervention Chandola, Gaurav Zhang, Jun-Mei Tan, Ru-San Chai, Ping Teo, Lynette Allen, John C. Low, Ris Huang, Weimin Leng, Shuang Fam, Jiang Ming Chin, Chee Yang Kassab, Ghassan S. Low, Adrian Fatt Hoe Tan, Swee Yaw Chua, Terrance Lim, Soo Teik Zhong, Liang Front Bioeng Biotechnol Bioengineering and Biotechnology Invasive fractional flow reserve (FFR) is recommended to guide stent deployment. We previously introduced a non-invasive FFR calculation (FFR(B)) based on computed tomography coronary angiography (CTCA) with reduced-order computational fluid dynamics (CFD) and resistance boundary conditions. Current study aimed to assess the feasibility and accuracy of FFR(B) for predicting coronary hemodynamics before and after stenting, with invasive FFR as the reference. Twenty-five patients who had undergone CTCA were prospectively enrolled before invasive coronary angiography (ICA) and FFR-guided percutaneous coronary intervention (PCI) on 30 coronary vessels. Using reduced-order CFD with novel boundary conditions on three-dimensional (3D) patient-specific anatomic models reconstructed from CTCA, we calculated FFR(B) before and after virtual stenting. The latter simulated PCI by clipping stenotic segments from the 3D coronary models and replacing them with segments to mimic the deployed coronary stents. Pre- and post-virtual stenting FFR(B) were compared with FFR measured pre- and post-PCI by investigators blinded to FFR(B) results. Among 30 coronary lesions, pre-stenting FFR(B) (mean 0.69 ± 0.12) and FFR (mean 0.67 ± 0.13) exhibited good correlation (r = 0.86, p < 0.001) and agreement [mean difference 0.024, 95% limits of agreement (LoA): −0.11, 0.15]. Similarly, post-stenting FFR(B) (mean 0.84 ± 0.10) and FFR (mean 0.86 ± 0.08) exhibited fair correlation (r = 0.50, p < 0.001) and good agreement (mean difference 0.024, 95% LoA: −0.20, 0.16). The accuracy of FFR(B) for identifying post-stenting ischemic lesions (FFR ≤ 0.8) (residual ischemia) was 87% (sensitivity 80%, specificity 88%). Our novel FFR(B), based on CTCA with reduced-order CFD and resistance boundary conditions, accurately predicts the hemodynamic effects of stenting which may serve as a tool in PCI planning. Frontiers Media S.A. 2021-09-07 /pmc/articles/PMC8452917/ /pubmed/34557479 http://dx.doi.org/10.3389/fbioe.2021.739667 Text en Copyright © 2021 Chandola, Zhang, Tan, Chai, Teo, Allen, Low, Huang, Leng, Fam, Chin, Kassab, Low, Tan, Chua, Lim and Zhong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Chandola, Gaurav Zhang, Jun-Mei Tan, Ru-San Chai, Ping Teo, Lynette Allen, John C. Low, Ris Huang, Weimin Leng, Shuang Fam, Jiang Ming Chin, Chee Yang Kassab, Ghassan S. Low, Adrian Fatt Hoe Tan, Swee Yaw Chua, Terrance Lim, Soo Teik Zhong, Liang Computed Tomography Coronary Angiography and Computational Fluid Dynamics Based Fractional Flow Reserve Before and After Percutaneous Coronary Intervention |
title | Computed Tomography Coronary Angiography and Computational Fluid Dynamics Based Fractional Flow Reserve Before and After Percutaneous Coronary Intervention |
title_full | Computed Tomography Coronary Angiography and Computational Fluid Dynamics Based Fractional Flow Reserve Before and After Percutaneous Coronary Intervention |
title_fullStr | Computed Tomography Coronary Angiography and Computational Fluid Dynamics Based Fractional Flow Reserve Before and After Percutaneous Coronary Intervention |
title_full_unstemmed | Computed Tomography Coronary Angiography and Computational Fluid Dynamics Based Fractional Flow Reserve Before and After Percutaneous Coronary Intervention |
title_short | Computed Tomography Coronary Angiography and Computational Fluid Dynamics Based Fractional Flow Reserve Before and After Percutaneous Coronary Intervention |
title_sort | computed tomography coronary angiography and computational fluid dynamics based fractional flow reserve before and after percutaneous coronary intervention |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452917/ https://www.ncbi.nlm.nih.gov/pubmed/34557479 http://dx.doi.org/10.3389/fbioe.2021.739667 |
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