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Improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy

Transcatheter aortic valve replacement (TAVR) is now a standard treatment for high-surgical-risk patients with severe aortic valve stenosis. TAVR is being explored for broader indications including degenerated bioprosthetic valves, bicuspid valves and for aortic valve (AV) insufficiency. It is, howe...

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Autores principales: Govindarajan, Vijay, Kolanjiyil, Arun, Johnson, Nils P., Kim, Hyunggun, Chandran, Krishnan B., McPherson, David D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826300/
https://www.ncbi.nlm.nih.gov/pubmed/35154799
http://dx.doi.org/10.1098/rsos.211694
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author Govindarajan, Vijay
Kolanjiyil, Arun
Johnson, Nils P.
Kim, Hyunggun
Chandran, Krishnan B.
McPherson, David D.
author_facet Govindarajan, Vijay
Kolanjiyil, Arun
Johnson, Nils P.
Kim, Hyunggun
Chandran, Krishnan B.
McPherson, David D.
author_sort Govindarajan, Vijay
collection PubMed
description Transcatheter aortic valve replacement (TAVR) is now a standard treatment for high-surgical-risk patients with severe aortic valve stenosis. TAVR is being explored for broader indications including degenerated bioprosthetic valves, bicuspid valves and for aortic valve (AV) insufficiency. It is, however, challenging to predict whether the chosen valve size, design or its orientation would produce the most-optimal haemodynamics in the patient. Here, we present a novel patient-specific evaluation framework to realistically predict the patient's AV performance with a high-fidelity fluid–structure interaction analysis that included the patient's left ventricle and ascending aorta (AAo). We retrospectively evaluated the pre- and post-TAVR dynamics of a patient who underwent a 23 mm TAVR and evaluated against the patient's virtually de-calcified AV serving as a hypothetical benchmark. Our model predictions were consistent with clinical data. Stenosed AV produced a turbulent flow during peak-systole, while aortic flow with TAVR and de-calcified AV were both in the laminar-to-turbulent transitional regime with an estimated fivefold reduction in viscous dissipation. For TAVR, dissipation was highest during early systole when valve deformation was the greatest, suggesting that an efficient valve opening may reduce energy loss. Our study demonstrates that such patient-specific modelling frameworks can be used to improve predictability and in the planning of AV interventions.
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spelling pubmed-88263002022-02-10 Improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy Govindarajan, Vijay Kolanjiyil, Arun Johnson, Nils P. Kim, Hyunggun Chandran, Krishnan B. McPherson, David D. R Soc Open Sci Engineering Transcatheter aortic valve replacement (TAVR) is now a standard treatment for high-surgical-risk patients with severe aortic valve stenosis. TAVR is being explored for broader indications including degenerated bioprosthetic valves, bicuspid valves and for aortic valve (AV) insufficiency. It is, however, challenging to predict whether the chosen valve size, design or its orientation would produce the most-optimal haemodynamics in the patient. Here, we present a novel patient-specific evaluation framework to realistically predict the patient's AV performance with a high-fidelity fluid–structure interaction analysis that included the patient's left ventricle and ascending aorta (AAo). We retrospectively evaluated the pre- and post-TAVR dynamics of a patient who underwent a 23 mm TAVR and evaluated against the patient's virtually de-calcified AV serving as a hypothetical benchmark. Our model predictions were consistent with clinical data. Stenosed AV produced a turbulent flow during peak-systole, while aortic flow with TAVR and de-calcified AV were both in the laminar-to-turbulent transitional regime with an estimated fivefold reduction in viscous dissipation. For TAVR, dissipation was highest during early systole when valve deformation was the greatest, suggesting that an efficient valve opening may reduce energy loss. Our study demonstrates that such patient-specific modelling frameworks can be used to improve predictability and in the planning of AV interventions. The Royal Society 2022-02-09 /pmc/articles/PMC8826300/ /pubmed/35154799 http://dx.doi.org/10.1098/rsos.211694 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Engineering
Govindarajan, Vijay
Kolanjiyil, Arun
Johnson, Nils P.
Kim, Hyunggun
Chandran, Krishnan B.
McPherson, David D.
Improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy
title Improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy
title_full Improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy
title_fullStr Improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy
title_full_unstemmed Improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy
title_short Improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy
title_sort improving transcatheter aortic valve interventional predictability via fluid–structure interaction modelling using patient-specific anatomy
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826300/
https://www.ncbi.nlm.nih.gov/pubmed/35154799
http://dx.doi.org/10.1098/rsos.211694
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