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A validated computational framework to predict outcomes in TAVI

Transcatheter aortic valve implantation (TAVI) still presents complications: paravalvular leakage (PVL) and onset of conduction abnormalities leading to permanent pacemaker implantation. Our aim was testing a validated patient-specific computational framework for prediction of TAVI outcomes and poss...

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Autores principales: Bosi, Giorgia M., Capelli, Claudio, Cheang, Mun Hong, Delahunty, Nicola, Mullen, Michael, Taylor, Andrew M., Schievano, Silvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303192/
https://www.ncbi.nlm.nih.gov/pubmed/32555300
http://dx.doi.org/10.1038/s41598-020-66899-6
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author Bosi, Giorgia M.
Capelli, Claudio
Cheang, Mun Hong
Delahunty, Nicola
Mullen, Michael
Taylor, Andrew M.
Schievano, Silvia
author_facet Bosi, Giorgia M.
Capelli, Claudio
Cheang, Mun Hong
Delahunty, Nicola
Mullen, Michael
Taylor, Andrew M.
Schievano, Silvia
author_sort Bosi, Giorgia M.
collection PubMed
description Transcatheter aortic valve implantation (TAVI) still presents complications: paravalvular leakage (PVL) and onset of conduction abnormalities leading to permanent pacemaker implantation. Our aim was testing a validated patient-specific computational framework for prediction of TAVI outcomes and possible complications. Twenty-eight TAVI patients (14 SapienXT and 14 CoreValve) were retrospectively selected. Pre-procedural CT images were post-processed to create 3D patient-specific implantation sites. The procedures were simulated with finite element analysis. Simulations’ results were compared against post-procedural clinical fluoroscopy and echocardiography images. The computational model was in good agreement with clinical findings: the overall stent diameter difference was 2.6% and PVL was correctly identified with a post-processing algorithm in 83% of cases. Strains in the implantation site were studied to assess the risk of conduction system disturbance and were found highest in the patient who required pacemaker implantation. This study suggests that computational tool could support safe planning and broadening of TAVI.
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spelling pubmed-73031922020-06-22 A validated computational framework to predict outcomes in TAVI Bosi, Giorgia M. Capelli, Claudio Cheang, Mun Hong Delahunty, Nicola Mullen, Michael Taylor, Andrew M. Schievano, Silvia Sci Rep Article Transcatheter aortic valve implantation (TAVI) still presents complications: paravalvular leakage (PVL) and onset of conduction abnormalities leading to permanent pacemaker implantation. Our aim was testing a validated patient-specific computational framework for prediction of TAVI outcomes and possible complications. Twenty-eight TAVI patients (14 SapienXT and 14 CoreValve) were retrospectively selected. Pre-procedural CT images were post-processed to create 3D patient-specific implantation sites. The procedures were simulated with finite element analysis. Simulations’ results were compared against post-procedural clinical fluoroscopy and echocardiography images. The computational model was in good agreement with clinical findings: the overall stent diameter difference was 2.6% and PVL was correctly identified with a post-processing algorithm in 83% of cases. Strains in the implantation site were studied to assess the risk of conduction system disturbance and were found highest in the patient who required pacemaker implantation. This study suggests that computational tool could support safe planning and broadening of TAVI. Nature Publishing Group UK 2020-06-18 /pmc/articles/PMC7303192/ /pubmed/32555300 http://dx.doi.org/10.1038/s41598-020-66899-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Bosi, Giorgia M.
Capelli, Claudio
Cheang, Mun Hong
Delahunty, Nicola
Mullen, Michael
Taylor, Andrew M.
Schievano, Silvia
A validated computational framework to predict outcomes in TAVI
title A validated computational framework to predict outcomes in TAVI
title_full A validated computational framework to predict outcomes in TAVI
title_fullStr A validated computational framework to predict outcomes in TAVI
title_full_unstemmed A validated computational framework to predict outcomes in TAVI
title_short A validated computational framework to predict outcomes in TAVI
title_sort validated computational framework to predict outcomes in tavi
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303192/
https://www.ncbi.nlm.nih.gov/pubmed/32555300
http://dx.doi.org/10.1038/s41598-020-66899-6
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