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Sensitivity Analysis of a Mathematical Model Simulating the Post-Hepatectomy Hemodynamics Response
Recently a lumped-parameter model of the cardiovascular system was proposed to simulate the hemodynamics response to partial hepatectomy and evaluate the risk of portal hypertension (PHT) due to this surgery. Model parameters are tuned based on each patient data. This work focuses on a global sensit...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832106/ https://www.ncbi.nlm.nih.gov/pubmed/36326994 http://dx.doi.org/10.1007/s10439-022-03098-6 |
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author | Sala, Lorenzo Golse, Nicolas Joosten, Alexandre Vibert, Eric Vignon-Clementel, Irene |
author_facet | Sala, Lorenzo Golse, Nicolas Joosten, Alexandre Vibert, Eric Vignon-Clementel, Irene |
author_sort | Sala, Lorenzo |
collection | PubMed |
description | Recently a lumped-parameter model of the cardiovascular system was proposed to simulate the hemodynamics response to partial hepatectomy and evaluate the risk of portal hypertension (PHT) due to this surgery. Model parameters are tuned based on each patient data. This work focuses on a global sensitivity analysis (SA) study of such model to better understand the main drivers of the clinical outputs of interest. The analysis suggests which parameters should be considered patient-specific and which can be assumed constant without losing in accuracy in the predictions. While performing the SA, model outputs need to be constrained to physiological ranges. An innovative approach exploits the features of the polynomial chaos expansion method to reduce the overall computational cost. The computed results give new insights on how to improve the calibration of some model parameters. Moreover the final parameter distributions enable the creation of a virtual population available for future works. Although this work is focused on partial hepatectomy, the pipeline can be applied to other cardiovascular hemodynamics models to gain insights for patient-specific parameterization and to define a physiologically relevant virtual population. |
format | Online Article Text |
id | pubmed-9832106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-98321062023-01-12 Sensitivity Analysis of a Mathematical Model Simulating the Post-Hepatectomy Hemodynamics Response Sala, Lorenzo Golse, Nicolas Joosten, Alexandre Vibert, Eric Vignon-Clementel, Irene Ann Biomed Eng S.I.: Modeling for Advancing Regulatory Science Recently a lumped-parameter model of the cardiovascular system was proposed to simulate the hemodynamics response to partial hepatectomy and evaluate the risk of portal hypertension (PHT) due to this surgery. Model parameters are tuned based on each patient data. This work focuses on a global sensitivity analysis (SA) study of such model to better understand the main drivers of the clinical outputs of interest. The analysis suggests which parameters should be considered patient-specific and which can be assumed constant without losing in accuracy in the predictions. While performing the SA, model outputs need to be constrained to physiological ranges. An innovative approach exploits the features of the polynomial chaos expansion method to reduce the overall computational cost. The computed results give new insights on how to improve the calibration of some model parameters. Moreover the final parameter distributions enable the creation of a virtual population available for future works. Although this work is focused on partial hepatectomy, the pipeline can be applied to other cardiovascular hemodynamics models to gain insights for patient-specific parameterization and to define a physiologically relevant virtual population. Springer International Publishing 2022-11-03 2023 /pmc/articles/PMC9832106/ /pubmed/36326994 http://dx.doi.org/10.1007/s10439-022-03098-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | S.I.: Modeling for Advancing Regulatory Science Sala, Lorenzo Golse, Nicolas Joosten, Alexandre Vibert, Eric Vignon-Clementel, Irene Sensitivity Analysis of a Mathematical Model Simulating the Post-Hepatectomy Hemodynamics Response |
title | Sensitivity Analysis of a Mathematical Model Simulating the Post-Hepatectomy Hemodynamics Response |
title_full | Sensitivity Analysis of a Mathematical Model Simulating the Post-Hepatectomy Hemodynamics Response |
title_fullStr | Sensitivity Analysis of a Mathematical Model Simulating the Post-Hepatectomy Hemodynamics Response |
title_full_unstemmed | Sensitivity Analysis of a Mathematical Model Simulating the Post-Hepatectomy Hemodynamics Response |
title_short | Sensitivity Analysis of a Mathematical Model Simulating the Post-Hepatectomy Hemodynamics Response |
title_sort | sensitivity analysis of a mathematical model simulating the post-hepatectomy hemodynamics response |
topic | S.I.: Modeling for Advancing Regulatory Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832106/ https://www.ncbi.nlm.nih.gov/pubmed/36326994 http://dx.doi.org/10.1007/s10439-022-03098-6 |
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