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An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle
In-vivo studies of pulmonary vascular disease and pulmonary hypertension (PH) have provided key insight into the progression of right ventricular (RV) dysfunction. Additional in-silico experiments using multiscale computational models have provided further details into biventricular mechanics and he...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524687/ https://www.ncbi.nlm.nih.gov/pubmed/36126091 http://dx.doi.org/10.1371/journal.pcbi.1010017 |
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author | Colebank, Mitchel J. Chesler, Naomi C. |
author_facet | Colebank, Mitchel J. Chesler, Naomi C. |
author_sort | Colebank, Mitchel J. |
collection | PubMed |
description | In-vivo studies of pulmonary vascular disease and pulmonary hypertension (PH) have provided key insight into the progression of right ventricular (RV) dysfunction. Additional in-silico experiments using multiscale computational models have provided further details into biventricular mechanics and hemodynamic function in the presence of PH, yet few have assessed whether model parameters are practically identifiable prior to data collection. Moreover, none have used modeling to devise synergistic experimental designs. To address this knowledge gap, we conduct a practical identifiability analysis of a multiscale cardiovascular model across four simulated experimental designs. We determine a set of parameters using a combination of Morris screening and local sensitivity analysis, and test for practical identifiability using profile likelihood-based confidence intervals. We employ Markov chain Monte Carlo (MCMC) techniques to quantify parameter and model forecast uncertainty in the presence of noise corrupted data. Our results show that model calibration to only RV pressure suffers from practical identifiability issues and suffers from large forecast uncertainty in output space. In contrast, parameter and model forecast uncertainty is substantially reduced once additional left ventricular (LV) pressure and volume data is included. A comparison between single point systolic and diastolic LV data and continuous, time-dependent LV pressure-volume data reveals that at least some quantitative data from both ventricles should be included for future experimental studies. |
format | Online Article Text |
id | pubmed-9524687 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-95246872022-10-01 An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle Colebank, Mitchel J. Chesler, Naomi C. PLoS Comput Biol Research Article In-vivo studies of pulmonary vascular disease and pulmonary hypertension (PH) have provided key insight into the progression of right ventricular (RV) dysfunction. Additional in-silico experiments using multiscale computational models have provided further details into biventricular mechanics and hemodynamic function in the presence of PH, yet few have assessed whether model parameters are practically identifiable prior to data collection. Moreover, none have used modeling to devise synergistic experimental designs. To address this knowledge gap, we conduct a practical identifiability analysis of a multiscale cardiovascular model across four simulated experimental designs. We determine a set of parameters using a combination of Morris screening and local sensitivity analysis, and test for practical identifiability using profile likelihood-based confidence intervals. We employ Markov chain Monte Carlo (MCMC) techniques to quantify parameter and model forecast uncertainty in the presence of noise corrupted data. Our results show that model calibration to only RV pressure suffers from practical identifiability issues and suffers from large forecast uncertainty in output space. In contrast, parameter and model forecast uncertainty is substantially reduced once additional left ventricular (LV) pressure and volume data is included. A comparison between single point systolic and diastolic LV data and continuous, time-dependent LV pressure-volume data reveals that at least some quantitative data from both ventricles should be included for future experimental studies. Public Library of Science 2022-09-20 /pmc/articles/PMC9524687/ /pubmed/36126091 http://dx.doi.org/10.1371/journal.pcbi.1010017 Text en © 2022 Colebank, Chesler https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Colebank, Mitchel J. Chesler, Naomi C. An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle |
title | An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle |
title_full | An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle |
title_fullStr | An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle |
title_full_unstemmed | An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle |
title_short | An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle |
title_sort | in-silico analysis of experimental designs to study ventricular function: a focus on the right ventricle |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524687/ https://www.ncbi.nlm.nih.gov/pubmed/36126091 http://dx.doi.org/10.1371/journal.pcbi.1010017 |
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