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A Comparative Data-Based Modeling Study on Respiratory CO(2) Gas Exchange during Mechanical Ventilation
The goal of this study is to derive a minimally complex but credible model of respiratory CO(2) gas exchange that may be used in systematic design and pilot testing of closed-loop end-tidal CO(2) controllers in mechanical ventilation. We first derived a candidate model that captures the essential me...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4737892/ https://www.ncbi.nlm.nih.gov/pubmed/26870728 http://dx.doi.org/10.3389/fbioe.2016.00008 |
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author | Kim, Chang-Sei Ansermino, J. Mark Hahn, Jin-Oh |
author_facet | Kim, Chang-Sei Ansermino, J. Mark Hahn, Jin-Oh |
author_sort | Kim, Chang-Sei |
collection | PubMed |
description | The goal of this study is to derive a minimally complex but credible model of respiratory CO(2) gas exchange that may be used in systematic design and pilot testing of closed-loop end-tidal CO(2) controllers in mechanical ventilation. We first derived a candidate model that captures the essential mechanisms involved in the respiratory CO(2) gas exchange process. Then, we simplified the candidate model to derive two lower-order candidate models. We compared these candidate models for predictive capability and reliability using experimental data collected from 25 pediatric subjects undergoing dynamically varying mechanical ventilation during surgical procedures. A two-compartment model equipped with transport delay to account for CO(2) delivery between the lungs and the tissues showed modest but statistically significant improvement in predictive capability over the same model without transport delay. Aggregating the lungs and the tissues into a single compartment further degraded the predictive fidelity of the model. In addition, the model equipped with transport delay demonstrated superior reliability to the one without transport delay. Further, the respiratory parameters derived from the model equipped with transport delay, but not the one without transport delay, were physiologically plausible. The results suggest that gas transport between the lungs and the tissues must be taken into account to accurately reproduce the respiratory CO(2) gas exchange process under conditions of wide-ranging and dynamically varying mechanical ventilation conditions. |
format | Online Article Text |
id | pubmed-4737892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-47378922016-02-11 A Comparative Data-Based Modeling Study on Respiratory CO(2) Gas Exchange during Mechanical Ventilation Kim, Chang-Sei Ansermino, J. Mark Hahn, Jin-Oh Front Bioeng Biotechnol Bioengineering and Biotechnology The goal of this study is to derive a minimally complex but credible model of respiratory CO(2) gas exchange that may be used in systematic design and pilot testing of closed-loop end-tidal CO(2) controllers in mechanical ventilation. We first derived a candidate model that captures the essential mechanisms involved in the respiratory CO(2) gas exchange process. Then, we simplified the candidate model to derive two lower-order candidate models. We compared these candidate models for predictive capability and reliability using experimental data collected from 25 pediatric subjects undergoing dynamically varying mechanical ventilation during surgical procedures. A two-compartment model equipped with transport delay to account for CO(2) delivery between the lungs and the tissues showed modest but statistically significant improvement in predictive capability over the same model without transport delay. Aggregating the lungs and the tissues into a single compartment further degraded the predictive fidelity of the model. In addition, the model equipped with transport delay demonstrated superior reliability to the one without transport delay. Further, the respiratory parameters derived from the model equipped with transport delay, but not the one without transport delay, were physiologically plausible. The results suggest that gas transport between the lungs and the tissues must be taken into account to accurately reproduce the respiratory CO(2) gas exchange process under conditions of wide-ranging and dynamically varying mechanical ventilation conditions. Frontiers Media S.A. 2016-02-03 /pmc/articles/PMC4737892/ /pubmed/26870728 http://dx.doi.org/10.3389/fbioe.2016.00008 Text en Copyright © 2016 Kim, Ansermino and Hahn. http://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) or licensor 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 Kim, Chang-Sei Ansermino, J. Mark Hahn, Jin-Oh A Comparative Data-Based Modeling Study on Respiratory CO(2) Gas Exchange during Mechanical Ventilation |
title | A Comparative Data-Based Modeling Study on Respiratory CO(2) Gas Exchange during Mechanical Ventilation |
title_full | A Comparative Data-Based Modeling Study on Respiratory CO(2) Gas Exchange during Mechanical Ventilation |
title_fullStr | A Comparative Data-Based Modeling Study on Respiratory CO(2) Gas Exchange during Mechanical Ventilation |
title_full_unstemmed | A Comparative Data-Based Modeling Study on Respiratory CO(2) Gas Exchange during Mechanical Ventilation |
title_short | A Comparative Data-Based Modeling Study on Respiratory CO(2) Gas Exchange during Mechanical Ventilation |
title_sort | comparative data-based modeling study on respiratory co(2) gas exchange during mechanical ventilation |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4737892/ https://www.ncbi.nlm.nih.gov/pubmed/26870728 http://dx.doi.org/10.3389/fbioe.2016.00008 |
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