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A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms
Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. Th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8517122/ https://www.ncbi.nlm.nih.gov/pubmed/34658911 http://dx.doi.org/10.3389/fphys.2021.724046 |
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author | Agrawal, Deepak K. Smith, Bradford J. Sottile, Peter D. Albers, David J. |
author_facet | Agrawal, Deepak K. Smith, Bradford J. Sottile, Peter D. Albers, David J. |
author_sort | Agrawal, Deepak K. |
collection | PubMed |
description | Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome. |
format | Online Article Text |
id | pubmed-8517122 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85171222021-10-16 A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms Agrawal, Deepak K. Smith, Bradford J. Sottile, Peter D. Albers, David J. Front Physiol Physiology Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome. Frontiers Media S.A. 2021-10-01 /pmc/articles/PMC8517122/ /pubmed/34658911 http://dx.doi.org/10.3389/fphys.2021.724046 Text en Copyright © 2021 Agrawal, Smith, Sottile and Albers. https://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) and the copyright owner(s) 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 | Physiology Agrawal, Deepak K. Smith, Bradford J. Sottile, Peter D. Albers, David J. A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms |
title | A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms |
title_full | A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms |
title_fullStr | A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms |
title_full_unstemmed | A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms |
title_short | A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms |
title_sort | damaged-informed lung ventilator model for ventilator waveforms |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8517122/ https://www.ncbi.nlm.nih.gov/pubmed/34658911 http://dx.doi.org/10.3389/fphys.2021.724046 |
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