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An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung

Mechanical ventilation (MV) is a lifesaving therapy for patients with the acute respiratory distress syndrome. However, selecting the optimal MV settings is a difficult process as setting a high positive end-expiratory pressure (PEEP) value will improve oxygenation, but can produce ventilator induce...

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Autores principales: Knörzer, A., Docherty, P.D., Chiew, Y.S., Chase, J.G., Möller, K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IFAC. Published by Elsevier Ltd. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9121182/
http://dx.doi.org/10.3182/20140824-6-ZA-1003.01834
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author Knörzer, A.
Docherty, P.D.
Chiew, Y.S.
Chase, J.G.
Möller, K.
author_facet Knörzer, A.
Docherty, P.D.
Chiew, Y.S.
Chase, J.G.
Möller, K.
author_sort Knörzer, A.
collection PubMed
description Mechanical ventilation (MV) is a lifesaving therapy for patients with the acute respiratory distress syndrome. However, selecting the optimal MV settings is a difficult process as setting a high positive end-expiratory pressure (PEEP) value will improve oxygenation, but can produce ventilator induced lung injuries (VILI). To find a suitable value is patient specific and depends on different things like the underlying illness and the current state. In this study, a respiratory model that defined constant bronchial resistance and pressure-dependent variable elastance was fitted to pressure volume (PV) responses for 12 datasets of 10 acute respiratory distress syndrome (ARDS) patients which underwent a recruitment maneuver (RM) to open previous collapsed alveoli. We believe that the range of minimal elastance represents that range in which oxygenation can be improved by recruitment with reducing the risk of VILI. The first order model with a variable elastance (E(drs)) described by Chiew et al. (2011) was modified with a factor α to express added end-expiratory volume due to an increased PEEP. Model parameters were identified using a nonlinear least square method that optimized E(drs) agreement across PEEP-levels. The model yielded an increase in overlapping quality of pressure dependent E(drs)-curves. A best pressure range for PEEP could be identified in 9 of 12 datasets. The model could potentially provide a simple method of decision support at the bedside for clinicians and could prospectively an automated extend in mechanical ventilation devices.
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spelling pubmed-91211822022-05-20 An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung Knörzer, A. Docherty, P.D. Chiew, Y.S. Chase, J.G. Möller, K. IFAC Proceedings Volumes Article Mechanical ventilation (MV) is a lifesaving therapy for patients with the acute respiratory distress syndrome. However, selecting the optimal MV settings is a difficult process as setting a high positive end-expiratory pressure (PEEP) value will improve oxygenation, but can produce ventilator induced lung injuries (VILI). To find a suitable value is patient specific and depends on different things like the underlying illness and the current state. In this study, a respiratory model that defined constant bronchial resistance and pressure-dependent variable elastance was fitted to pressure volume (PV) responses for 12 datasets of 10 acute respiratory distress syndrome (ARDS) patients which underwent a recruitment maneuver (RM) to open previous collapsed alveoli. We believe that the range of minimal elastance represents that range in which oxygenation can be improved by recruitment with reducing the risk of VILI. The first order model with a variable elastance (E(drs)) described by Chiew et al. (2011) was modified with a factor α to express added end-expiratory volume due to an increased PEEP. Model parameters were identified using a nonlinear least square method that optimized E(drs) agreement across PEEP-levels. The model yielded an increase in overlapping quality of pressure dependent E(drs)-curves. A best pressure range for PEEP could be identified in 9 of 12 datasets. The model could potentially provide a simple method of decision support at the bedside for clinicians and could prospectively an automated extend in mechanical ventilation devices. IFAC. Published by Elsevier Ltd. 2014 2016-04-25 /pmc/articles/PMC9121182/ http://dx.doi.org/10.3182/20140824-6-ZA-1003.01834 Text en © 2014 IFAC Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Knörzer, A.
Docherty, P.D.
Chiew, Y.S.
Chase, J.G.
Möller, K.
An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung
title An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung
title_full An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung
title_fullStr An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung
title_full_unstemmed An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung
title_short An Extension to the First Order Model of Pulmonary Mechanics to Capture a Pressure dependent Elastance in the Human Lung
title_sort extension to the first order model of pulmonary mechanics to capture a pressure dependent elastance in the human lung
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9121182/
http://dx.doi.org/10.3182/20140824-6-ZA-1003.01834
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