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Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit

BACKGROUND: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and do not provide unique patient-specific solutions. Model-based methods...

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Autores principales: Sundaresan, Ashwath, Chase, J Geoffrey, Shaw, Geoffrey M, Chiew, Yeong Shiong, Desaive, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3167768/
https://www.ncbi.nlm.nih.gov/pubmed/21794116
http://dx.doi.org/10.1186/1475-925X-10-64
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author Sundaresan, Ashwath
Chase, J Geoffrey
Shaw, Geoffrey M
Chiew, Yeong Shiong
Desaive, Thomas
author_facet Sundaresan, Ashwath
Chase, J Geoffrey
Shaw, Geoffrey M
Chiew, Yeong Shiong
Desaive, Thomas
author_sort Sundaresan, Ashwath
collection PubMed
description BACKGROUND: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and do not provide unique patient-specific solutions. Model-based methods offer a novel way of using non-invasive pressure-volume (PV) measurements to estimate patient recruitability. This paper examines the clinical viability of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, assess the disease state and response over time. METHODS: Ten patients with acute lung injury or ARDS underwent incremental PEEP recruitment manoeuvres. PV data was measured at increments of 5 cmH(2)O and fitted to the recruitment model. Inspiratory and expiratory breath holds were performed to measure airway resistance and auto-PEEP. Three model-based metrics are used to optimise PEEP based on opening pressures, closing pressures and net recruitment. ARDS status was assessed by model parameters capturing recruitment and compliance. RESULTS: Median model fitting error across all patients for inflation and deflation was 2.8% and 1.02% respectively with all patients experiencing auto-PEEP. In all three metrics' cases, model-based optimal PEEP was higher than clinically selected PEEP. Two patients underwent multiple recruitment manoeuvres over time and model metrics reflected and tracked the state or their ARDS. CONCLUSIONS: For ARDS patients, the model-based method presented in this paper provides a unique, non-invasive method to select optimal patient-specific PEEP. In addition, the model has the capability to assess disease state over time using these same models and methods.
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spelling pubmed-31677682011-09-07 Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit Sundaresan, Ashwath Chase, J Geoffrey Shaw, Geoffrey M Chiew, Yeong Shiong Desaive, Thomas Biomed Eng Online Research BACKGROUND: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and do not provide unique patient-specific solutions. Model-based methods offer a novel way of using non-invasive pressure-volume (PV) measurements to estimate patient recruitability. This paper examines the clinical viability of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, assess the disease state and response over time. METHODS: Ten patients with acute lung injury or ARDS underwent incremental PEEP recruitment manoeuvres. PV data was measured at increments of 5 cmH(2)O and fitted to the recruitment model. Inspiratory and expiratory breath holds were performed to measure airway resistance and auto-PEEP. Three model-based metrics are used to optimise PEEP based on opening pressures, closing pressures and net recruitment. ARDS status was assessed by model parameters capturing recruitment and compliance. RESULTS: Median model fitting error across all patients for inflation and deflation was 2.8% and 1.02% respectively with all patients experiencing auto-PEEP. In all three metrics' cases, model-based optimal PEEP was higher than clinically selected PEEP. Two patients underwent multiple recruitment manoeuvres over time and model metrics reflected and tracked the state or their ARDS. CONCLUSIONS: For ARDS patients, the model-based method presented in this paper provides a unique, non-invasive method to select optimal patient-specific PEEP. In addition, the model has the capability to assess disease state over time using these same models and methods. BioMed Central 2011-07-27 /pmc/articles/PMC3167768/ /pubmed/21794116 http://dx.doi.org/10.1186/1475-925X-10-64 Text en Copyright ©2011 Sundaresan et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Sundaresan, Ashwath
Chase, J Geoffrey
Shaw, Geoffrey M
Chiew, Yeong Shiong
Desaive, Thomas
Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit
title Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit
title_full Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit
title_fullStr Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit
title_full_unstemmed Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit
title_short Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit
title_sort model-based optimal peep in mechanically ventilated ards patients in the intensive care unit
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3167768/
https://www.ncbi.nlm.nih.gov/pubmed/21794116
http://dx.doi.org/10.1186/1475-925X-10-64
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