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Predicting Patient-ventilator Asynchronies with Hidden Markov Models

In mechanical ventilation, it is paramount to ensure the patient’s ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventila...

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Autores principales: Marchuk, Yaroslav, Magrans, Rudys, Sales, Bernat, Montanya, Jaume, López-Aguilar, Josefina, de Haro, Candelaria, Gomà, Gemma, Subirà, Carles, Fernández, Rafael, Kacmarek, Robert M., Blanch, Lluis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279839/
https://www.ncbi.nlm.nih.gov/pubmed/30514876
http://dx.doi.org/10.1038/s41598-018-36011-0
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author Marchuk, Yaroslav
Magrans, Rudys
Sales, Bernat
Montanya, Jaume
López-Aguilar, Josefina
de Haro, Candelaria
Gomà, Gemma
Subirà, Carles
Fernández, Rafael
Kacmarek, Robert M.
Blanch, Lluis
author_facet Marchuk, Yaroslav
Magrans, Rudys
Sales, Bernat
Montanya, Jaume
López-Aguilar, Josefina
de Haro, Candelaria
Gomà, Gemma
Subirà, Carles
Fernández, Rafael
Kacmarek, Robert M.
Blanch, Lluis
author_sort Marchuk, Yaroslav
collection PubMed
description In mechanical ventilation, it is paramount to ensure the patient’s ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) – z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction.
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spelling pubmed-62798392018-12-07 Predicting Patient-ventilator Asynchronies with Hidden Markov Models Marchuk, Yaroslav Magrans, Rudys Sales, Bernat Montanya, Jaume López-Aguilar, Josefina de Haro, Candelaria Gomà, Gemma Subirà, Carles Fernández, Rafael Kacmarek, Robert M. Blanch, Lluis Sci Rep Article In mechanical ventilation, it is paramount to ensure the patient’s ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) – z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction. Nature Publishing Group UK 2018-12-04 /pmc/articles/PMC6279839/ /pubmed/30514876 http://dx.doi.org/10.1038/s41598-018-36011-0 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Marchuk, Yaroslav
Magrans, Rudys
Sales, Bernat
Montanya, Jaume
López-Aguilar, Josefina
de Haro, Candelaria
Gomà, Gemma
Subirà, Carles
Fernández, Rafael
Kacmarek, Robert M.
Blanch, Lluis
Predicting Patient-ventilator Asynchronies with Hidden Markov Models
title Predicting Patient-ventilator Asynchronies with Hidden Markov Models
title_full Predicting Patient-ventilator Asynchronies with Hidden Markov Models
title_fullStr Predicting Patient-ventilator Asynchronies with Hidden Markov Models
title_full_unstemmed Predicting Patient-ventilator Asynchronies with Hidden Markov Models
title_short Predicting Patient-ventilator Asynchronies with Hidden Markov Models
title_sort predicting patient-ventilator asynchronies with hidden markov models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279839/
https://www.ncbi.nlm.nih.gov/pubmed/30514876
http://dx.doi.org/10.1038/s41598-018-36011-0
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