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Title:	Predicting patient-ventilator asynchronies with hidden markov models
Author:	Marchuk, Yaroslav; Magrans, Rudys; Sales, Bernat; Montanyà, Jaume; López-Aguilar, Josefina; De Haro, Candelaria; Gomà Fernández, Gemma; Subirà Cuyàs, Carles; Fernández Fernández, Rafael; Kacmarek, Robert M.; Blanch, Lluís
Notes:	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.
Subject(s):	-Enginyeria biomèdica -Mineria de dades -Medicina preventiva -Estadística -Ingeniería biomédica -Procesamiento de datos -Medicina preventiva -Estadística -Biomedical engineering -Data mining -Preventive Medicine -Statistical methods -Statistics -61
Rights:	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/. http://creativecommons.org/licenses/by/4.0/
Document type:	Article Article - Accepted version
Published by:	Springer Nature
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