Predicting Patient-ventilator Asynchronies with Hidden Markov Models
Marchuk, Yaroslav (Better Care (Espanya))
Magrans, Rudys (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Sales, Bernat (Better Care (Espanya))
Montanya, Jaume (Better Care (Espanya))
López-Aguilar, Josefina (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
de Haro, Candelaria (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Gomà, Gemma (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Subirà, Carles (Universitat Internacional de Catalunya)
Fernández, Rafael (Universitat Internacional de Catalunya)
Kacmarek, Robert M. (Massachusetts General Hospital (Boston))
Blanch, Lluís (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))
Universitat Autònoma de Barcelona

Date:	2018
Abstract:	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.
Grants:	Instituto de Salud Carlos III PI09/91074 Ministerio de Economía, Industria y Competitividad PI13/02204 Ministerio de Ciencia e Innovación TSI-020302-2008-38 Ministerio de Ciencia, Innovación y Universidades RTC-2017-6193-1
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Published in:	Scientific reports, Vol. 8 (december 2018) , ISSN 2045-2322

DOI: 10.1038/s41598-018-36011-0
PMID: 30514876

7 p, 1.0 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Parc Taulí Research and Innovation Institute (I3PT
Articles > Research articles
Articles > Published articles