Predicting patient-ventilator asynchronies with hidden markov models

Home | About RECERCAT | Contact

Català | Castellano

All of RECERCAT

By Communities &
Collections By Defense Date By Authors By Titles By Subject

This Collection

By Defense Date By Authors By Titles By Subject

Statistics

View Statistics All RECERCAT

My RECERCAT

Other repositories directory

RECERCAT Home > Universitat Internacional de Catalunya > Recerca > Articles > Ciències de la Salut > View document

To access the full text documents, please follow this link: http://hdl.handle.net/20.500.12328/1406

dc.contributor.author	Marchuk, Yaroslav
dc.contributor.author	Magrans, Rudys
dc.contributor.author	Sales, Bernat
dc.contributor.author	Montanyà, Jaume
dc.contributor.author	López-Aguilar, Josefina
dc.contributor.author	De Haro, Candelaria
dc.contributor.author	Gomà Fernández, Gemma
dc.contributor.author	Subirà Cuyàs, Carles
dc.contributor.author	Fernández Fernández, Rafael
dc.contributor.author	Kacmarek, Robert M.
dc.contributor.author	Blanch, Lluís
dc.date	2019-12-23T15:39:11Z
dc.date	2019-12-23T15:39:11Z
dc.date	2018-12-04
dc.identifier	Marchuk, Yaroslav; Magrans, Rudys; Sales, Bernat [et al.]. Predicting patient-ventilator asynchronies with hidden markov models. Scientific Reports, 2018, vol. 8, p. 1-7. Disponible en: <https://www.nature.com/articles/s41598-018-36011-0#rightslink>. Fecha de acceso: 23 dic. 2019. DOI: 10.1038/s41598-018-36011-0
dc.identifier	2045-2322
dc.identifier	https://dx.doi.org/10.1038/s41598-018-36011-0
dc.identifier.uri	http://hdl.handle.net/20.500.12328/1406
dc.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.
dc.format	7
dc.format	application/pdf
dc.language	eng
dc.publisher	Springer Nature
dc.relation	Scientific Reports
dc.relation	8;
dc.relation	info:eu-repo/grantAgreement/ES/2PE/RTC-2017-6193-1
dc.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/.
dc.rights	http://creativecommons.org/licenses/by/4.0/
dc.subject	Enginyeria biomèdica
dc.subject	Mineria de dades
dc.subject	Medicina preventiva
dc.subject	Estadística
dc.subject	Ingeniería biomédica
dc.subject	Procesamiento de datos
dc.subject	Medicina preventiva
dc.subject	Estadística
dc.subject	Biomedical engineering
dc.subject	Data mining
dc.subject	Preventive Medicine
dc.subject	Statistical methods
dc.subject	Statistics
dc.subject	61
dc.title	Predicting patient-ventilator asynchronies with hidden markov models
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/acceptedVersion

Show simple item record

All of RECERCAT

This Collection

Statistics

My RECERCAT

Related documents

Other documents of the same author