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Pàgina inicial > Articles > Articles publicats > Predicting Patient-ventilator Asynchronies with Hidden Markov Models |
Data: | 2018 |
Resum: | 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. |
Ajuts: | 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 |
Drets: | 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. |
Llengua: | Anglès |
Document: | Article ; recerca ; Versió publicada |
Publicat a: | Scientific reports, Vol. 8 (december 2018) , ISSN 2045-2322 |
7 p, 1.0 MB |