Predicting Patient-ventilator Asynchronies with Hidden Markov Models
| العنوان: | Predicting Patient-ventilator Asynchronies with Hidden Markov Models |
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| المؤلفون: | Carles Subirà, Candelaria de Haro, Robert M. Kacmarek, Jaume Montanya, Josefina López-Aguilar, Bernat Sales, Gemma Gomà, Rudys Magrans, Lluis Blanch, Rafael Fernandez, Yaroslav Marchuk |
| المصدر: | Scientific Reports Scientific Reports, Vol 8, Iss 1, Pp 1-7 (2018) Recercat. Dipósit de la Recerca de Catalunya instname |
| بيانات النشر: | Nature Publishing Group UK, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Male, medicine.medical_specialty, Statistical methods, Computer science, medicine.medical_treatment, Critical Illness, lcsh:Medicine, Estadística, Biostatistics, Article, 03 medical and health sciences, Medicina preventiva, 0302 clinical medicine, Physical medicine and rehabilitation, medicine, Humans, Hidden Markov model, lcsh:Science, Procesamiento de datos, Data mining, Poisson hidden Markov model, Aged, Monitoring, Physiologic, Mechanical ventilation, Multidisciplinary, Statistics, lcsh:R, 030208 emergency & critical care medicine, Ingeniería biomédica, Middle Aged, Respiration, Artificial, Asynchrony (computer programming), 030228 respiratory system, Enginyeria biomèdica, lcsh:Q, Female, Mineria de dades, Preventive Medicine, Pulmonary Ventilation, Biomedical engineering |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2045-2322 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99a498dabf34b61c070dc529b62c8a22Test http://europepmc.org/articles/PMC6279839Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....99a498dabf34b61c070dc529b62c8a22 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20452322 |
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