An effective pressure-flow characterization of respiratory asynchronies in mechanical ventilation.
Automatic monitoring
Machine learning
Mechanical ventilator
Respiratory asynchrony
Journal
Journal of clinical monitoring and computing
ISSN: 1573-2614
Titre abrégé: J Clin Monit Comput
Pays: Netherlands
ID NLM: 9806357
Informations de publication
Date de publication:
Apr 2021
Apr 2021
Historique:
received:
13
08
2019
accepted:
22
01
2020
pubmed:
30
1
2020
medline:
29
10
2021
entrez:
30
1
2020
Statut:
ppublish
Résumé
Ineffective effort during expiration (IEE) occurs when there is a mismatch between the demand of a mechanically ventilated patient and the support delivered by a Mechanical ventilator during the expiration. This work presents a pressure-flow characterization for respiratory asynchronies and validates a machine-learning method, based on the presented characterization, to identify IEEs. 1500 breaths produced by 8 mechanically-ventilated patients were considered: 500 of them were included into the training set and the remaining 1000 into the test set. Each of them was evaluated by 3 experts and classified as either normal, artefact, or containing inspiratory, expiratory, or cycling-off asynchronies. A software implementing the proposed method was trained by using the experts' evaluations of the training set and used to identify IEEs in the test set. The outcomes were compared with a consensus of three expert evaluations. The software classified IEEs with sensitivity 0.904, specificity 0.995, accuracy 0.983, positive and negative predictive value 0.963 and 0.986, respectively. The Cohen's kappa is 0.983 (with 95% confidence interval (CI): [0.884, 0.962]). The pressure-flow characterization of respiratory cycles and the monitoring technique proposed in this work automatically identified IEEs in real-time in close agreement with the experts.
Identifiants
pubmed: 31993892
doi: 10.1007/s10877-020-00469-z
pii: 10.1007/s10877-020-00469-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
289-296Subventions
Organisme : Gruppo Nazionale per l'Analisi Matematica, la Probabilitá e le loro Applicazioni
ID : Logic Programming for early detection of pancreatic cancer
Organisme : Ministerio de Industria, Turismo y Comercio (ES) and Ministerio de Ciencia, Innovación y Universidades (ES)
ID : Plan Avanza TSI-020302-2008-38
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