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An effective pressure-flow characterization of respiratory asynchronies in mechanical ventilation.
Casagrande, Alberto; Quintavalle, Francesco; Fernandez, Rafael; Blanch, Lluis; Ferluga, Massimo; Lena, Enrico; Fabris, Francesco; Lucangelo, Umberto.
Afiliación
  • Casagrande A; Departments of Mathematics and Geosciences, University of Trieste, Via Valerio, 12/1, 34127, Trieste, Italy. acasagrande@units.it.
  • Quintavalle F; DAI Emergenza Urgenza ed Accettazione, Azienda Sanitaria Univeritaria integrata di Trieste, Trieste, Italy.
  • Fernandez R; CIBER Enfermedades Respiratorias, ICU, Hospital Sant Joan de Déu, Fundació Althaia, Manresa, Spain.
  • Blanch L; Critical Care Center, ParcTaulì Hospital Universitari, Institut d'Investigaciò i Innovaciò Parc Taulì I3PT, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Ferluga M; CIBER Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain.
  • Lena E; DAI Emergenza Urgenza ed Accettazione, Azienda Sanitaria Univeritaria integrata di Trieste, Trieste, Italy.
  • Fabris F; DAI Emergenza Urgenza ed Accettazione, Azienda Sanitaria Univeritaria integrata di Trieste, Trieste, Italy.
  • Lucangelo U; Departments of Mathematics and Geosciences, University of Trieste, Via Valerio, 12/1, 34127, Trieste, Italy.
J Clin Monit Comput ; 35(2): 289-296, 2021 Apr.
Article en En | MEDLINE | ID: mdl-31993892
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Respiración Artificial / Ventiladores Mecánicos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Clin Monit Comput Asunto de la revista: INFORMATICA MEDICA / MEDICINA Año: 2021 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Respiración Artificial / Ventiladores Mecánicos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Clin Monit Comput Asunto de la revista: INFORMATICA MEDICA / MEDICINA Año: 2021 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Países Bajos