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An effective pressure–flow characterization of respiratory asynchronies in mechanical ventilation

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Abstract

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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Funding

This work is partially supported by INdAM/GNCS. Lluis Blanch is supported in part by Plan Avanza TSI-020302-2008-38, Ministerio de Industria, Turismo y Comercio (Spain) and Ministerio de Ciencia, Innovación y Universidades (Spain) and also Gruppo Nazionale per l'Analisi Matematica, la Probabilità e le loro Applicazioni (Grant No. Logic Programming for early detection of pancreatic cancer).

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Authors and Affiliations

  1. Departments of Mathematics and Geosciences, University of Trieste, Via Valerio, 12/1, 34127, Trieste, Italy

    Alberto Casagrande & Francesco Fabris

  2. DAI Emergenza Urgenza ed Accettazione, Azienda Sanitaria Univeritaria integrata di Trieste, Trieste, Italy

    Francesco Quintavalle, Massimo Ferluga, Enrico Lena & Umberto Lucangelo

  3. Critical Care Center, ParcTaulì Hospital Universitari, Institut d’Investigaciò i Innovaciò Parc Taulì I3PT, Universitat Autònoma de Barcelona, Barcelona, Spain

    Lluis Blanch

  4. CIBER Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain

    Lluis Blanch

  5. CIBER Enfermedades Respiratorias, ICU, Hospital Sant Joan de Déu, Fundació Althaia, Manresa, Spain

    Rafael Fernandez

Authors
  1. Alberto Casagrande
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  2. Francesco Quintavalle
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  3. Rafael Fernandez
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  4. Lluis Blanch
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  5. Massimo Ferluga
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  6. Enrico Lena
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  7. Francesco Fabris
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  8. Umberto Lucangelo
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Corresponding author

Correspondence to Alberto Casagrande.

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Conflict of interest

Lluis Blanch is inventor of one Corporació Sanitaria Parc Taulí owned US patent: “A Method and system for managed related patient parameters provided by a monitoring device”, US Patent No. 12/538,940. Lluis Blanch is a founder of BetterCare S.L.: a research and development company, spin-off of Corporació Sanitària Parc Taulí. Lluis Blanch is supported in part by Plan Avanza TSI-020302-2008-38, MCYIN and MITYC (Spain). Umberto Lucangelo own stock options of BetterCare S.L. Alberto Casagrande, Francesco Quintavalle, Francesco Fabris, and Umberto Lucangelo are inventors of the EU patent “Apparatus To Identify Respiratory Asynchronies In An Assisted Breathing Machine” European Patent no. EP 3308819 (A1) [29].

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Casagrande, A., Quintavalle, F., Fernandez, R. et al. An effective pressure–flow characterization of respiratory asynchronies in mechanical ventilation. J Clin Monit Comput 35, 289–296 (2021). https://doi.org/10.1007/s10877-020-00469-z

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  • DOI: https://doi.org/10.1007/s10877-020-00469-z

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