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Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation

Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge....

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Autores principales: Sarlabous, Leonardo, Aquino-Esperanza, José, Magrans, Rudys, de Haro, Candelaria, López-Aguilar, Josefina, Subirà, Carles, Batlle, Montserrat, Rué, Montserrat, Gomà, Gemma, Ochagavia, Ana, Fernández, Rafael, Blanch, Lluís
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431581/
https://www.ncbi.nlm.nih.gov/pubmed/32807815
http://dx.doi.org/10.1038/s41598-020-70814-4
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author Sarlabous, Leonardo
Aquino-Esperanza, José
Magrans, Rudys
de Haro, Candelaria
López-Aguilar, Josefina
Subirà, Carles
Batlle, Montserrat
Rué, Montserrat
Gomà, Gemma
Ochagavia, Ana
Fernández, Rafael
Blanch, Lluís
author_facet Sarlabous, Leonardo
Aquino-Esperanza, José
Magrans, Rudys
de Haro, Candelaria
López-Aguilar, Josefina
Subirà, Carles
Batlle, Montserrat
Rué, Montserrat
Gomà, Gemma
Ochagavia, Ana
Fernández, Rafael
Blanch, Lluís
author_sort Sarlabous, Leonardo
collection PubMed
description Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm’s performance was compared versus the gold standard (the ventilator’s waveform recordings for CP-VI were scored visually by three experts; Fleiss’ kappa = 0.90 (0.87–0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient’s own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78–0.86) and 0.78 (0.78–0.85), and accuracies of 0.93 (0.89–0.93) and 0.89 (0.89–0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.
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spelling pubmed-74315812020-08-18 Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation Sarlabous, Leonardo Aquino-Esperanza, José Magrans, Rudys de Haro, Candelaria López-Aguilar, Josefina Subirà, Carles Batlle, Montserrat Rué, Montserrat Gomà, Gemma Ochagavia, Ana Fernández, Rafael Blanch, Lluís Sci Rep Article Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm’s performance was compared versus the gold standard (the ventilator’s waveform recordings for CP-VI were scored visually by three experts; Fleiss’ kappa = 0.90 (0.87–0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient’s own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78–0.86) and 0.78 (0.78–0.85), and accuracies of 0.93 (0.89–0.93) and 0.89 (0.89–0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications. Nature Publishing Group UK 2020-08-17 /pmc/articles/PMC7431581/ /pubmed/32807815 http://dx.doi.org/10.1038/s41598-020-70814-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Sarlabous, Leonardo
Aquino-Esperanza, José
Magrans, Rudys
de Haro, Candelaria
López-Aguilar, Josefina
Subirà, Carles
Batlle, Montserrat
Rué, Montserrat
Gomà, Gemma
Ochagavia, Ana
Fernández, Rafael
Blanch, Lluís
Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
title Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
title_full Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
title_fullStr Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
title_full_unstemmed Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
title_short Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
title_sort development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431581/
https://www.ncbi.nlm.nih.gov/pubmed/32807815
http://dx.doi.org/10.1038/s41598-020-70814-4
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