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

Abstract 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 ch...

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Autores principales:	Leonardo Sarlabous, José Aquino-Esperanza, Rudys Magrans, Candelaria de Haro, Josefina López-Aguilar, Carles Subirà, Montserrat Batlle, Montserrat Rué, Gemma Gomà, Ana Ochagavia, Rafael Fernández, Lluís Blanch
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bba6a7931fe740f496ba5ff618e35376
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spelling	oai:doaj.org-article:bba6a7931fe740f496ba5ff618e353762021-12-02T16:46:33ZDevelopment and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation10.1038/s41598-020-70814-42045-2322https://doaj.org/article/bba6a7931fe740f496ba5ff618e353762020-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-70814-4https://doaj.org/toc/2045-2322Abstract 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.Leonardo SarlabousJosé Aquino-EsperanzaRudys MagransCandelaria de HaroJosefina López-AguilarCarles SubiràMontserrat BatlleMontserrat RuéGemma GomàAna OchagaviaRafael FernándezLluís BlanchNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Leonardo Sarlabous José Aquino-Esperanza Rudys Magrans Candelaria de Haro Josefina López-Aguilar Carles Subirà Montserrat Batlle Montserrat Rué Gemma Gomà Ana Ochagavia Rafael Fernández Lluís Blanch Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
description	Abstract 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.
format	article
author	Leonardo Sarlabous José Aquino-Esperanza Rudys Magrans Candelaria de Haro Josefina López-Aguilar Carles Subirà Montserrat Batlle Montserrat Rué Gemma Gomà Ana Ochagavia Rafael Fernández Lluís Blanch
author_facet	Leonardo Sarlabous José Aquino-Esperanza Rudys Magrans Candelaria de Haro Josefina López-Aguilar Carles Subirà Montserrat Batlle Montserrat Rué Gemma Gomà Ana Ochagavia Rafael Fernández Lluís Blanch
author_sort	Leonardo Sarlabous
title	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_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_sort	development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/bba6a7931fe740f496ba5ff618e35376
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Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation

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