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Título:	Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
Autor/a:	Sarlabous, Leonardo; Aquino‑Esperanza, José; Magrans, Rudys; De Haro, Candelaria; López-Aguilar, Josefina; Subirà Cuyàs, Carles; Batlle, Montserrat; Rué, Montserrat; Gomà Fernández, Gemma; Ochagavia, Ana; Fernández Fernández, Rafael; Blanch, Lluís
Notas:	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.
Materia(s):	-Enginyeria biomèdica -Marcadors bioquímics -Bases de dades -Aprenentatge automàtic -Estadística -Ingeniería biomédica -Marcadores bioquímicos -Bases de datos -Aprendizaje automático -Estadística -Biomedical engineering -Biochemical markers -Database -Machine learning -Statistics -61
Derechos:	© The Author(s) 2020. Tis 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. Te 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/. http://creativecommons.org/licenses/by/4.0/
Tipo de documento:	Artículo Artículo - Versión publicada
Editor:	Springer Nature
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