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Automated detection and quantification of reverse triggering effort under mechanical ventilation

BACKGROUND: Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially harmful for the lung and the diaphragm, but its detection is challenging. Magnitude of effort generated by RT is currently unknown. Our obj...

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Autores principales: Pham, Tài, Montanya, Jaume, Telias, Irene, Piraino, Thomas, Magrans, Rudys, Coudroy, Rémi, Damiani, L. Felipe, Mellado Artigas, Ricard, Madorno, Matías, Blanch, Lluis, Brochard, Laurent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7883535/
https://www.ncbi.nlm.nih.gov/pubmed/33588912
http://dx.doi.org/10.1186/s13054-020-03387-3
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author Pham, Tài
Montanya, Jaume
Telias, Irene
Piraino, Thomas
Magrans, Rudys
Coudroy, Rémi
Damiani, L. Felipe
Mellado Artigas, Ricard
Madorno, Matías
Blanch, Lluis
Brochard, Laurent
author_facet Pham, Tài
Montanya, Jaume
Telias, Irene
Piraino, Thomas
Magrans, Rudys
Coudroy, Rémi
Damiani, L. Felipe
Mellado Artigas, Ricard
Madorno, Matías
Blanch, Lluis
Brochard, Laurent
author_sort Pham, Tài
collection PubMed
description BACKGROUND: Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially harmful for the lung and the diaphragm, but its detection is challenging. Magnitude of effort generated by RT is currently unknown. Our objective was to validate supervised methods for automatic detection of RT using only airway pressure (Paw) and flow. A secondary objective was to describe the magnitude of the efforts generated during RT. METHODS: We developed algorithms for detection of RT using Paw and flow waveforms. Experts having Paw, flow and esophageal pressure (Pes) assessed automatic detection accuracy by comparison against visual assessment. Muscular pressure (Pmus) was measured from Pes during RT, triggered breaths and ineffective efforts. RESULTS: Tracings from 20 hypoxemic patients were used (mean age 65 ± 12 years, 65% male, ICU survival 75%). RT was present in 24% of the breaths ranging from 0 (patients paralyzed or in pressure support ventilation) to 93.3%. Automatic detection accuracy was 95.5%: sensitivity 83.1%, specificity 99.4%, positive predictive value 97.6%, negative predictive value 95.0% and kappa index of 0.87. Pmus of RT ranged from 1.3 to 36.8 cmH(2)0, with a median of 8.7 cmH(2)0. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths. CONCLUSION: An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH(2)O with important variability between and within patients. TRIAL REGISTRATION: BEARDS, NCT03447288. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-020-03387-3.
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spelling pubmed-78835352021-02-16 Automated detection and quantification of reverse triggering effort under mechanical ventilation Pham, Tài Montanya, Jaume Telias, Irene Piraino, Thomas Magrans, Rudys Coudroy, Rémi Damiani, L. Felipe Mellado Artigas, Ricard Madorno, Matías Blanch, Lluis Brochard, Laurent Crit Care Research BACKGROUND: Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially harmful for the lung and the diaphragm, but its detection is challenging. Magnitude of effort generated by RT is currently unknown. Our objective was to validate supervised methods for automatic detection of RT using only airway pressure (Paw) and flow. A secondary objective was to describe the magnitude of the efforts generated during RT. METHODS: We developed algorithms for detection of RT using Paw and flow waveforms. Experts having Paw, flow and esophageal pressure (Pes) assessed automatic detection accuracy by comparison against visual assessment. Muscular pressure (Pmus) was measured from Pes during RT, triggered breaths and ineffective efforts. RESULTS: Tracings from 20 hypoxemic patients were used (mean age 65 ± 12 years, 65% male, ICU survival 75%). RT was present in 24% of the breaths ranging from 0 (patients paralyzed or in pressure support ventilation) to 93.3%. Automatic detection accuracy was 95.5%: sensitivity 83.1%, specificity 99.4%, positive predictive value 97.6%, negative predictive value 95.0% and kappa index of 0.87. Pmus of RT ranged from 1.3 to 36.8 cmH(2)0, with a median of 8.7 cmH(2)0. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths. CONCLUSION: An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH(2)O with important variability between and within patients. TRIAL REGISTRATION: BEARDS, NCT03447288. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-020-03387-3. BioMed Central 2021-02-15 /pmc/articles/PMC7883535/ /pubmed/33588912 http://dx.doi.org/10.1186/s13054-020-03387-3 Text en © The Author(s) 2021, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Pham, Tài
Montanya, Jaume
Telias, Irene
Piraino, Thomas
Magrans, Rudys
Coudroy, Rémi
Damiani, L. Felipe
Mellado Artigas, Ricard
Madorno, Matías
Blanch, Lluis
Brochard, Laurent
Automated detection and quantification of reverse triggering effort under mechanical ventilation
title Automated detection and quantification of reverse triggering effort under mechanical ventilation
title_full Automated detection and quantification of reverse triggering effort under mechanical ventilation
title_fullStr Automated detection and quantification of reverse triggering effort under mechanical ventilation
title_full_unstemmed Automated detection and quantification of reverse triggering effort under mechanical ventilation
title_short Automated detection and quantification of reverse triggering effort under mechanical ventilation
title_sort automated detection and quantification of reverse triggering effort under mechanical ventilation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7883535/
https://www.ncbi.nlm.nih.gov/pubmed/33588912
http://dx.doi.org/10.1186/s13054-020-03387-3
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