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 obje...
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| Vydáno v: | Critical care (London, England) Ročník 25; číslo 1; s. 60 - 10 |
|---|---|
| Hlavní autoři: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
BioMed Central
15.02.2021
BioMed Central Ltd Springer Nature B.V BMC |
| Témata: | |
| ISSN: | 1364-8535, 1466-609X, 1364-8535, 1466-609X, 1366-609X |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. Abstract 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 cmH20, with a median of 8.7 cmH20. 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 cmH2O with important variability between and within patients. Trial registration BEARDS, NCT03447288. 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.BACKGROUNDReverse 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.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.METHODSWe 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.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 cmH20, with a median of 8.7 cmH20. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths.RESULTSTracings 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 cmH20, with a median of 8.7 cmH20. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths.An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH2O with important variability between and within patients.CONCLUSIONAn automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH2O with important variability between and within patients.BEARDS, NCT03447288.TRIAL REGISTRATIONBEARDS, NCT03447288. 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 cmH20, with a median of 8.7 cmH20. 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 cmH2O with important variability between and within patients. Trial registration BEARDS, NCT03447288. 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. 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. 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 0, with a median of 8.7 cmH 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. An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH O with important variability between and within patients. BEARDS, NCT03447288. 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 [+ or -] 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.sub.20, with a median of 8.7 cmH.sub.20. 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.sub.2O with important variability between and within patients. Trial registration BEARDS, NCT03447288. Keywords: Reverse triggering, Dyssynchrony, Mechanical ventilation, Lung and diaphragm protection, Respiratory muscles 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. 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. Tracings from 20 hypoxemic patients were used (mean age 65 [+ or -] 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.sub.20, with a median of 8.7 cmH.sub.20. RT with breath stacking had the highest levels of Pmus, and RTs with no breath stacking were of similar magnitude than pressure support breaths. An automated detection tool using airway pressure and flow can diagnose reverse triggering with excellent accuracy. RT generates a median Pmus of 9 cmH.sub.2O with important variability between and within patients. |
| ArticleNumber | 60 |
| Audience | Academic |
| Author | Pham, Tài Mellado Artigas, Ricard Damiani, L. Felipe Piraino, Thomas Telias, Irene Montanya, Jaume Magrans, Rudys Madorno, Matías Coudroy, Rémi Brochard, Laurent Blanch, Lluis |
| Author_xml | – sequence: 1 givenname: Tài orcidid: 0000-0002-4373-0711 surname: Pham fullname: Pham, Tài email: tai.pham@aphp.fr organization: Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Interdepartmental Division of Critical Care Medicine, University of Toronto, Université Paris-Saclay, AP-HP, Service de médecine intensive-réanimation, Hôpital de Bicêtre, DMU CORREVE, FHU SEPSIS, Groupe de recherche clinique CARMAS – sequence: 2 givenname: Jaume surname: Montanya fullname: Montanya, Jaume organization: Better Care SL – sequence: 3 givenname: Irene surname: Telias fullname: Telias, Irene organization: Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Interdepartmental Division of Critical Care Medicine, University of Toronto, Division of Respirology, Department of Medicine, University Health Network, Sinai Health System – sequence: 4 givenname: Thomas orcidid: 0000-0002-4887-8686 surname: Piraino fullname: Piraino, Thomas organization: St. Michael’s Hospital, Unity Health Toronto, Division of Critical Care, Department of Anesthesia, McMaster University – sequence: 5 givenname: Rudys surname: Magrans fullname: Magrans, Rudys organization: Better Care SL – sequence: 6 givenname: Rémi surname: Coudroy fullname: Coudroy, Rémi organization: Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Interdepartmental Division of Critical Care Medicine, University of Toronto, Médecine Intensive Réanimation, CHU de Poitiers, INSERM CIC 1402, Groupe ALIVE, Université de Poitiers – sequence: 7 givenname: L. Felipe orcidid: 0000-0002-8338-0488 surname: Damiani fullname: Damiani, L. Felipe organization: Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Interdepartmental Division of Critical Care Medicine, University of Toronto, Departamento Ciencias de la Salud, Carrera de Kinesiología, Faculdad de Medicina, Pontificia Universidad Católica de Chile – sequence: 8 givenname: Ricard surname: Mellado Artigas fullname: Mellado Artigas, Ricard organization: Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Interdepartmental Division of Critical Care Medicine, University of Toronto, Surgical ICU, Department of Anesthesia, Hospital Clínic – sequence: 9 givenname: Matías surname: Madorno fullname: Madorno, Matías organization: Instituto Tecnológico de Buenos Aires (ITBA) – sequence: 10 givenname: Lluis surname: Blanch fullname: Blanch, Lluis organization: Critical Care Center, Hospital Universitari Parc Taulí, Institut D’Investigació I Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III – sequence: 11 givenname: Laurent surname: Brochard fullname: Brochard, Laurent organization: Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Interdepartmental Division of Critical Care Medicine, University of Toronto |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33588912$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Contributor | Piraino, Thomas Beloncle, François Soundoulounaki, Stella Dres, M Santis, Cesar Kondili, Eumorfia Chelly, J Mancebo, Jordi Tiribelli, Norberto Madorno, Matías Ferrando Ortolá, Carlos Chiumello, Davide Heunks, Leo Thille, Arnaud W Chen, Chang-Wen Diehl, J L Volta, Carlo Alberto Jochmans, S Guérin, Claude Fanelli, V Pham, Tài Rittayamai, Nuttapol Schaedler, Dirk Zhou, Jian-Xin Arnal, J M Grasselli, Giacomo Coudroy, Rémi Mojoli, Francesco Terzi, Nicolas Montanya, Jaume Beitler, J Roca, Oriol Santafe, Manel Bolgiaghi, Erica Ferrari Luca Damiani, L Felipe Mellado Artigas, Ricard Telias, Irene Baedorf Kassis, E Weiler, Norbert Brochard, Laurent Demoule, A Mauri, Tommaso Fredes, Sebastian Spinelli, Elena Mercat, Alain de Vries, Heder Chen, Guang-Qiang Papazian, Laurent Alphonsine, J E Magrans, Rudys Becher, Tobias Georgopoulos, Dimitris Spadaro, Savino Blanch, Lluis |
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| Copyright | The Author(s) 2021 corrected publication 2022 COPYRIGHT 2021 BioMed Central Ltd. 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2021 |
| Copyright_xml | – notice: The Author(s) 2021 corrected publication 2022 – notice: COPYRIGHT 2021 BioMed Central Ltd. – notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2021 |
| CorporateAuthor | the BEARDS study investigators BEARDS study investigators |
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| Keywords | Dyssynchrony Respiratory muscles Mechanical ventilation Reverse triggering Lung and diaphragm protection |
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Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially... Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially harmful... Background Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is potentially... Abstract Background Reverse triggering (RT) is a dyssynchrony defined by a respiratory muscle contraction following a passive mechanical insufflation. It is... |
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| SubjectTerms | Aged Algorithms Anesthesia Area Under Curve Artificial respiration Automation Catheters Critical care Critical Care Medicine Diaphragm (Anatomy) Dyssynchrony Emergency Medicine Esophagus Female Health aspects Humans Intensive Lung and diaphragm protection Machine learning Male Mechanical ventilation Mechanization Medicine Medicine & Public Health Methods Middle Aged Muscle contraction Patient monitoring Patients Positive-Pressure Respiration - methods Positive-Pressure Respiration - statistics & numerical data Pressure Respiration, Artificial - methods Respiration, Artificial - statistics & numerical data Respiratory Mechanics - physiology Respiratory muscles Reverse triggering ROC Curve Signal processing Ventilators Weights and Measures - instrumentation Work of Breathing - physiology |
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| Title | Automated detection and quantification of reverse triggering effort under mechanical ventilation |
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