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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Published in:	Critical care (London, England) Vol. 25; no. 1; pp. 60 - 10
Main Authors:	Pham, Tài, Montanya, Jaume, Telias, Irene, Piraino, Thomas, Magrans, Rudys, Coudroy, Rémi, Damiani, L. Felipe, Mellado Artigas, Ricard, Madorno, Matías, Blanch, Lluis, Brochard, Laurent
Format:	Journal Article
Language:	English
Published:	London BioMed Central 15.02.2021 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	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 France Dyssynchrony Respiratory muscles Mechanical ventilation Reverse triggering Lung and diaphragm protection
ISSN:	1364-8535, 1466-609X, 1364-8535, 1466-609X, 1366-609X
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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	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. 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 ± 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. 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. 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
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/33588912$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1007/s10877-019-00444-3 10.1007/s00134-008-1121-9 10.1007/s00134-016-4593-z 10.1007/s00134-006-0301-8 10.1164/rccm.201703-0536OC 10.2307/2529310 10.1007/s00134-012-2493-4 10.1007/s00134-015-3692-6 10.1016/j.compbiomed.2018.04.016 10.1186/cc13063 10.1001/jama.2016.0291 10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2763 10.4187/respcare.04750 10.1007/s00134-005-2828-5 10.1164/rccm.200706-893OC 10.1016/j.mayocp.2017.05.004 10.1007/s00134-019-05608-y 10.1136/bmj.309.6947.102 10.1007/s00134-018-5500-6 10.1164/rccm.2107138 10.1016/j.arbres.2008.04.003 10.1139/y83-092 10.2202/1557-4679.1275 10.1164/ajrccm.164.9.2011150 10.1378/chest.12-1817 10.1136/bmj.308.6943.1552 10.1164/rccm.201804-0649LE 10.1056/NEJMoa070447 10.1164/rccm.201907-1358IM 10.1164/rccm.201603-0483LE 10.23736/S0375-9393.18.12506-5 10.1016/S2213-2600(18)30366-7 10.1513/AnnalsATS.201811-767CC 10.1007/s00134-015-4177-3 10.1007/s00134-007-0767-z 10.1097/CCM.0000000000002849 10.1164/rccm.201809-1781IM 10.1097/CCM.0b013e3182711972
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
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Keywords	Dyssynchrony Respiratory muscles Mechanical ventilation Reverse triggering Lung and diaphragm protection
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References	HJ de Vries (3387_CR9) 2019; 16 3387_CR22 E Akoumianaki (3387_CR6) 2013; 143 II Ramirez (3387_CR21) 2017; 62 M Orozco-Levi (3387_CR38) 2001; 164 T Yoshida (3387_CR36) 2013; 41 AW Thille (3387_CR24) 2008; 34 DG Altman (3387_CR27) 1994; 308 C Sinderby (3387_CR32) 2013; 17 HK Su (3387_CR12) 2019; 45 S Jochmans (3387_CR11) 2018; 84 J Gea (3387_CR40) 2009; 45 T Pham (3387_CR5) 2017; 92 C Graves (3387_CR18) 1986; 250 S Delisle (3387_CR10) 2016; 194 EC Goligher (3387_CR39) 2018; 197 S Muzzin (3387_CR17) 1992; 263 K Vaporidi (3387_CR3) 2017; 43 E Turbil (3387_CR13) 2019; 201 PD Sottile (3387_CR34) 2018; 46 AW Thille (3387_CR1) 2006; 32 RGT Blokpoel (3387_CR7) 2019; 200 EC Goligher (3387_CR19) 2019; 7 3387_CR25 CN Sessler (3387_CR29) 2002; 166 Q Mulqueeny (3387_CR33) 2007; 33 T Yoshida (3387_CR14) 2018; 198 D Georgopoulos (3387_CR20) 2006; 32 S Levine (3387_CR37) 2008; 358 M de Wit (3387_CR4) 2009; 37 G Bellani (3387_CR42) 2016; 315 GA Petrillo (3387_CR16) 1983; 61 JR Landis (3387_CR26) 1977; 33 L Blanch (3387_CR31) 2012; 38 A Esteban (3387_CR41) 2008; 177 PO Rodriguez (3387_CR35) 2019; 34 B Gholami (3387_CR23) 2018; 97 DG Altman (3387_CR28) 1994; 309 L Blanch (3387_CR2) 2015; 41 J Bourenne (3387_CR8) 2019; 45 RH Kallet (3387_CR15) 2005; 50 G Murias (3387_CR30) 2016; 42
References_xml	– volume: 34 start-page: 1239 year: 2019 ident: 3387_CR35 publication-title: J Clin Monit Comput doi: 10.1007/s10877-019-00444-3 – volume: 34 start-page: 1477 year: 2008 ident: 3387_CR24 publication-title: Intensive Care Med doi: 10.1007/s00134-008-1121-9 – volume: 43 start-page: 184 year: 2017 ident: 3387_CR3 publication-title: Intensive Care Med doi: 10.1007/s00134-016-4593-z – volume: 32 start-page: 1515 year: 2006 ident: 3387_CR1 publication-title: Intensive Care Med doi: 10.1007/s00134-006-0301-8 – volume: 197 start-page: 204 year: 2018 ident: 3387_CR39 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201703-0536OC – volume: 33 start-page: 159 year: 1977 ident: 3387_CR26 publication-title: Biometrics doi: 10.2307/2529310 – volume: 37 start-page: 2740 year: 2009 ident: 3387_CR4 publication-title: Crit Care Med – volume: 38 start-page: 772 year: 2012 ident: 3387_CR31 publication-title: Intensive Care Med doi: 10.1007/s00134-012-2493-4 – volume: 41 start-page: 633 year: 2015 ident: 3387_CR2 publication-title: Intensive Care Med doi: 10.1007/s00134-015-3692-6 – volume: 97 start-page: 137 year: 2018 ident: 3387_CR23 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2018.04.016 – volume: 17 start-page: R239 year: 2013 ident: 3387_CR32 publication-title: Crit Care Lond Engl doi: 10.1186/cc13063 – volume: 315 start-page: 788 year: 2016 ident: 3387_CR42 publication-title: JAMA doi: 10.1001/jama.2016.0291 – ident: 3387_CR22 doi: 10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2763 – volume: 250 start-page: R902 year: 1986 ident: 3387_CR18 publication-title: Am J Physiol – volume: 62 start-page: 144 year: 2017 ident: 3387_CR21 publication-title: Respir Care doi: 10.4187/respcare.04750 – volume: 32 start-page: 34 year: 2006 ident: 3387_CR20 publication-title: Intensive Care Med doi: 10.1007/s00134-005-2828-5 – volume: 177 start-page: 170 year: 2008 ident: 3387_CR41 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.200706-893OC – volume: 92 start-page: 1382 year: 2017 ident: 3387_CR5 publication-title: Mayo Clin Proc doi: 10.1016/j.mayocp.2017.05.004 – volume: 45 start-page: 1161 year: 2019 ident: 3387_CR12 publication-title: Intensive Care Med doi: 10.1007/s00134-019-05608-y – volume: 309 start-page: 102 year: 1994 ident: 3387_CR28 publication-title: BMJ doi: 10.1136/bmj.309.6947.102 – volume: 50 start-page: 1623 year: 2005 ident: 3387_CR15 publication-title: Respir Care – volume: 45 start-page: 725 year: 2019 ident: 3387_CR8 publication-title: Intensive Care Med doi: 10.1007/s00134-018-5500-6 – volume: 166 start-page: 1338 year: 2002 ident: 3387_CR29 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.2107138 – volume: 45 start-page: 68 year: 2009 ident: 3387_CR40 publication-title: Arch Bronconeumol doi: 10.1016/j.arbres.2008.04.003 – volume: 61 start-page: 599 year: 1983 ident: 3387_CR16 publication-title: Can J Physiol Pharmacol doi: 10.1139/y83-092 – ident: 3387_CR25 doi: 10.2202/1557-4679.1275 – volume: 164 start-page: 1734 year: 2001 ident: 3387_CR38 publication-title: Am J Respir Crit Care Med doi: 10.1164/ajrccm.164.9.2011150 – volume: 143 start-page: 927 year: 2013 ident: 3387_CR6 publication-title: Chest doi: 10.1378/chest.12-1817 – volume: 308 start-page: 1552 year: 1994 ident: 3387_CR27 publication-title: BMJ doi: 10.1136/bmj.308.6943.1552 – volume: 198 start-page: 1096 year: 2018 ident: 3387_CR14 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201804-0649LE – volume: 263 start-page: R292 year: 1992 ident: 3387_CR17 publication-title: Am J Physiol – volume: 358 start-page: 1327 year: 2008 ident: 3387_CR37 publication-title: N Engl J Med doi: 10.1056/NEJMoa070447 – volume: 201 start-page: e24 year: 2019 ident: 3387_CR13 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201907-1358IM – volume: 194 start-page: 1166 year: 2016 ident: 3387_CR10 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201603-0483LE – volume: 84 start-page: 871 year: 2018 ident: 3387_CR11 publication-title: Minerva Anestesiol doi: 10.23736/S0375-9393.18.12506-5 – volume: 7 start-page: 90 year: 2019 ident: 3387_CR19 publication-title: Lancet Respir Med doi: 10.1016/S2213-2600(18)30366-7 – volume: 16 start-page: 499 year: 2019 ident: 3387_CR9 publication-title: Ann Am Thorac Soc doi: 10.1513/AnnalsATS.201811-767CC – volume: 42 start-page: 1058 year: 2016 ident: 3387_CR30 publication-title: Intensive Care Med doi: 10.1007/s00134-015-4177-3 – volume: 33 start-page: 2014 year: 2007 ident: 3387_CR33 publication-title: Intensive Care Med doi: 10.1007/s00134-007-0767-z – volume: 46 start-page: e151 year: 2018 ident: 3387_CR34 publication-title: Crit Care Med doi: 10.1097/CCM.0000000000002849 – volume: 200 start-page: e4 year: 2019 ident: 3387_CR7 publication-title: Am J Respir Crit Care Med doi: 10.1164/rccm.201809-1781IM – volume: 41 start-page: 536 year: 2013 ident: 3387_CR36 publication-title: Crit Care Med doi: 10.1097/CCM.0b013e3182711972
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Snippet	Background 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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