Lluis Blanch1,2, Laurent Brochard3,4, Tài Pham5,6,7, Jaume Montanya8, Irene Telias3,4,9,10, Thomas Piraino11,12, Rudys Magrans8, Rémi Coudroy3,4,13,14, L Felipe Damiani3,4,15, Ricard Mellado Artigas3,4,16, Matías Madorno17. 1. Critical Care Center, Hospital Universitari Parc Taulí, Institut D'Investigació I Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain. 2. Biomedical Research Networking Center in Respiratory Disease (CIBERES), Instituto de Salud Carlos III, Madrid, Spain. 3. Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond St, Toronto, ON, M5B 1W8, Canada. 4. Interdepartmental Division of Critical Care Medicine, University of Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada. 5. Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 30 Bond St, Toronto, ON, M5B 1W8, Canada. tai.pham@aphp.fr. 6. Interdepartmental Division of Critical Care Medicine, University of Toronto, 209 Victoria St, Toronto, ON, M5B 1T8, Canada. tai.pham@aphp.fr. 7. Université Paris-Saclay, AP-HP, Service de médecine intensive-réanimation, Hôpital de Bicêtre, DMU CORREVE, FHU SEPSIS, Groupe de recherche clinique CARMAS, Le Kremlin-Bicêtre, France. tai.pham@aphp.fr. 8. Better Care SL, Sabadell, Spain. 9. Division of Respirology, Department of Medicine, University Health Network, Toronto, Canada. 10. Sinai Health System, Toronto, Canada. 11. St. Michael's Hospital, Unity Health Toronto, Toronto, Canada. 12. Division of Critical Care, Department of Anesthesia, McMaster University, Hamilton, Canada. 13. Médecine Intensive Réanimation, CHU de Poitiers, Poitiers, France. 14. INSERM CIC 1402, Groupe ALIVE, Université de Poitiers, Poitiers, France. 15. Departamento Ciencias de la Salud, Carrera de Kinesiología, Faculdad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile. 16. Surgical ICU, Department of Anesthesia, Hospital Clínic, Barcelona, Spain. 17. Instituto Tecnológico de Buenos Aires (ITBA), Buenos Aires, Argentina.
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 (patientsparalyzed 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.
Authors: Richard H Kallet; Andre R Campbell; Rochelle A Dicker; Jeffrey A Katz; Robert C Mackersie Journal: Respir Care Date: 2005-12 Impact factor: 2.258
Authors: Joaquim Gea; Ercheng Zhu; Juan B Gáldiz; Norman Comtois; Igor Salazkin; José Antonio Fiz; Alejandro Grassino Journal: Arch Bronconeumol Date: 2009-02 Impact factor: 4.872
Authors: Marjolein de Wit; Kristin B Miller; David A Green; Henry E Ostman; Chris Gennings; Scott K Epstein Journal: Crit Care Med Date: 2009-10 Impact factor: 7.598
Authors: Brian Murray; Andrea Sikora; Jason R Mock; Thomas Devlin; Kelli Keats; Rebecca Powell; Thomas Bice Journal: Front Pharmacol Date: 2022-06-22 Impact factor: 5.988
Authors: Francesco Mojoli; Marco Pozzi; Anita Orlando; Isabella M Bianchi; Eric Arisi; Giorgio A Iotti; Antonio Braschi; Laurent Brochard Journal: Crit Care Date: 2022-01-30 Impact factor: 9.097
Authors: Blair Carl Schwartz; Dev Jayaraman; Stephen Su Yang; Evan G Wong; Jed Lipes; Sandra Dial Journal: Can J Anaesth Date: 2022-02-24 Impact factor: 6.713