Automated detection and quantification of reverse triggering effort under mechanical ventilation
Tài Pham, Jaume Montanyà, Irene Telías, Thomas Piraino, Rudys Magrans, Rémi Coudroy, L. Felipe Damiani, Ricard Mellado Artigas, Matías Madorno, Lluís Blanch, Laurent Brochard, the BEARDS study investigators, Tài Pham, Jaume Montanyà, Irene Telías, Thomas Piraino, Rudys Magrans, Rémi Coudroy, L. Felipe Damiani, Ricard Mellado Artigas, Matías Madorno, Lluís Blanch, Laurent Brochard, César Santis, Tommaso Mauri, Elena Spinelli, Giacomo Grasselli, Savino Spadaro, Carlo Alberto Volta, Francesco Mojoli, Dimitris Georgopoulos, Εumorfia Kondili, Stella Soundoulounaki, Tobias Becher, Norbert Weiler, Dirk Schaedler, Oriol Roca, Manel M. Santafé, Jordi Mancebo, Leo Heunks, Heder de Vries, Chang-Wen Chen, Zhou Jian-xin, Guangqiang Chen, Nuttapol Rittayamai, Norberto Tiribelli, Sebastián Fredes, Ricard Mellado Artigas, C. Ferrando Ortolá, François Beloncle, Alain Mercat, Jean-Michel Arnal, JL Diehl, Alexandre Demoule, Martin Dres, Sébastien Jochmans, Jonathan Chelly, Nicolas Terzi, Claude Guérin, Elias Baedorf Kassis, Jeremy R. Beitler, Davide Chiumello, Erica Ferrari Luca Bolgiaghi, Vito Fanelli, Jean-Emmanuel Alphonsine, Arnaud W. Thille, Laurent Papazian
Abstract
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.