Automated detection and quantification of reverse triggering effort under mechanical ventilation

• 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: England BioMed Central Ltd 15.02.2021; BioMed Central; BMC
• Subjects: Aged; Algorithms; Anesthesia; Area Under Curve; Artificial respiration; Automation; Catheters; Critical care; Diaphragm (Anatomy); Dyssynchrony; Esophagus; Female; Health aspects; Humans; Lung and diaphragm protection; Machine learning; Male; Mechanical ventilation; Mechanization; 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; 1366-609X
• DOI: 10.1186/s13054-020-03387-3

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.