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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Veröffentlicht in:Critical care (London, England) Jg. 25; H. 1; S. 60 - 10
Hauptverfasser: 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
Sprache:Englisch
Veröffentlicht: London BioMed Central 15.02.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1364-8535
1466-609X
1364-8535
1466-609X
1366-609X
DOI:10.1186/s13054-020-03387-3