Respiratory care · 2026
Identification and Characterization of Reverse Triggering in Acute Hypoxemic Respiratory Failure Using an Automated Tool.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Marambio-Coloma Consuelo, Miranda-Hurtado Martin, Basoalto Roque, Bugedo Guillermo, Bruhn Alejandro, Damiani L Felipe
Аннотация
BACKGROUND: Reverse triggering is a highly common ventilatory asynchrony in sedated critically ill patients. When accompanied by high respiratory effort, it may cause diaphragmatic and lung injury. However, assessing its incidence and the associated effort remains limited, as it relies on expert review of waveform segments representing <5% of the full recording. Automated methods to assess and better characterize this patient-ventilator interaction are needed.
METHODS: We conducted an observational study in subjects with acute hypoxemic respiratory failure. Using ventilator and esophageal tracings, we developed an automated tool in Python (PyCharm Community Edition 2025.1.3) to identify the prevalence of reverse triggering and characterize reverse triggering in terms of the magnitude of respiratory effort and temporal trends over ∼12-h recordings. The tool was validated against expert visual assessment.
RESULTS: A total of 10 subjects were included (median age 56.5 years [interquartile range 45.0-69.0], 70% female). A total of 91 h of ventilatory recordings, corresponding to 97,687 respiratory cycles, were included. Reverse triggering was identified in 30.9% of all breaths, with marked inter-individual variability ranging from 4.6% to 77.4% across patients. Automated detection showed high diagnostic performance, with an overall accuracy of 89.1%, sensitivity of 85.9%, specificity of 92.4%, positive predictive value of 88.6%, and negative predictive value of 90.5%. The median reverse triggering associated respiratory effort (ΔPes) was -5.20 cm H2O.
CONCLUSIONS: An automated tool using flow and airway pressure accurately detected reverse triggering during prolonged recordings. Reverse triggering was frequent and typically associated with low respiratory effort, with marked variability across patients.
METHODS: We conducted an observational study in subjects with acute hypoxemic respiratory failure. Using ventilator and esophageal tracings, we developed an automated tool in Python (PyCharm Community Edition 2025.1.3) to identify the prevalence of reverse triggering and characterize reverse triggering in terms of the magnitude of respiratory effort and temporal trends over ∼12-h recordings. The tool was validated against expert visual assessment.
RESULTS: A total of 10 subjects were included (median age 56.5 years [interquartile range 45.0-69.0], 70% female). A total of 91 h of ventilatory recordings, corresponding to 97,687 respiratory cycles, were included. Reverse triggering was identified in 30.9% of all breaths, with marked inter-individual variability ranging from 4.6% to 77.4% across patients. Automated detection showed high diagnostic performance, with an overall accuracy of 89.1%, sensitivity of 85.9%, specificity of 92.4%, positive predictive value of 88.6%, and negative predictive value of 90.5%. The median reverse triggering associated respiratory effort (ΔPes) was -5.20 cm H2O.
CONCLUSIONS: An automated tool using flow and airway pressure accurately detected reverse triggering during prolonged recordings. Reverse triggering was frequent and typically associated with low respiratory effort, with marked variability across patients.