Automated detection and quantification of reverse triggering effort under mechanical ventilation

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.

The physiological underpinnings of life-saving respiratory support

“…As physiological understanding of the complex interaction between the risks of harm and benefits of mechanical ventilation and technical development of monitoring tools progress, the amount of data for decision-making becomes overwhelming. In this context, automated tools for analysis [58], decision support systems aided by artificial intelligence integrating physiological data [59,60], and automated modes of ventilation might help clinicians efficiently care for patients while minimizing harm. These will need to be rigorously developed, validated, and prospectively tested.…”

Timing of inspiratory muscle activity detected from airway pressure and flow during pressure support ventilation: the waveform method

“…Automation of the method is particularly attractive having the potential to decrease asynchronies and being at the same time non-invasive, low-cost and easy to be integrated in a mechanical ventilator. Machine learning has been already applied in the field with promising results [ 36 – 39 ].…”

Reverse Triggering: An Introduction to Diagnosis, Management, and Pharmacologic Implications

“…While bedside monitoring and maneuvers may be simple to perform, they require that clinicians must first be present at the time of the asynchrony to evaluate and must also be familiar with the classic signs that merit further workup of reverse triggering. Techniques and tools such as esophageal pressure monitoring or electromyography monitoring of the diaphragm in addition to new software and machine learning algorithms have shown promise and allow for the capture of even subtle signals on a continuous basis ( Rodriguez et al, 2020b ; Pham et al, 2021 ).…”

“…Techniques and tools such as esophageal pressure monitoring or electromyography monitoring of the diaphragm in addition to new software and machine learning algorithms have shown promise and allow for the capture of even subtle signals on a continuous basis (Rodriguez et al, 2020b;Pham et al, 2021).…”