Background: Patient-ventilator asynchronies are usually detected by visual inspection of ventilator waveforms but with low sensitivity, even when performed by experts in the field. Recently, estimation of the inspiratory muscle pressure (Pmus) waveforms through artificial intelligence algorithm has been proposed (Magnamed®, São Paulo, Brazil). We hypothesized that the display of these waveforms could help healthcare providers identify patient-ventilator asynchronies.
Methods: A prospective single-center randomized study with parallel assignment was conducted to assess whether the display of the estimated Pmus waveform would improve the correct identification of asynchronies in simulated clinical scenarios. The primary outcome was the mean asynchrony detection rate (sensitivity). Physicians and respiratory therapists who work in intensive care units were randomized to control or intervention group. In both groups, participants analyzed pressure and flow waveforms of 49 different scenarios elaborated using the ASL-5000 lung simulator. In the intervention group the estimated Pmus waveform was displayed in addition to pressure and flow waveforms.
Results: A total of 98 participants were included, 49 per group. The sensitivity per participant in identifying asynchronies was significantly higher in the Pmus group (65.8 ± 16.2 vs. 52.94 ± 8.42, p < 0.001). This effect remained when stratifying asynchronies by type.
Conclusions: We showed that the display of the Pmus waveform improved the ability of healthcare professionals to recognize patient-ventilator asynchronies by visual inspection of ventilator tracings. These findings require clinical validation.
Trial registration: ClinicalTrials.gov: NTC05144607. Retrospectively registered 3 December 2021.
Keywords: Artificial ventilation; Interactive ventilatory support; Mechanical ventilation; Respiratory failure; Respiratory diaphragm.
© 2023. The Author(s).