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Updated: May 27, 2025
Mechanical Ventilation Boot Camp Curriculum
Published on: March 12, 2018
A Bench Model of Asynchrony in 6 Ventilators Equipped With Waveform-Guided Options
Isabella Maria Bianchi1, Eric Arisi2, Marco Pozzi2
1Dr Bianchi is affiliated with Department of Anesthesia and Intensive Care Medicine, Papa Giovanni XXXIII Hospital, Bergamo, Italy; and Department of Clinical-Surgical, Diagnostic and Paediatric Sciences, Unit of Anaesthesia and Intensive Care, University of Pavia, Pavia, Italy.
None:
Background: Pressure support ventilation is frequently associated with patient-ventilator asynchrony. Algorithms based on ventilator waveforms have been developed to automatically detect patient respiratory activity and to guide triggering and cycling. The aim of this study was to assess the performance in terms of synchronization of 6 mechanical ventilators, all provided with a waveform-guided software. Methods: This was a bench study to compare standard and new-generation systems simulating different respiratory mechanics, levels of assistance, and respiratory efforts. Six mechanical ventilators were tested: Hamilton G5 (G5) and C6 (C6), IMT bellavista1000 (B1000), Mindray SV300, and Philips RespironicsV200 (V200) and V60 (V60). Apart from V60, the other ventilators were tested twice: with default settings for standard triggering and cycling and with the waveform-guided automation. Results: With the automated settings, breaths with trigger delay ≤ 300 ms increased with B1000, G5, and C6. Ineffective efforts decreased with B1000, G5, C6, and V200. Improvement of triggering was mainly driven by findings obtained in the obstructive profile. With the automated settings, breaths with cycling delay > 300 ms decreased with B1000, G5, C6, and V200 while early cycled breaths increased with B1000. Improvement of cycling was mainly driven by findings obtained in the obstructive profile, whereas worsening of cycling was observed in the restrictive profile with 2 ventilators (B100 and V200). With the automated settings, the asynchrony index (AI) was reduced with G5 and C6 when all the profiles were grouped. In the obstructive profile, the AI decreased with B1000, G5, C6, and V200; in the restrictive profile, the AI increased with B1000. Conclusions: Waveforms-based algorithms have the potential to improve patient-ventilator synchronization. Automation had the most favorable impact when obstructive patients were simulated, while caution should be paid with restrictive ones.
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