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Automated cycling-off for improved patient-ventilator synchrony

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Date of first publication: 07.10.2022

A recent study showed that automated control of ventilator cycling-off based on real-time analysis of waveforms provided a reliable means of improving synchronization in mechanically ventilated patients (1).
Automated cycling-off for improved patient-ventilator synchrony

The prospective randomized crossover study was conducted in an Italian University ICU on 15 difficult-to-wean patients with normal or obstructive lung mechanics undergoing pressure-support ventilation. The investigators compared automated control of cycling-off at both baseline and high (50% increase) pressure support with the standard cycling-off setting (ETS set at 25% of peak inspiratory flow), as well as cycling-off optimized by an expert clinician at both baseline and high pressure support. 

Decrease in cycling delay and ineffective efforts

Results showed a significant decrease of more than 85% in cycling delay (407 ms vs. 59 ms) with automated versus standard settings at baseline pressure support, exceeding the primary endpoint reduction of 75%. The number of ineffective efforts was more than 75% lower (12.5% vs. 2.8%), exceeding the secondary endpoint of a 50% reduction.

Cycling delay with automation was also shorter than with expert optimization at both baseline and high pressure support. At high pressure support, cycling delay increased in the expert-optimization arm but not in the automated arm, and remained significantly longer even after the second optimization. 

At both pressure-support levels, the asynchrony time was significantly lower with automated settings than with expert optimization. Similarly, tidal volume decreased with automated settings at both levels. 

Automated versus expert optimization

The authors found that automated cycling-off was as good as, if not better than expert optimization for improving patient-ventilator interaction, and superior in terms of decreasing cycling delay. This may be due to the expiratory trigger adapting in real time to the patient’s effort, as opposed to the fixed – albeit personalized – sensitivity when optimized by the expert.

See full citation below (Mojoli F, Orlando A, Bianchi IM, et al. Waveforms-guided cycling-off during pressure support ventilation improves both inspiratory and expiratory patient-ventilator synchronisation [published online ahead of print, 2022 Sep 6]. Anaesth Crit Care Pain Med. 2022;41(6):101153. doi:10.1016/j.accpm.2022.1011531​).

 

Waveforms-guided cycling-off during pressure support ventilation improves both inspiratory and expiratory patient-ventilator synchronisation.

Mojoli F, Orlando A, Bianchi IM, et al. Waveforms-guided cycling-off during pressure support ventilation improves both inspiratory and expiratory patient-ventilator synchronisation [published online ahead of print, 2022 Sep 6]. Anaesth Crit Care Pain Med. 2022;41(6):101153. doi:10.1016/j.accpm.2022.101153



OBJECTIVE

To test the performance of a software able to control mechanical ventilator cycling-off by means of automatic, real-time analysis of ventilator waveforms during pressure support ventilation.

DESIGN

Prospective randomised crossover study.

SETTING

University Intensive Care Unit.

PATIENTS

Fifteen difficult-to-wean patients under pressure support ventilation.

INTERVENTIONS

Patients were ventilated using a G5 ventilator (Hamilton Medical, Bonaduz, Switzerland) with three different cycling-off settings: standard (expiratory trigger sensitivity set at 25% of peak inspiratory flow), optimised by an expert clinician and automated; the last two settings were tested at baseline pressure support and after a 50% increase in pressure support.

MEASUREMENTS AND MAIN RESULTS

Ventilator waveforms were recorded and analysed by four physicians experts in waveforms analysis. Major and minor asynchronies were detected and total asynchrony time computed. Automation compared to standard setting reduced cycling delay from 407 ms [257-567] to 59 ms [22-111] and ineffective efforts from 12.5% [3.4-46.4] to 2.8% [1.9-4.6]) at baseline support (p < 0.001); expert optimisation performed similarly. At high support both cycling delay and ineffective efforts increased, mainly in the case of expert setting, with the need of reoptimisation of expiratory trigger sensitivity. At baseline support, asynchrony time decreased from 39.9% [27.4-58.7] with standard setting to 32% [22.3-39.4] with expert optimisation (p < 0.01) and to 24.4% [19.6-32.5] with automation (p < 0.001). Both at baseline and at high support, asynchrony time was lower with automation than with expert setting.

CONCLUSIONS

Cycling-off guided by automated real-time waveforms analysis seems a reliable solution to improve synchronisation in difficult-to-wean patients under pressure support ventilation.