Evaluation of health care providers' ability to identify patient-ventilator triggering asynchrony in intensive care unit: a translational observational study in China.

Abstract

Background: Patient-ventilator asynchrony (PVA) can result in ventilator-induced lung injury (VILI), prolong mechanical ventilation, and ventilator withdrawal failure. The ability of healthcare providers in China to recognize patient-ventilator asynchrony is unknown. The aim of our study was to evaluate the ability and potential influencing factors to correctly identify patient-ventilator triggering asynchrony in tertiary hospitals in China.

Methods: This was an observational study carried out in 53 tertiary hospitals in China. A total of 191 healthcare providers were asked to finish entry test and evaluation test sequentially. Entry test identified qualified professionals by matching concepts with its corresponding interpretations. Evaluation test assessed the ability in recognizing patient-ventilator asynchrony waveforms by matching asynchrony waveforms with corresponding concepts. A total of 109 qualified professionals were identified. Further analysis based on professional title, role in critical care team, years of experience in managing invasive mechanical ventilation, number of published articles in the field of clinical critical respiratory medicine and training in respiratory waveform/respiratory mechanics was carried out among qualified professionals. A self-innovate Remote-VentlateView platform was used to discriminate the patient-ventilator triggering asynchrony.

Results: Among 109 qualified professionals, the average recognition accuracy was 3.45 out of 8 sets. Inconsistency of concept cognition and waveform recognition of patient-ventilator asynchrony was found among all types of asynchronies. The accuracy of the trained professionals was greater than that of the nontrained professionals for ineffective trigger [76.7% vs. 59.2% (p = 0.009)], auto-trigger [26.7% vs. 12.2% (p = 0.014)] and reverse triggers [30.8% vs. 12.2% (p = 0.002)]. Professionals who published more than 2 articles in the field of critical respiratory performed better on auto-triggers [41.7% vs. 15.9% (p = 0.001)] and reverse triggers [38.9% vs. 19.2% (p = 0.018)]. Neither experience in managing invasive mechanical ventilation nor professional title was associated with the ability of healthcare providers to identify asynchrony.

Conclusions: Receiving training in mechanical ventilation and conducting critical respiratory clinical research may increase healthcare providers' ability to identify patient-ventilator asynchrony by using waveform analysis. The Remote-VentlateView platform may assist in identifying patient-ventilator asynchronies.