Abstract
Background: The study aims to assess the ability of ICU practitioners to identify different common types of patient-ventilator asynchronies according to their profession, years of experience, and prior training on mechanical ventilation by using waveform analysis.
Methods: We used an evaluation tool designed and validated by (Ramirez et al 2017). This tool consisted of 3 videos of common asynchronies (double-triggering, auto-triggering, and ineffective effort). The data was collected by evaluation sheet that was distributed to different hospitals in Saudi Arabia among different ICU practitioners, including physicians, respiratory therapists (RTs), and nurses. Each video was recorded from a SERVO- I ventilator, and showed 3 scalars: pressure versus time, flow versus time, and volume versus time wave forms. We used descriptive analysis, the Chi-square and Fisher exact tests. The experience and prior training on MV were classified as >5 years, <5 years' experience, trained and non-trained. We used a binary multiple logistic regression to find the association between years of experience, prior training on MV and profession and the ability of ICU practitioners to recognize ≥ 2 asynchronies correctly. Institutional Review Board (IRB) approval was obtained.
Results: Fifteen centers participated. A total of 152 ICU practitioners including 51 RTs (33.5%), 48 physicians (31.6%), nurses 53 (34.9%), completed the evaluation. Only 18 of ICU practitioners (11.8%) recognized the 3 types of asynchrony correctly, whereas 40 (26.3%) recognized 2 types of asynchrony correctly, and 65 (42.7%) recognized 1 type of asynchrony. 29 (19.1%) did not identified any type of asynchrony. Double-triggering was identify by 82 of ICU practitioners (53.9%), auto-triggering was identified by 62 (40.7%), ineffective effort was identified by 55 (36.1%). There were significant differences in term of training and experience among the ICU practitioners (see table 1). The RT profession was found to be significantly associated with the ability to identify 2 or more asynchronies after applying a binary multiple logistic regression model (An odds ratio of 0.429 (95% CI 0.185-0.997) with (P=0.04). The most recognized asynchrony by the RT's was double-triggering (28 of 51, 50%).
Conclusions: The results show that ICU practitioners in general have limited ability to recognize the most common types of PVAs. The profession of RT was found to be more associated factor to recognize types of asynchronies correctly.
Table 1. Comparisons of Trained VS Non-trained and Experienced VS Limited Experienced ICU Practitioners According the Number of PVAs Recognized Correctly.
Footnotes
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