Emergency and critical care medicine residents' competency to identify patient ventilator asynchrony using a mechanical ventilator waveform analysis in Addis Ababa, Ethiopia: a multicenter cross-sectional study.

Abstract

Background: patient-ventilator asynchrony (PVA) describes a condition in which a suboptimal interaction occurs between a patient and a mechanical ventilator. It is common and often undetected, with a negative impact on patient outcomes if unrecognized and addressed. Mechanical ventilator waveform analysis is a non-invasive and reliable way of identifying PVAs for which advanced methods of identifying PVA are lacking; however, it has not been well studied in residents working in developing setups like Ethiopia.

Objectives: to assess Emergency and Critical Care Medicine (ECCM) Residents' competency and associated factors to identify PVA using mechanical ventilator (MV) waveform analysis at Saint Paul Hospital Millennium Medical College (SPHMMC) and Tikur Anbesa Specialized Hospital (TASH).

Methodology: We conducted a cross-sectional study among senior ECCM residents who were on training at TASH and SPHMMC, Addis Ababa. The study enrolled all 91 senior ECCM residents with 80 completing it. A pretested and structured self-administered questionnaire was administered using an internally modified assessment tool. The completed data were collected via web links after being prepared using kobtoolbox. org, coded, manually checked, and exported to version 27 SPSS analysis. Descriptive statistics, the chi-square test, nonparametric tests, and multi-variable logistic regression were used for data analysis.

Results: Eighty senior residents responded out of 91, including 42 from TASH and 38 from SPHMMC. The overall competency of identifying PVA by MV waveforms was 30%. A median of 3 (IQR 1-4) PVAs were correctly identified. Only 1 resident (1.25%) identified all 6 different types of PVAs,;(8.75%) identified 5 PVAs; 20% identified 4 PVAs,22.5% identified 3 PVAs; 17.5% identified 2 PVAs, 13.75% identified 1 PVA Correctly and 16.25% did not identify any PVA. Auto-PEEP was the most frequently identified PVA, and delayed cycling was the least frequently identified PVA. Presenting or attending a seminar on MV waveforms and having lectures on mechanical ventilation increased the probability of identifying ≥ 4 PVAs.

Conclusion: The overall competency of identifying PVA by MV waveforms is low among ECCM residents. Presenting or attending seminars on MV waveforms, and having lectures on mechanical ventilation (MV) were associated with increased competency of identifying PVAs by MV waveform analysis.