Interpretation of Mechanical Ventilator Waveforms in Identification of Patient Ventilator Asynchrony by Critical Care Nurses in Critical Care Units at Kenyatta National Hospital

Abstract

Background: Interpretation of mechanical ventilator waveforms is a non-invasive valuable tool in assessment of patient-ventilator interactions in mechanically ventilated patients. The mechanical ventilator should work in harmony with the respiratory mechanics of the patient. When this harmonious coordination is broken patient ventilator asynchrony occur. The ventilator waveforms provide actual time information that nurses can use to determine either if the ventilator is working as desired for the patient or if there is poor coordination between the ventilator and the patient. Despite the important information ventilator waveforms carry its application in management of mechanically ventilated patients has been underutilized by the nurses and entire critical care team due to limited understanding of the ventilator waveforms and its complexities. Main Objective: To determine the interpretation of mechanical ventilator waveforms in identification of patient ventilator asynchrony by critical care nurses in critical care units at Kenyatta National Hospital. Methods: The study involved 165 critical care nurses working in the main, Labour ward, pediatric, cardiothoracic, neurosurgical, medical and private wing critical care units at Kenyatta National Hospital. The study utilized descriptive cross-sectional study that employed quantitative methods in data collection and data analysis. Stratified random sampling was used to obtain the sample size. A self-administered structured questionnaire was used to collect data. The data was cleaned, coded and entered into statistical package for social sciences version 29 computer software. Descriptive and inferential statistics were used to analyze the data. Likert scale measurement was used to assess the respondents’ attitude and opinion in interpretation of waveform in identification of PVA. Chi-square test and logistic regression for multivariate analysis were used to evaluate and assess the relationship between knowledge, years of experience and critical care training and identification of PVAs using ventilator waveforms. Data was presented in form of tables, bar-charts, pie-charts and in-text form. Results: Ninety-six (58.2%) of respondents were female, 71(43%) were aged between 30 and 39 years. About 55.8% had higher diploma qualification in critical care nursing. The Bloom's cut-off, showed that 84 nurses (50.9%) had good knowledge, 15.2% had moderate knowledge, and 33.9% had poor knowledge. The study found significant associations between knowledge levels and two key factors. First, the highest level of education is significantly related to knowledge adequacy, with individuals holding diplomas showing better knowledge levels compared to those with graduate degrees (χ² = 6.444, p = 0.013). Second, additional training or Continuing Medical Education (CME) is strongly associated with higher knowledge adequacy, as those who participated in such programs demonstrated significantly better knowledge (χ² = 20.348, p < 0.001). The data reveals that out of 165 nurses, 84 (50.9%) exhibited a positive attitude, while 81 (49.1%)

had a negative attitude. The analysis identified significant associations between attitudes and both years of experience in critical care and additional training or Continuing Medical Education (CME). Specifically, years of experience in critical care were significantly linked to positive attitudes (χ²(1) = 0.005). Moreover, additional training or CME also significantly impacted attitudes, with those having such training being 2.3 times more likely to have a positive attitude compared to those without it (Odds Ratio = 2.3, 95% Confidence Interval: 1.22 – 4.36, p = 0.011). Conclusion and recommendations: The findings illustrated poor knowledge and attitude in the interpretation of mechanical ventilator waveforms in identification of patient ventilator asynchrony by critical care nurses. Thus, there is need to include comprehensive training on mechanical ventilator waveform interpretation through continuous medical education and certification programs.