The Impact of a Training Intervention on Detection of Patient-Ventilator Asynchronies in Nursing Students

Critical care nurses' knowledge and attitudes towards using ventilator waveform monitoring to detect patient-ventilator asynchrony: A cross-sectional online survey

BACKGROUND

Patient-ventilator asynchrony (PVA) is a condition that commonly affects patients who are mechanically ventilated. PVA happens when the patient's own breathing effort and the ventilator preset settings are out of sync. Ventilator waveform monitoring is viewed as a difficult undertaking, even for experienced practitioners, despite being a non-invasive and reliable tool for diagnosing PVA.

AIM

To assess the knowledge levels and attitudes of critical care nurses (CCNs) regarding the use of ventilator waveform monitoring to detect PVA.

STUDY DESIGN

A cross-sectional online survey was conducted in three intensive care units (ICUs) in Alexandria, Egypt. The questionnaire consisted of four parts to evaluate CCNs' level of knowledge and attitude regarding ventilator waveform monitoring and assess their ability to detect PVA.

RESULTS

Of the 137 CCNs approached, 101 CCNs completed the survey, resulting in a 73.7% response rate. Most nurses (88.1%) demonstrated poor knowledge levels and negative attitudes (93.1%) towards using waveform monitoring to detect PVA. A significant relationship was found between nurses' knowledge of ventilator waveform monitoring and their participation in previous training programmes on mechanical ventilation (MV; p = .031). Additionally, nurses' attitudes towards ventilator waveform monitoring were significantly associated with their level of education (p = .002) and attendance in previous courses on waveform analysis (p = .020).

CONCLUSIONS

A majority of CCNs have poor knowledge and negative attitudes regarding ventilator waveform monitoring. Previous training in MV and attendance courses on ventilator waveform analysis showed a significant correlation between nurses' level of knowledge and attitudes regarding ventilator waveform monitoring.

RELEVANCE TO CLINICAL PRACTICE

Assessment of CCNs' knowledge and attitudes regarding ventilator waveform monitoring for detecting patient-ventilator asynchrony (PVA) informs the development of future educational programmes, ultimately aiding in the delivery of prompt and high-quality care.

Survey of Ventilator Waveform Interpretation Among ICU Professionals

BACKGROUND

The interpretation of ventilator waveforms is essential for effective and safe mechanical ventilation but requires specialized training and expertise. This study aimed to investigate the ability of ICU professionals to interpret ventilator waveforms, identify areas requiring further education and training, and explore the factors influencing their interpretation skills.

METHODS

We conducted an international online anonymous survey of ICU professionals (physicians, nurses, and respiratory therapists [RTs]), with ≥ 1 y of experience working in the ICU. The survey consisted of demographic information and 15 multiple-choice questions related to ventilator waveforms. Results were compared between professions using descriptive statistics, and logistic regression (expressed as odds ratios [ORs; 95% CI]) was performed to identify factors associated with high performance, which was defined by a threshold of 60% correct answers.

RESULTS

A total of 1,832 professionals from 31 countries or regions completed the survey; 53% of respondents answered ≥ 60% of the questions correctly. The 3 questions with the most correct responses were related to waveforms that demonstrated condensation (90%), pressure overshoot (79%), and bronchospasm (75%). Conversely, the 3 questions with the fewest correct responses were waveforms that demonstrated early cycle leading to double trigger (43%), severe under assistance (flow starvation) (37%), and early/reverse trigger (31%). Factors significantly associated with ≥ 60% correct answers included years of ICU working experience (≥ 10 y, OR 1.6 [1.2-2.0], P < .001), profession (RT, OR 2.8 [2.1-3.7], P < .001), highest degree earned (graduate, OR 1.7 [1.3-2.2], P < .001), workplace (teaching hospital, OR 1.4 [1.1-1.7], P = .008), and prior ventilator waveforms training (OR 1.7 [1.3-2.2], P < .001).

CONCLUSIONS

Slightly over half respondents correctly identified ≥ 60% of waveforms demonstrating patient-ventilator discordance. High performance was associated with ≥ 10 years of ICU working experience, RT profession, graduate degree, working in a teaching hospital, and prior ventilator waveforms training. Some discordances were poorly recognized across all groups of surveyed professionals.

Specific Training Improves the Detection and Management of Patient-Ventilator Asynchrony

BACKGROUND

Patient-ventilator asynchrony is common in patients undergoing mechanical ventilation. The proportion of health-care professionals capable of identifying and effectively managing different types of patient-ventilator asynchronies is limited. A few studies have developed specific training programs, but they mainly focused on improving patient-ventilator asynchrony detection without assessing the ability of health-care professionals to determine the possible causes.

METHODS

We conducted a 36-h training program focused on patient-ventilator asynchrony detection and management for health-care professionals from 20 hospitals in Latin America and Spain. The training program included 6 h of a live online lesson during which 120 patient-ventilator asynchrony cases were presented. After the 6-h training lesson, health-care professionals were required to complete a 1-h training session per day for the subsequent 30 d. A 30-question assessment tool was developed and used to assess health-care professionals before training, immediately after the 6-h training lecture, and after the 30 d of training (1-month follow-up).

RESULTS

One hundred sixteen health-care professionals participated in the study. The median (interquartile range) of the total number of correct answers in the pre-training, post-training, and 1-month follow-up were significantly different (12 [8.75-15], 18 [13.75-22], and 18.5 [14-23], respectively). The percentages of correct answers also differed significantly between the time assessments. Study participants significantly improved their performance between pre-training and post-training (P < .001). This performance was maintained after a 1-month follow-up (P = .95) for the questions related to the detection, determination of cause, and management of patient-ventilator asynchrony.

CONCLUSIONS

A specific 36-h training program significantly improved the ability of health-care professionals to detect patient-ventilator asynchrony, determine the possible causes of patient-ventilator asynchrony, and properly manage different types of patient-ventilator asynchrony.