Simulation-based education for non-invasive ventilation

Simulation-Based Teaching and Learning in Respiratory Care Education: A Narrative Review

Simulation-based pedagogy has become an essential aspect of healthcare education. However, there is a significant gap in the literature regarding the application of simulation-based modalities in respiratory care education. This review aims to address this gap by providing insight into the theory and current uses of simulation, its effectiveness in respiratory care education, and strategies to enhance faculty development. The study utilizes a narrative synthesis approach to review relevant literature and provide a comprehensive understanding of the topic. The research involved comprehensive searches of electronic databases, including PubMed and Google Scholar, to identify relevant literature, encompassing original articles, reviews, and other pertinent content, focusing on simulation-based teaching and learning in respiratory care education published between 1990 and 2022. Findings suggest that simulation-based education is an effective tool for improving respiratory care education and can enhance the clinical skills of learners. The study concludes by discussing the future of simulation in respiratory care education and the potential benefits it may offer.

The Effectiveness of NIV and CPAP Training on the Job in COVID-19 Acute Care Wards: A Nurses' Self-Assessment of Skills

Background: Noninvasive ventilation (NIV) in COVID-19 patients outside of intensive care unit (ICU) settings was a feasible support during the pandemic outbreak. The aim of this study was to assess the effectiveness of an “on the job” NIV training program provided to 66 nurses working in 3 COVID-19 wards in an Italian university hospital. Methods: A quasi-experimental longitudinal before−after study was designed. The NIV Team education program, provided by expert ICU nurses, included: 3 h sessions of training on the job during work-shifts about the management of helmet-continuous positive airway pressure (CPAP) Venturi systems, and NIV with oronasal and full-face masks. An eleven-item “brief skills self-report tool” was administered before and after the program to explore the perception of NIV education program attendees about their level of skills. Results: In total, 59 nurses responded to the questionnaire. There was an improvement in the skill levels of the management of Helmet-CPAP (median before training 2, inter-quartile range (IQR) 0−6; median after training 8, IQR 3−9; p < 0.0001), and mask-NIV (median before training 2, IQR 0−6; median after training 8, IQR 3−9; p < 0.0001). Conclusions: Training on the job performed by expert ICU nurses can be a valuable and fast means to implement new Helmet-CPAP and mask-NIV skills outside of ICUs.

Randomized crossover trial of a pressure sensing visual feedback system to improve mask fitting in noninvasive ventilation

BACKGROUND AND OBJECTIVE

A good mask fit, avoiding air leaks and pressure effects on the skin are key elements for a successful noninvasive ventilation (NIV). However, delivering practical training for NIV is challenging, and it takes time to build experience and competency. This study investigated whether a pressure sensing system with real-time visual feedback improved mask fitting.

METHODS

During an NIV training session, 30 healthcare professionals (14 trained in mask fitting and 16 untrained) performed two mask fittings on the same healthy volunteer in a randomized order: one using standard mask-fitting procedures and one with additional visual feedback on mask pressure on the nasal bridge. Participants were required to achieve a mask fit with low mask pressure and minimal air leak (<10 L/min). Pressure exerted on the nasal bridge, perceived comfort of mask fit and staff- confidence were measured.

RESULTS

Compared with standard mask fitting, a lower pressure was exerted on the nasal bridge using the feedback system (71.1 ± 17.6 mm Hg vs 63.2 ± 14.6 mm Hg, P < 0.001). Both untrained and trained healthcare professionals were able to reduce the pressure on the nasal bridge (74.5 ± 21.2 mm Hg vs 66.1 ± 17.4 mm Hg, P = 0.023 and 67 ± 12.1 mm Hg vs 60 ± 10.6 mm Hg, P = 0.002, respectively) using the feedback system and self-rated confidence increased in the untrained group.

CONCLUSION

Real-time visual feedback using pressure sensing technology supported healthcare professionals during mask-fitting training, resulted in a lower pressure on the skin and better mask fit for the volunteer, with increased staff confidence.