Anne-Kathrin Brill1,2,3, Mohammad Moghal1,3, Mary J Morrell1,3, Anita K Simonds1,3. 1. Academic Unit of Sleep and Breathing, National Heart and Lung Institute, Imperial College, London, UK. 2. Department of Pulmonary Medicine, University Hospital Bern, Bern, Switzerland. 3. National Institute for Health Research (NIHR) Respiratory Biomedical Research Unit, Royal Brompton and Harefield NHS Foundation Trust and Imperial College, London, UK.
Abstract
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.
RCT Entities:
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.
Authors: Marc A Masen; Aaron Chung; Joanna U Dawczyk; Zach Dunning; Lydia Edwards; Christopher Guyott; Thomas A G Hall; Rachel C Januszewski; Shaoli Jiang; Rikeen D Jobanputra; Kabelan J Karunaseelan; Nikolaos Kalogeropoulos; Maria R Lima; C Sebastian Mancero Castillo; Idris K Mohammed; Manoj Murali; Filip P Paszkiewicz; Magdalena Plotczyk; Catalin I Pruncu; Euan Rodgers; Felix Russell; Richard Silversides; Jennifer C Stoddart; Zhengchu Tan; David Uribe; Kian K Yap; Xue Zhou; Ravi Vaidyanathan Journal: PLoS One Date: 2020-09-24 Impact factor: 3.240