Background: Orotracheal intubation (OTI) can result in aerosolisation leading to an increased risk of infection for healthcare providers, a key concern during the COVID-19 pandemic.
Objective: This study aimed to evaluate the OTI time and success rate of two aerosol-mitigating strategies under direct laryngoscopy and videolaryngoscopy performed by anaesthesiologists, intensive care physicians and emergency physicians who were voluntarily recruited for OTI in an airway simulation model.
Methodology: The outcomes were successful OTI, degree of airway visualisation and time required for OTI. Not using a stylet during OTI reduced the success rate among non-anaesthesiologists and increased the time required for intubation, regardless of the laryngoscopy device used.
Results: Success rates were similar among physicians from different specialties during OTI using videolaryngoscopy with a stylet. The time required for successful OTI by intensive care and emergency physicians using videolaryngoscopy with a stylet was longer compared with anaesthesiologists using the same technique. Videolaryngoscopy increased the time required for OTI among intensive care physicians compared with direct laryngoscopy. The aerosol-mitigating strategy under direct laryngoscopy with stylet did not increase the time required for intubation, nor did it interfere with OTI success, regardless of the specialty of the performing physician.
Conclusions: The use of a stylet within the endotracheal tube, especially for non-anaesthesiologists, had an impact on OTI success rates and decreased procedural time.
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http://dx.doi.org/10.1136/bmjstel-2020-000757 | DOI Listing |
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OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.
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The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning model to predict the Overall Sharp Score (OSS) from hand X-ray images.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!