Articles: hospital-emergency-service.
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Scand J Trauma Resus · Jan 2024
Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.
Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology. ⋯ This pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISKINDEX, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models.
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Endotracheal intubation is a potentially lifesaving procedure. Previously, data demonstrated that intubation remains the most performed airway intervention in the Role 1 setting. Additionally, deployed data demonstrate that casualties intubated in the prehospital setting have worse survival than those intubated in the emergency department setting. Technological solutions may improve intubation success in this setting. Certain intubation practices, including the use of endotracheal tube introducer bougies, facilitate intubation success especially in patients with difficult airways. We sought to determine the current state of the market for introducer devices. ⋯ We identified 12 introducer-variants on the market. Clinical studies are necessary to determine which devices may improve patient outcomes in the Role 1 setting.