Scand J Trauma Resus
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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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Scand J Trauma Resus · Jan 2024
Ambulance nurses' experiences as the sole caregiver with critical patients during long ambulance transports: an interview study.
Working in rural areas involves tackling long distances and occasional lack of supportive resources. Ambulance nurses are faced with the responsibility of making immediate autonomous decisions and providing extended care to critically ill patients during prolonged ambulance transport to reach emergency medical facilities. This study aims to expose the experiences of ambulance nurses acting as primary caregivers for critically ill patients during lengthy ambulance transfers in rural regions. ⋯ The findings underscore the necessity for thorough planning and adaptable thinking when attending to critically ill patients during extended transport scenarios. The absence of supporting resources can render the task demanding. Nevertheless, participants reported an inherent tranquility that aids them in maintaining focus amid their responsibilities.
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Although significant efforts have been made to enhance trauma care, the mortality rate for traumatic cardiac arrest (TCA) remains exceedingly high. Therefore, our institution has implemented special measures to optimize the treatment of major trauma patients. These measures include a prehospital Medical Intervention Car (MIC) and a 'code red' protocol in the trauma resuscitation room for patients with TCA or shock. ⋯ However, a significant proportion of these patients still die due to circulatory failure shortly after. Our observations from patients who underwent clamshell thoracotomy or received echocardiographic evaluation in conjunction with current scientific findings led us to conclude that dysfunction of the heart itself may be the cause. Therefore, we propose discussing severe trauma-associated cardiac failure (STAC) as a new entity to facilitate scientific research and the development of specific treatment strategies, with the aim of improving the outcome of severe trauma.