Emergency medicine journal : EMJ
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Multicenter Study
Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study.
Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. ⋯ Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.
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To estimate incidence, risk factors, clinical characteristics and outcomes of acute (myo)pericarditis (AMP) in patients with COVID-19. ⋯ AMP is unusual as a form of COVID-19 presentation (about 1‰ cases), but SI is more than fourfold higher than non-COVID population, and it is less symptomatic, more severe and has higher in-hospital mortality; therefore, rapid recognition, echocardiographic assessment of myopericardial inflammation/dysfunction and treatment with vasoactive drugs when needed are recommended in AMP in patients with COVID-19.
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The high incidence of out-of-hospital cardiac arrest refractory to standard resuscitation protocols, despite precompetitive screening, demonstrated the need for a prehospital team to provide an effective system for life support and resuscitation at the Volleyball Men's World Championship. The evolution of mechanical circulatory support suggests that current advanced cardiovascular life support protocols no longer represent the highest standard of care at competitive sporting events with large spectator numbers. Extracorporeal life support (ECLS) improves resuscitation strategies and offers a rescue therapy for refractory cardiac arrest that can no longer be ignored. We present our operational experience of an out-of-hospital ECLS cardiopulmonary resuscitation team at an international sporting event.
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Patients with mild traumatic brain injury on CT scan are routinely admitted for inpatient observation. Only a small proportion of patients require clinical intervention. We recently developed a decision rule using traditional statistical techniques that found neurologically intact patients with isolated simple skull fractures or single bleeds <5 mm with no preinjury antiplatelet or anticoagulant use may be safely discharged from the emergency department. The decision rule achieved a sensitivity of 99.5% (95% CI 98.1% to 99.9%) and specificity of 7.4% (95% CI 6.0% to 9.1%) to clinical deterioration. We aimed to transparently report a machine learning approach to assess if predictive accuracy could be improved. ⋯ We found no clear advantages over the traditional prediction methods, although the models were, effectively, developed using a smaller data set, due to the need to divide it into training, calibration and validation sets. Future research should focus on developing models that provide clear advantages over existing classical techniques in predicting outcomes in this population.