European journal of emergency medicine : official journal of the European Society for Emergency Medicine
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Multicenter Study
MEESSI-AHF score to estimate short-term prognosis of acute heart failure patients in the Emergency Department: a prospective and multicenter study.
The assessment of acute heart failure (AHF) prognosis is primordial in emergency setting. Although AHF management is exhaustively codified using mortality predictors, there is currently no recommended scoring system for assessing prognosis. The European Society of Cardiology (ESC) recommends a comprehensive assessment of global AHF prognosis, considering in-hospital mortality, early rehospitalization rates and the length of hospital stay. ⋯ Among patients admitted to ED for an episode of AHF, the MEESSI-AHF score estimates with good performance the number of days alive and out of the hospital.
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Randomized Controlled Trial
Immigration bias among medical students: a randomized controlled trial.
Racial bias is found in both physicians and medical students. Immigrants in many parts of the world face challenges similar to racial minorities. Identification of immigrants might however be more subtle than identification by race, and currently, no data are available on a possible bias against the large minority group of migrants in Europe. ⋯ Medical students showed no immigration bias with regard to administering pain medication but were less likely to choose high-potency analgesia in immigrants. We also found a gender difference in pain management. These results demonstrate the importance of including knowledge about immigration bias in medical training.
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Acute dyspnoea is a common symptom in Emergency Medicine, and severity assessment is difficult during the first time the patient calls the Emergency Medical Call Centre. ⋯ During first calls for dyspnoea, six predictive factors are independently associated with the risk of early requirement of respiratory support.
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Observational Study
Using emergency department triage for machine learning-based admission and mortality prediction.
Patient admission is a decision relying on sparsely available data. This study aims to provide prediction models for discharge versus admission for ward observation or intensive care, and 30 day-mortality for patients triaged with the Manchester Triage System. ⋯ Machine learning can provide prediction on discharge versus admission to general wards and intensive care and inform about risk on 30 day-mortality for patients in the emergency department.