Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.

Introduction: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.

Aim: The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.

Failure rate of D-dimer testing in patients with high clinical probability of pulmonary embolism: Ancillary analysis of three European studies.

Background: In patients with a high clinical probability of pulmonary embolism (PE), the high prevalence can lower the D-dimer negative predictive value and increase the risk of diagnostic failure. It is therefore recommended that these high-risk patients should undergo chest imaging without D-dimer testing although no evidence supports this recommendation.

Objective: The objective was to evaluate the safety of ruling out PE based on D-dimer testing among patients with a high clinical probability of PE.

Effect of administration sequence of induction agents on first-attempt failure during emergency intubation: A Bayesian analysis of a prospective cohort.

Background: Emergency tracheal intubation is associated with a risk of clinical adverse events, including the risk of first-attempt failure. Induction agents usually include a sedative and a neuromuscular blocking agent (i.e.

Oxygen therapy in acute hypoxemic respiratory failure: guidelines from the SRLF-SFMU consensus conference.

Introduction: Although largely used, the place of oxygen therapy and its devices in patients with acute hypoxemic respiratory failure (ARF) deserves to be clarified. The French Intensive Care Society (Société de Réanimation de Langue Française, SRLF) and the French Emergency Medicine Society (Société Française de Médecine d'Urgence, SFMU) organized a consensus conference on oxygen therapy in ARF (excluding acute cardiogenic pulmonary oedema and hypercapnic exacerbation of chronic obstructive diseases) in December 2023.

Methods: A committee without any conflict of interest (CoI) with the subject defined 7 generic questions and drew up a list of sub questions according to the population, intervention, comparison and outcomes (PICO) model.