CJEM
-
This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critical care within 12 h of ED arrival. ⋯ Machine learning models outperformed CTAS in identifying, at the point of ED triage, patients likely to need early critical care. If validated in future studies, machine learning models such as the ones developed here may be considered for incorporation in future revisions of the CTAS triage algorithm, potentially improving discrimination and reliability.
-
Our meta-analysis aimed to evaluate the safety of procedural sedation and analgesia in pediatric emergency department (ED) settings by investigating the incidence of cardiac, respiratory, gastrointestinal, and neurological adverse events associated with different sedation medications. ⋯ Procedural sedation in pediatric EDs is generally safe, with a low incidence of adverse events, such as vomiting, agitation, and hypoxia. Life-threatening respiratory adverse events are extremely rare. Our findings thus support the careful selection and monitoring of sedation protocols to minimize risks.