Annals of emergency medicine
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Multicenter Study Comparative Study
Evaluating Sex Disparities in the Emergency Department Management of Patients With Suspected Acute Coronary Syndrome.
We compare clinical management and outcomes of emergency department (ED) encounters by sex after implementation of a clinical care pathway in 15 community EDs that standardized recommendations based on patient risk, using the History, ECG, Age, Risk Factors, and Troponin (HEART) score. ⋯ Women with low-risk HEART scores are hospitalized or stress tested less than men, which is likely appropriate, and women have better outcomes than men. Use of the HEART score has the potential to reduce sex disparities in acute coronary syndrome care.
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Multicenter Study Observational Study
Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm.
Machine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electronic medical records. Transfer learning, a new subfield of machine learning, allows generalizability of an algorithm across clinical sites. We aim to validate the Artificial Intelligence Sepsis Expert for the prediction of delayed septic shock in a cohort of patients treated in the ED and demonstrate the feasibility of transfer learning to improve external validity at a second site. ⋯ The Artificial Intelligence Sepsis Expert algorithm accurately predicted the development of delayed septic shock. The use of transfer learning allowed significantly improved external validity and generalizability at a second site. Future prospective studies are indicated to evaluate the clinical utility of this model.