Machine learning for prediction of septic shock at initial triage in emergency department.

Background: We hypothesized utilizing machine learning (ML) algorithms for screening septic shock in ED would provide better accuracy than qSOFA or MEWS.

Methods: The study population was adult (≥20 years) patients visiting ED for suspected infection. Target event was septic shock within 24 h after arrival.

The usefulness of C-reactive protein and procalcitonin to predict prognosis in septic shock patients: A multicenter prospective registry-based observational study.

The objective of this study was to evaluate the prognostic value of C-reactive protein (CRP), procalcitonin (PCT), and their combination for mortality in patients with septic shock. This multicenter, prospective, observational study was conducted between November 2015 and December 2017. A total of 1,772 septic shock patients were included, and the overall 28-day mortality was 20.

Quality improvement activity for improving pain management in acute extremity injuries in the emergency department.

Objective: The aim of this study was to investigate the effectiveness of a quality improvement activity for pain management in patients with extremity injury in the emergency department (ED).

Methods: This was a retrospective interventional study. The patient group consisted of those at least 19 years of age who visited the ED and were diagnosed with International Classification of Diseases codes S40-S99 (extremity injuries).