A clinical prediction model for safe early discharge of patients with an infection at the emergency department.

Background: Every hospital admission is associated with healthcare costs and a risk of adverse events. The need to identify patients who do not require hospitalization has emerged with the profound increase in hospitalization rates due to infectious diseases during the last decades, especially during the COVID-19 pandemic. This study aimed to identify predictors of safe early discharge (SED) in patients presenting to the emergency department (ED) with a suspected infection meeting the Systemic Inflammatory Response Syndrome (SIRS) criteria.

Predictive performance of NEWS and qSOFA in immunocompromised sepsis patients at the emergency department.

Purpose: Sepsis has a high incidence and a poor prognosis. Early recognition is important to facilitate timely initiation of adequate care. Sepsis screening tools, such as the (quick) Sequential Organ Failure Assessment ((q)SOFA) and National Early Warning Score (NEWS), could help recognize sepsis.