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.

Methods: We conducted a prospective cohort study on adult non-trauma patients with a suspected infection and at least two SIRS criteria. We defined SED as hospital discharge within 24 h (e.g. direct ED discharge or rapid ward discharge) without disease-related readmission to our hospital or death during the first seven days. A prediction model for SED was developed using multivariate logistic regression analysis and tested with k-fold cross-validation.

Results: We included 1381 patients, of whom 1027 (74.4 %) were hospitalized for longer than 24 h or re-admitted within seven days and 354 (25.6 %) met SED criteria. Parameters associated with SED were relatively young age, absence of comorbidities, living independently, yellow or green triage urgency, lack of ambulance transport or general practitioner referral, normal clinical impression scores, and risk scores (i.e., qSOFA, PIRO, MEDS, NEWS, and SIRS), normal vital sign measurements and absence of kidney and respiratory failure. The model performance metrics showed an area under the curve of 0.824. The validation showed a minimal drop in performance and indicated a good fit.

Conclusion: We developed and validated a model to identify patients with an infection at the ED who can be safely discharged early.