Factors Associated With Observation Unit Admission in Emergency Department Patients With Skin and Soft Tissue Infections.

Abstract

Objectives: Skin and soft tissue infections (SSTIs) constitute a significant portion of emergency department (ED) cases, with some requiring admission into the observation unit (OU) for ongoing care. Identifying factors linked to OU admission is essential for optimizing patient disposition decisions.

Methods: A retrospective cohort study identified patients with the International Classification of Diseases 10th Revision (ICD-10) codes indicating SSTI at an urban, tertiary care ED over 3 years (2017-2019) who were ultimately discharged. Patients admitted at index visit were excluded. Medical charts were reviewed for demographic and clinical data. Simple logistic regression models explored bivariate associations with OU admission, while a multiple logistic regression model adjusted for demographics, clinical characteristics, vital signs, and pre-index visit and in-ED management.

Results: Among 1675 patients (42.9% female; mean age, 45.5 ± 15.4 years; 56.4% identifying as Black), 20.7% (n = 346) were admitted to the OU. Unadjusted analysis showed associations between OU admission and factors, including age, history of intravenous drug use (IVDU), lower extremity SSTI, subjective systemic illness, fever at index visit, ED surgical consultation, and pre-index visit antimicrobial choice. After adjustment, age (odds ratio [OR], 1.16; 95% CI, 1.04-1.30), immunocompromised status (OR, 1.83; 95% CI, 1.07-3.13), extremity cellulitis (lower: OR, 2.51; 95% CI, 1.55-4.14; upper: OR, 2.35; 95% CI, 1.36-4.12), surgical consultation (OR, 2.64; 95% CI, 1.79-3.91), and prehospital methicillin-resistant Staphylococcus aureus (MRSA) antibiotic prescription (OR, 2.76; 95% CI, 1.69-4.54; P = .0001) remained statistically significant.

Conclusion: Identifying factors associated with OU admission provides insights into clinician decision making, potentially identifying patients who might benefit from OU admission through future work, which should focus on predictors of hospital admission, OU failure, and antimicrobial selection to reduce OU failure rates.