Acute Periorbital Infections: Who Needs Emergent Imaging?

Abstract

Computed tomography (CT) is used often in the evaluation of orbital infections to identify children who are most likely to benefit from surgical intervention. Our objective was to identify predictors for intraorbital or intracranial abscess among children who present with signs or symptoms of periorbital infection. These predictors could be used to better target patients for emergent CT. This was a retrospective cohort study of all patients admitted to an urban pediatric tertiary care emergency department between 1995 and 2008. We included otherwise healthy patients with suspected acute clinical periorbital or orbital cellulitis without a history of craniofacial surgery, trauma, or external source of infection. Immunocompromised patients and patients with noninfectious causes of periorbital swelling were excluded. Variables analyzed included age, duration of symptoms, highest recorded temperature, previous antibiotic therapy, physical examination findings, laboratory results, and interpretation of imaging. CT scans of the orbit were reread by a neuroradiologist. Nine hundred eighteen patients were included; 298 underwent a CT scan, and of those, 111 were shown to have an abscess. Although proptosis, pain with external ocular movement, and ophthalmoplegia were associated with presence of an abscess, 56 (50.5%) patients with abscess did not experience these symptoms. Other variables associated with the presence of an abscess in multivariate analysis were a peripheral blood neutrophil count greater than 10,000/microL, absence of infectious conjunctivitis, periorbital edema, age greater than 3 years, and previous antibiotic therapy (P < .05 for all). Our recursive partitioning model identified all high-risk (44%) patients as well as a low-risk (0.4%-2%) group (Rsq = 0.27). We confirmed that patients with proptosis and/or pain or limitation of extraocular movements are at high risk for intraorbital abscess, yet many do not have these predictors. Other features can identify patients who do not have such obvious predictors but do have significant risk of disease. A recursive partitioning model is presented.