Work

A Predictive Clinical Model to Facilitate Early Recognition of Spinal Epidural Abscess in Adults

Year: 2016

Type: article

Abstract: Background. The incidence of spinal epidural abscess (SEA), an uncommon yet highly morbid and potentially lethal pyogenic infection of the CNS has risen dramatically over the past several decades. Although the presence and duration of neurologic deficits correlate with adverse outcomes, diagnostic delays are common as clinical manifestations may be non-specific at presentation. We developed a predictive clinical model to facilitate early recognition of SEA. Methods. In this retrospective, case-control study at a single, high-volume, tertiary-care, academic medical center, we used ICD-9 diagnostic codes to identify all adult inpatients from 2005 to 2015 with potential SEA. Records were systematically abstracted and designated as cases or controls based on an imaging and microbiologic algorithm. Logistic regression was used to develop a predictive model. Results. Univariate screening of 250 eligible patients identified several covariates that varied significantly between cases (n = 162) and controls (n = 88). A multivariable model identified seven as independent predictors of SEA in this population (figure), associated with 90% sensitivity, 84% specificity, and an AUC 0.91 (95% CI, 0.87–0.95). Removing bacteremia from the model, as it may not be apparent at initial clinical presentation, had little effect on sensitivity but reduced the specificity to 76%. ESR >50 (OR = 6.7; 95% CI, 2.1–20.8) was a strong predictor of SEA in the 73% of cases and 32% of controls with available data. Conclusion. Our model reliably discriminated SEA from other spinal pathologies in this retrospective study. It requires prospective validation in a multicenter study. Disclosures. All authors: No reported disclosures

Source: Open Forum Infectious Diseases

DOI: 10.1093/ofid/ofw172.895

Authors: Andrew Artenstein, Jennifer Friderici, Adam Holers, Deirdre Lewis, Jan Fitzgerald, Paul Visintainer

Institutions: Baystate Medical Center, University of Massachusetts Chan Medical School, Baystate Health

Language: English

Topic: Infectious Diseases and Tuberculosis

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,053