The time-sensitive challenge of diagnosing spinal epidural abscess in the emergency department.

Abstract

Spinal epidural abscess (SEA) is a rare but devastating condition. Entry of infectious contents into the epidural space occurs via contiguous infected tissue, hematogenous spread, or iatrogenic inoculation. Traditionally, emergency providers are taught to assess for the "classic triad" of spinal pain, fever, and neurological deficits, but this constellation of findings is seen in only 10-15% of cases. Delays in diagnosis and treatment of this condition directly correspond to worse, and often debilitating, outcomes for these patients. This review will demonstrate the challenges of diagnosing SEA, describe key diagnostic pitfalls, and present a model and framework for its evaluation. The authors conducted a systematic review in PubMed and Google Scholar for articles describing the emergency medicine evaluation and management of spinal epidural abscess dating from 1996 to 2016. Of the initial 219 articles found, 18 articles were selected based on their relevancy to emergency medicine. Lower back pain is a common chief complaint, whereas SEA is a rare condition and may not be anticipated. The "classic triad" of SEA symptoms presents infrequently. Moreover, the early symptoms of back pain and fever are non-specific, and patients seek medical attention at varying stages of disease progression. Once the more conspicuous and wide-ranging neurological symptoms develop, they are often irreversible. In fact, final outcomes correlate with the severity and duration of symptoms before surgery. Furthermore, discovering these late neurological symptoms can be particularly difficult in bed-bound and chronically ill patients. MRI is the best diagnostic imaging tool for SEA. Early diagnosis is the major prognostic factor for favorable outcome of SEA, and yet, making this diagnosis in the emergency department (ED) has proved challenging. Shifting from a "classic triad" screening to a risk factor-based model of evaluation represents the current optimal strategy for diagnosing SEA. An algorithm incorporating the most recent data is provided.