Non-Operative Management of Spinal Epidural Abscess: Development of a Predictive Algorithm for Failure
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CitationShah, Akash A. 2018. Non-Operative Management of Spinal Epidural Abscess: Development of a Predictive Algorithm for Failure. Doctoral dissertation, Harvard Medical School.
AbstractBackground context: Prompt diagnosis and treatment is key in spinal epidural abscess (SEA), as delay can lead to paralysis or death. The initial management decision for SEA is not always clear, with the literature showing conflicting results. When considering non-operative management, it is crucial to avoid failure of treatment, given the significant neurologic compromise incurred through failure. Unfortunately, data regarding risk factors associated with failure are scarce.
Purpose: The purpose of this study is to identify independent predictors of failure of non-operative management. Furthermore, we aim to develop a predictive algorithm that generates a probability of treatment failure based on the presence of these predictors.
Methods: All patients admitted to our hospital system with a diagnosis of SEA from 1993 to 2016 were identified. Patients older than 18 years who were initially managed non-operatively were included. Explanatory variables and outcomes were collected retrospectively. Bivariate and multivariable analyses were performed on these variables to identify independent predictors of failure. A nomogram was constructed to generate a risk of failure based on these predictors.
Results: We identified 367 patients who initially underwent non-operative management. Of these, 99 patients failed medical management. Multivariable logistic regression yielded six independent predictors of failure. Presenting motor deficit, pathologic/compression fracture in affected levels, active malignancy, diabetes mellitus, and sensory changes were positive predictors. Location of the abscess dorsal to the thecal sac was a negative predictor. Furthermore, we constructed a nomogram that generates a numerical probability of failure based on the presence of these factors. The presence of each independent predictor is assigned a point value. The points are summed and the total is converted to a probability of failing non-operative management.
Conclusions: By quantifying the risk of failure based on the presence of six independent predictors of treatment failure, our nomogram may provide a useful tool for the treatment team when weighing the risks and benefits of initial non-operative versus operative management.
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