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Predicting patients with concurrent noncontiguous spinal epidural abscess lesions.

Kevin L Ju1, Sang Do Kim1, Rojeh Melikian1

  • 1Department of Orthopaedic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA.

The Spine Journal : Official Journal of the North American Spine Society
|June 24, 2014
PubMed
Summary

Identifying concurrent noncontiguous spinal epidural abscesses (SEAs) is challenging. A prediction model using delayed presentation, extraspinal infection, and high erythrocyte sedimentation rate can identify patients needing whole-spine imaging.

Keywords:
AlgorithmConcurrentEpidural abscessNoncontiguousPredictionSkip lesion

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Area of Science:

  • Neurosurgery
  • Infectious Diseases
  • Radiology

Background:

  • Spinal epidural abscess (SEA) is a critical condition with high morbidity and mortality.
  • Diagnosis is often delayed due to nonspecific symptoms.
  • Concurrent noncontiguous SEAs present diagnostic challenges, potentially leading to missed diagnoses and adverse outcomes.

Purpose of the Study:

  • To identify clinical and laboratory predictors for concurrent noncontiguous spinal epidural abscesses (SEAs).
  • To develop a tool for risk stratification and guide imaging decisions.

Main Methods:

  • Retrospective case-control study of 233 adult patients with SEAs who underwent entire spinal imaging.
  • Comparison of clinical and radiographic features between patients with single SEA and concurrent noncontiguous SEAs.
  • Multivariate logistic regression to identify independent predictors and construct a prediction algorithm.

Main Results:

  • Three significant predictors for concurrent noncontiguous SEAs were identified: delayed presentation (≥7 days), extraspinal infection, and erythrocyte sedimentation rate >95 mm/h.
  • A prediction model showed high accuracy (Area Under Curve = 0.936) for identifying patients with skip lesions.
  • The probability of skip SEA ranged from 0% to 73% based on the number of predictors present.

Conclusions:

  • A three-predictor model can effectively identify patients at high risk for concurrent noncontiguous SEAs.
  • This tool can aid clinicians in determining the necessity of entire spinal imaging.
  • Early identification and appropriate imaging can prevent delayed treatment and improve patient outcomes.