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Updated: Aug 20, 2026
Diagnosis and Surgical Treatment of Human Brucellar Spondylodiscitis
Published on: May 23, 2021
Treating the host, not just the spine: a position paper proposing a clinical algorithm for spondylodiscitis based on
Stavros Oikonomidis1,2, Peer Eysel3, Dorothee Hornik4
1Department of Orthopaedic Surgery, Traumatology and Plastic-Reconstructive Surgery, University of Cologne, Faculty of Medicine and University Hospital Cologne, Joseph-Stelzmann-Str. 24, 50931, Cologne, Germany. Stavros.oikonomidis@rwth-aachen.de.
Purpose:
This study introduces a novel Clinical Risk Stratification Algorithm for spondylodiscitis, shifting the management focus from purely radiological criteria to clinical risk factors.
Methods:
The study presents a novel clinical risk stratification algorithm for spondylodiscitis, developed through a systematic synthesis of data from a 14-year prospective monocentric cohort. Developed from a prospective 14-year cohort (2008-2022) at a tertiary center, the algorithm synthesizes data from ten sub-analyses using multivariate regression to identify key drivers of mortality and treatment failure.
Results:
Significant risk factors for adverse outcomes include Chronic Kidney Disease (CKD), malignancy, age ≥ 65, and bacteremia. For patients with Spinal Epidural Abscess (SEA), diabetes and CRP levels ≥ 150 mg/l are critical predictors of neurologic deficit. The algorithm categorizes patients into three pathways: Path A (High Mortality) prioritizes aggressive surgical source control, challenging the traditional view that multimorbid patients are "too sick for surgery". Path B addresses failure risks like S. aureus using a "2-week CRP Checkpoint" to guide potential revision surgery. Path C focuses on quality-of-life-driven palliative care for oncology patients.
Conclusion:
This clinical tool enables personalized management by integrating systemic status into surgical decision-making. It emphasizes that surgery is a vital tool for sepsis control in frail patients and pain management in palliative care. While based on robust individual predictors, the proposed integrated decision tree has not yet undergone internal or external validation to confirm its clinical utility.
