Predicting the need for surgical intervention in patients with spondylodiscitis: the Brighton Spondylodiscitis Score (BSDS)

Eur Spine J. 2019 Apr;28(4):751-761. doi: 10.1007/s00586-018-5775-x. Epub 2018 Oct 13.


Purpose: Spondylodiscitis represents a condition with significant heterogeneity. A significant proportion of patients are managed without surgical intervention, but there remains a group where surgery is mandated. The aim of our study was to create a scoring system to guide clinicians as to which patients with spondylodiscitis may require surgery.

Methods: A retrospective analysis of patients presenting to our institution with a diagnosis of spondylodiscitis between 2005 and 2014 was performed. Data for 35 variables, characterised as potential risk factors for requiring surgical treatment of spondylodiscitis, were collected. Logistic regression analysis was performed to evaluate the predictability of each. A prediction model was constructed, and the model was externally validated using a second series of patients from 2014 to 2015 meeting the same standards as the first population. The predicted odds were calculated for every patient in the data set. Receiver operating characteristic (ROC) curves were created, and the area under curve (AUC) was determined.

Results: Sixty-five patients were identified. Surgery was deemed necessary in 21 patients. Six predictors: distant site infection, medical comorbidities, the immunocompromised patient, MRI findings, anatomical location and neurology, were found to be the most consistent risk factors for surgical intervention. An internally validated scoring system with an AUC of 0.83 and an Akaike information criterion (AIC) of 115.2 was developed. External validation using a further 20 patients showed an AUC of 0.71 at 95% confidence interval of 0.50-0.88.

Conclusions: A new scoring system has been developed which can help guide clinicians as to when surgical intervention may be required. Further prospective analyses are required to validate this proposed scoring system. These slides can be retrieved under Electronic Supplementary Material.

Keywords: Predict; Spondylodiscitis; Treatment.

MeSH terms

  • Adult
  • Aged
  • Area Under Curve
  • Decision Support Techniques*
  • Discitis / diagnosis*
  • Discitis / surgery*
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • ROC Curve
  • Retrospective Studies
  • Risk Factors