Observational studyEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2025
An imaging-based clinical prediction model to differentiate brucellar spondylitis from pyogenic spondylitis: a multicenter retrospective observational study.
Observational study in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeDifferentiating between pyogenic (PS) and brucellar (BS) spondylitis is clinically challenging due to their similar clinical symptoms, with delayed diagnosis or misdiagnosis common, causing trouble for surgeons in selecting appropriate treatment strategies. Currently, radiology-based diagnostic models for PS and BS are lacking. This study aimed to combine magnetic resonance (MR) and radiographic imaging to elucidate the differences between PS and BS and develop a novel diagnostic model for differential diagnosis.
methodsWe collected and analyzed the differences between MR and radiological images of patients with PS and BS from two medical institutions. A nomogram was constructed using least absolute shrinkage and selection operator (LASSO) regression, alongside univariate and multivariate analyses to select the best features of the predictive model. Model discrimination, calibration, and clinical utility were assessed using receiver operating characteristic, calibration, and decision curve analyses.
resultsAmong the enrolled 342 patients with PS (n = 167) or BS (n = 175), we found significant differences in MR and radiological characteristics between the two groups. LASSO regression analysis revealed that thoracic involvement, involved vertebrae number, parrot beak osteophyte presence, endplate destruction, and intervertebral disc signal strength on T1-weighted sequences were independent predictive factors for differentiating between PS and BS. The imaging-based clinical prediction model showed high accuracy in the training and validation sets, with the area under the curve achieving 0.861 and 0.908, respectively, and a significant net benefit in the threshold probability, indicating high clinical potential of the model.
conclusionThis imaging-based model offers a useful tool for efficiently differentiating PS and BS, facilitating prompt diagnosis and treatment and mitigating incorrect or delayed diagnosis.
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