An imaging-based clinical prediction model to differentiate brucellar spondylitis from pyogenic spondylitis: a multicenter retrospective observational study.

Authors

Jin Wang, Yuelong Cao, Xiaoqian Luo, Ruoyu Zhuang, Lijun Wang, Kaiying Cui, Tongxin Lu, Pengfei Hou, Zhen Song, Qing Wang, Zhaoxin Li, Qiang Zhang, Yanke Hao

Published 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. May 17, 2025. Epub May 17, 2025.

Abstract

Differentiating 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.
We 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.
Among 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.
This imaging-based model offers a useful tool for efficiently differentiating PS and BS, facilitating prompt diagnosis and treatment and mitigating incorrect or delayed diagnosis.

PMID:
40381013
Bibliographic data and abstract were imported from PubMed on 18 May 2025.

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