Maimaitiyibubaji Abudukadier,1,* Yuxin Zhang,2,* Maozhao Li,2,* Munire Muhetaer,2 Yibulayinjiang Mijiti,2 Zumulaiti Simayi,3 Maimaitijiang Aireti,4 Jingshun Tian,1 Maimaitishawutiaji Maimaiti2 

1Department of Hand and Foot Microsurgery, Children’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hospital of Beijing Children’s Hospital, Urumqi, Xinjiang, 830000, People’s Republic of China; 2Department of Spine Surgery, The First People’s Hospital of Kashi Prefecture, Kashi, Xinjiang, 844000, People’s Republic of China; 3Department of Neurology, The First People’s Hospital of Kashi Prefecture, Kashi, Xinjiang, 844000, People’s Republic of China; 4Department of Orthopedic, Children’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hospital of Beijing Children’s Hospital, Urumqi, Xinjiang, 830000, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Maimaitishawutiaji Maimaiti, Department of Spine Surgery, The First People’s Hospital of Kashi Prefecture, Kashi, Xinjiang, 844000, People’s Republic of China, Email flashmmt@163.com

Background: Tuberculous spondylitis (TS) and brucellar spondylitis (BS) exhibit certain similarities in clinical presentation and imaging characteristics, making differential diagnosis challenging. Developing a reliable differential diagnosis model can assist clinicians in distinguishing between these two conditions at an early stage, allowing for targeted prevention and treatment strategies.
Methods: Patients diagnosed with TS and BS were retrospectively collected and randomized into training and validation cohorts (ratio 7:3). The least absolute shrinkage and selection operator (LASSO) regression was used to reduce data dimensionality and select variables. Multivariate logistic regression was used to build predictive models. A nomogram was constructed to provide a visual representation of the model. Receiver operating characteristic (ROC) curve, calibration plots and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram.
Results: A total of 183 patients included (101 cases of TB, 82 cases of BS) our study. Our results showed that these variables including time from symptom onset to admission, anorexia, adenosine deaminase (ADA) and psoas abscess were important to differentiate TS and BS. The area under the curve (AUC) of ROC curve was 0.820 [95% CI (0.749, 0.892)] and 0.899 [95% CI (0.823, 0.976)] for the training and validation cohort, respectively. The results of calibration curve and DCA confirmed that the nomogram performed well in differentiating TS patient from BS.
Conclusion: The combination of time from symptom onset to admission, anorexia, ADA and psoas abscess demonstrated good differential properties for TS and BS. We developed a new nomogram model that can effectively differentiate TS and BS based on these four characteristics, which could be a valid and useful clinical tool for clinicians to aid in early differential diagnosis and targeted treatment.

Keywords: spinal infectious diseases, spinal tuberculous, brucellosis, nomograms