ArticleFrontiers in cellular and infection microbiology2023
A predictive model for early clinical diagnosis of spinal tuberculosis based on conventional laboratory indices: A multicenter real-world study.
Article in Frontiers in cellular and infection microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 9 citations in OpenAlex.
- Response to Letter to the Editor for AO Spine Clinical Practice Recommendations for the Management of Spinal Tuberculosis.Global spine journal · 2026Article
- Host Immune Competence as a Missing Variable in the Diagnostic Pathway for Spinal Tuberculosis.Global spine journal · 2026Article
- Comparison of the Efficacy and Prognosis of Different Treatment Strategies for Spinal Tuberculosis Patients Undergoing Vertebral Augmentation Surgery by Mistake.Orthopaedic surgery · 2026Article
- AO Spine Clinical Practice Recommendations for the Management of Spinal Tuberculosis.Global spine journal · 2026Review
- Development and validation of a nomogram for differential diagnosis of pyogenic spondylitis and tuberculous spondylitis in China: a multicenter retrospective study.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 · 2026Article
- [A clinical auxiliary differential diagnostic model for thoracolumbar spinal tuberculosis based on routine laboratory indicators: A multicenter retrospective cohort study].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Deep learning radiomics model based on contrast-enhanced MRI for distinguishing between tuberculous spondylitis and pyogenic spondylitis.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 · 2025Article
- Development and validation of an interpretable nomogram for predicting the risk of the prolonged postoperative length of stay for tuberculous spondylitis: a novel approach for risk stratification.BMC musculoskeletal disorders · 2025Article
- Deep Learning for Discrimination of Early Spinal Tuberculosis from Acute Osteoporotic Vertebral Fracture on CT.Infection and drug resistance · 2025Article
- Unlocking the Diagnostic Potential: A Systematic Review of Biomarkers in Spinal Tuberculosis.Journal of clinical medicine · 2024Review
- Combined clinical significance of MRI and serum mannose-binding lectin in the prediction of spinal tuberculosis.BMC infectious diseases · 2024Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Early diagnosis of spinal tuberculosis (STB) remains challenging. The aim of this study was to develop a predictive model for the early diagnosis of STB based on conventional laboratory indicators. Method: The clinical data of patients with suspected STB in four hospitals were included, and variables were screened by Lasso regression. Eighty-five percent of the cases in the dataset were randomly selected as the training set, and the other 15% were selected as the validation set. The diagnostic prediction model was established by logistic regression in the training set, and the nomogram was drawn. The diagnostic performance of the model was verified in the validation set. Result: A total of 206 patients were included in the study, including 105 patients with STB and 101 patients with NSTB. Twelve variables were screened by Lasso regression and modeled by logistic regression, and seven variables (TB.antibody, IGRAs, RBC, Mono%, RDW, AST, BUN) were finally included in the model. AUC of 0.9468 and 0.9188 in the training and validation cohort, respectively. Conclusion: In this study, we developed a prediction model for the early diagnosis of STB which consisted of seven routine laboratory indicators.
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