ArticleAnnals of medicine2023
Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning.
Article in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Preoperative differentiation of spinal tuberculosis, pyogenic, and brucellar spondylitis: a multimodal machine learning study across five centers.Frontiers in cellular and infection microbiology · 2026Article
- Heterogeneity in longitudinal medication adherence patterns among patients with spinal tuberculosis: a latent class analysis using multi-timepoint follow-up data and associations with clinical outcomes.Frontiers in public health · 2026Article
- Progress in diagnosis and treatment of primary spondylodiscitis: a systematic literature review.EFORT open reviews · 2025Article
- Gut microbial subtypes and clinicopathological value for colorectal cancer.Cancer medicine · 2024Article
- Prediction model for spinal cord injury in spinal tuberculosis patients using multiple machine learning algorithms: a multicentric study.Scientific reports · 2024Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe identification of spinal tuberculosis subphenotypes is an integral component of precision medicine. However, we lack proper study models to identify subphenotypes in patients with spinal tuberculosis. Here we identified possible subphenotypes of spinal tuberculosis and compared their clinical results.
methodsA total of 422 patients with spinal tuberculosis who received surgical treatment were enrolled. Clustering analysis was performed using the K-means clustering algorithm and the routinely available clinical data collected from patients within 24 h after admission. Finally, the differences in clinical characteristics, surgical efficacy, and postoperative complications among the subphenotypes were compared.
resultsTwo subphenotypes of spinal tuberculosis were identified. Laboratory examination results revealed that the levels of more than one inflammatory index in cluster 2 were higher than those in cluster 1. In terms of disease severity, Cluster 2 showed a higher Oswestry Disability Index (ODI), a higher visual analysis scale (VAS) score, and a lower Japanese Orthopedic Association (JOA) score. In addition, in terms of postoperative outcomes, cluster 2 patients were more prone to complications, especially wound infections, and had a longer hospital stay.
conclusionK-means clustering analysis based on conventional available clinical data can rapidly identify two subtypes of spinal tuberculosis with different clinical results. We believe this finding will help clinicians to rapidly and easily identify the subtypes of spinal tuberculosis at the bedside and become the cornerstone of individualized treatment strategies.
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