دورية أكاديمية
Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning
| العنوان: | Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning |
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| المؤلفون: | Yuanlin Yao, Shaofeng Wu, Chong Liu, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Chengqian Huang, Sitan Feng, Bin Zhang, Siling Wu, Fengzhi Ma, Lu Liu, Xinli Zhan |
| المصدر: | Annals of Medicine, Vol 55, Iss 2 (2023) |
| بيانات النشر: | Taylor & Francis Group, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Medicine |
| مصطلحات موضوعية: | Spinal tuberculosis, machine learning, cluster analysis, K-means, heterogeneity, Medicine |
| الوصف: | AbstractObjective The 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.Methods A 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.Results Two 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.Conclusion K-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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 07853890 1365-2060 0785-3890 |
| Relation: | https://doaj.org/toc/0785-3890; https://doaj.org/toc/1365-2060 |
| DOI: | 10.1080/07853890.2023.2249004 |
| URL الوصول: | https://doaj.org/article/1be83446c7e94e81b6b331f80a2dd602 |
| رقم الأكسشن: | edsdoj.1be83446c7e94e81b6b331f80a2dd602 |
| قاعدة البيانات: | Directory of Open Access Journals |
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Full Text Finder | تدمد: | 07853890 13652060 |
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| DOI: | 10.1080/07853890.2023.2249004 |