Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning
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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Annals of Medicine |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/07853890.2023.2249004 |
| _version_ | 1797302124605865984 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Yuanlin Yao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T23:32:15Z |
| format | Article |
| id | doaj.art-1be83446c7e94e81b6b331f80a2dd602 |
| institution | Directory Open Access Journal |
| issn | 0785-3890 1365-2060 |
| language | English |
| last_indexed | 2024-03-07T23:32:15Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of Medicine |
| spelling | doaj.art-1be83446c7e94e81b6b331f80a2dd6022024-02-20T11:58:23ZengTaylor & Francis GroupAnnals of Medicine0785-38901365-20602023-12-0155210.1080/07853890.2023.2249004Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learningYuanlin Yao0Shaofeng Wu1Chong Liu2Chenxing Zhou3Jichong Zhu4Tianyou Chen5Chengqian Huang6Sitan Feng7Bin Zhang8Siling Wu9Fengzhi Ma10Lu Liu11Xinli Zhan12Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaDepartment of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P.R. ChinaAbstractObjective 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.https://www.tandfonline.com/doi/10.1080/07853890.2023.2249004Spinal tuberculosismachine learningcluster analysisK-meansheterogeneity |
| spellingShingle | 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 Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning Annals of Medicine Spinal tuberculosis machine learning cluster analysis K-means heterogeneity |
| title | Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning |
| title_full | Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning |
| title_fullStr | Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning |
| title_full_unstemmed | Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning |
| title_short | Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning |
| title_sort | identification of spinal tuberculosis subphenotypes using routine clinical data a study based on unsupervised machine learning |
| topic | Spinal tuberculosis machine learning cluster analysis K-means heterogeneity |
| url | https://www.tandfonline.com/doi/10.1080/07853890.2023.2249004 |
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