Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning

Abstract

Objective

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.

Keywords:

• Spinal tuberculosis
• machine learning
• cluster analysis
• K-means
• heterogeneity

Acknowledgment

We are grateful to Dr. Xinli Zhan (Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University) for his kindly assistance in all stages of the present study.

Ethics approval

This study was approved by The Ethics Committee of the First Affiliated Hospital of Guangxi Medical University.

Author contribution

SW, YY, and XZ designed the study. CH, SF, CZ and JZ analyzed the data. BZ, LL, SW and ZM processed the digital visualization. SW wrote and revised the manuscript. CL and XZ revised the manuscript. All authors read and approved the final manuscript. All co-authors participated in the laboratory operation. All authors read and approved the final manuscript.

Consent form

Informed consent was obtained from all participants and/or their legal guardians.

Disclosure statement

The authors declare that they have no conflicts of interest.

Data availability statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.

Additional information

Funding

This work was supported by grants from the National Natural Science Foundation of China (81560359 and 81860393).