ArticleInfection and drug resistance2026
Analysis of High-Risk Factors for Tuberculosis Retreatment Based on Machine Learning and Latent Class Analysis.
Article in Infection and drug resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Object: To identify high-risk factors for tuberculosis retreatment and to provide a scientific basis for developing targeted prevention and control strategies by integrating machine learning with latent class analysis. Methods: This study retrospectively collected baseline and treatment-related data from 6,821 tuberculosis patients, employing machine learning and latent class analysis (LCA) to investigate the key influencing factors associated with high-risk populations for retreatment. Results: The XGBoost model achieved an overall accuracy of 84% and an area under the ROC curve (AUC) of 0.938. The analysis identified sputum examination results at month 6 or 8 of treatment, treatment regimen, and diagnostic classification as the most influential factors associated with retreatment. SHAP analysis further revealed that a sputum examination status of "not performed" was strongly linked to increased retreatment risk. Logistic regression confirmed this finding, with "not performed" ( Conclusion: It is recommended to improve treatment adherence and efficacy monitoring for newly diagnosed patients, strengthen whole-course supervision, and optimize management for elderly patients and those on long-term regimens.
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