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Construction and Validation of a Predictive Model for the Risk of Anti-Tuberculosis Drug-Induced Liver Injury Based

Jingru Cheng1, Ruina Chen1, Hongqiu Pan2

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|November 27, 2025
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Summary

Anti-tuberculosis drug-induced liver injury (ATLI) is a major concern. A machine learning model using eight clinical factors accurately predicts ATLI risk, aiding early detection in tuberculosis patients.

Keywords:
LightGBManti‐tuberculosis drug‐induced liver injurymachine learning algorithms

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Area of Science:

  • Hepatology
  • Pharmacology
  • Data Science

Background:

  • Anti-tuberculosis drug-induced liver injury (ATLI) poses a significant threat to the successful treatment of tuberculosis (TB).
  • Developing reliable methods for early ATLI risk prediction is crucial for patient management and treatment adherence.

Purpose of the Study:

  • To construct and validate a predictive model for ATLI risk using machine learning algorithms.
  • To identify key clinical features that contribute to ATLI development.

Main Methods:

  • A retrospective cohort of 2356 TB patients was analyzed.
  • Seven machine learning algorithms were employed, including LightGBM, logistic regression, decision tree, SVM, random forest, GBDT, and XGBoost.
  • Feature selection was performed using LASSO regression, and random undersampling addressed class imbalance.

Main Results:

  • The LightGBM model achieved optimal performance with an AUC of 0.789, sensitivity of 0.734, and specificity of 0.706 in the testing set.
  • An 8-feature model (baseline HDLC, GGT, triglycerides, TCHOL, uric acid, TBIL, GLB, and liver disease history) showed strong predictive ability (AUC = 0.764).
  • The model demonstrated robust performance in an external validation cohort (AUC = 0.721).

Conclusions:

  • A predictive model utilizing baseline high-density lipoprotein cholesterol, γ-glutamyl transpeptidase, triglycerides, total cholesterol, uric acid, total bilirubin, globulin, and liver disease history effectively predicts ATLI.
  • This LightGBM-based model can assist clinicians in the early identification of patients at high risk for ATLI.