Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model
Tao Zhong1, Zian Zhuang2,3,4, Xiaoli Dong2
1Department of Tuberculosis Control, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China.
JMIR Medical Informatics
|July 20, 2021
Summary
This study developed an interpretable machine learning model to predict tuberculosis-drug-induced liver injury (TB-DILI). The model accurately identifies patients at high risk, allowing for timely intervention to prevent liver damage.
Area of Science:
- Medical Informatics
- Hepatology
- Infectious Diseases
Background:
- Tuberculosis (TB) is a major global health threat, ranking among the top 10 causes of death.
- Drug-induced liver injury (DILI) is a significant and common adverse effect of TB treatment.
Purpose of the Study:
- To develop a predictive model for liver injury in patients undergoing TB treatment.
- To identify key clinical predictors for tuberculosis-drug-induced liver injury (TB-DILI).
Main Methods:
- An interpretable prediction model was designed using the XGBoost algorithm.
- Clinical data from 2014-2019 were extracted from the Shenzhen Nanshan Center for Chronic Disease Control Hospital Information System.
- Key predictors were identified based on relative importance and ROC curve analysis.
Main Results:
- Out of 757 patients, 287 (38%) developed TB-DILI.
- The best predictors identified were recent alanine transaminase levels, its rate of change, and cumulative doses of pyrazinamide and ethambutol.
- The model achieved 90% precision, 74% recall, and 76% accuracy, with an AUC of 0.912, predicting DILI a median of 15 days in advance.
Conclusions:
- The developed model demonstrates high accuracy and interpretability in predicting TB-DILI.
- This tool can assist clinicians in adjusting medication regimens to mitigate liver injury.
- Early prediction enables proactive management to prevent severe liver complications in TB patients.
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