Zeng Ying, Lu Hong, Li Sen, Shi Qun-Zhi, Liu Lin, Gong Yong-Qing, Yan Pan
Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, 410004, People's Republic of China.
Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People's Republic of China.
Drug Des Devel Ther. 2025 Jan 13;19:239-250. doi: 10.2147/DDDT.S495555. eCollection 2025.
Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs in pediatric tuberculosis patients. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of anti-tuberculosis drug-induced liver injury (ATB-DILI) in children.
A retrospective study was performed on the clinical data and therapeutic drug monitoring (TDM) results of children initially treated for tuberculosis at the affiliated Changsha Central Hospital of University of South China. After the features were screened by univariate risk factor analysis, AutoML technology was used to establish predictive models. The area under the receiver operating characteristic curve (AUC) was used to evaluate model's performance, and then the TreeShap algorithm was employed to interpret the variable contributions.
A total of 184 children were enrolled in this study, of whom 19 (10.33%) developed ATB-DILI. Univariate analysis showed that seven variables were risk factors for ATB-DILI, including the plasma peak concentration (C) of rifampicin, body mass index (BMI), alanine aminotransferase, total bilirubin, total bile acids, aspartate aminotransferase and creatinine. Among the numerous predictive models constructed by the "H2O" AutoML platform, the gradient boost machine (GBM) model exhibited the superior performance with AUCs of 0.838 and 0.784 on the training and testing sets, respectively. The TreeShap algorithm showed that C of rifampicin and BMI were important features that affect the AutoML model's performance.
The GBM model established by AutoML technology shows high predictive accuracy and interpretability for ATB-DILI in children. The prediction model can assist clinicians to implement timely interventions and mitigation strategies, and formulate personalized medication regimens, thereby minimizing potential harm to high-risk children of ATB-DILI.
药物性肝损伤（DILI）是小儿结核病患者中与一线抗结核药物相关的最常见且严重的药物不良反应之一。本研究旨在开发一种自动机器学习（AutoML）模型，用于预测儿童抗结核药物性肝损伤（ATB - DILI）的风险。
对在南华大学附属长沙中心医院初治结核病的儿童的临床资料和治疗药物监测（TDM）结果进行回顾性研究。通过单因素危险因素分析筛选特征后，使用AutoML技术建立预测模型。采用受试者操作特征曲线下面积（AUC）评估模型性能，然后运用TreeShap算法解释变量贡献。
本研究共纳入184名儿童，其中19名（10.33%）发生了ATB - DILI。单因素分析显示，7个变量是ATB - DILI的危险因素，包括利福平血浆峰浓度（C）、体重指数（BMI）、丙氨酸氨基转移酶、总胆红素、总胆汁酸、天门冬氨酸氨基转移酶和肌酐。在“H2O”AutoML平台构建的众多预测模型中，梯度提升机（GBM）模型表现出卓越性能，在训练集和测试集上的AUC分别为0.838和0.784。TreeShap算法表明，利福平的C和BMI是影响AutoML模型性能的重要特征。
通过AutoML技术建立的GBM模型对儿童ATB - DILI具有较高的预测准确性和可解释性。该预测模型可协助临床医生及时实施干预和缓解策略，制定个性化用药方案，从而将ATB - DILI对高危儿童的潜在危害降至最低。
