Department of Infectious Disease, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Department of Radiology Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Int J Med Inform. 2025 Jan;193:105660. doi: 10.1016/j.ijmedinf.2024.105660. Epub 2024 Oct 22.
Functional cure is currently the highest goal of hepatitis B virus(HBV) treatment.Pegylated interferon(Peg-IFN) alpha is an important drug for this purpose,but even in the hepatitis B e antigen(HBeAg)-negative population,there is still a portion of the population respond poorly to it.Therefore,it is important to explore the influencing factors affecting the response rate of Peg-IFN alpha and establish a prediction model to further identify advantaged populations.
We retrospectively analyzed 382 patients.297 patients were in the training set and 85 patients from another hospital were in the test set.The intersect features were extracted from all variables using the recursive feature elimination(RFE) algorithm, Boruta algorithm, and least absolute shrinkage and selection operator(LASSO) regression algorithm in the training dataset.Then,we employed six machine learning(ML) algorithms-Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),K Nearest Neighbors(KNN),Light Gradient Boosting Machine(LightGBM) and Extreme Gradient Boosting(XGBoost)-to develop the model.Internal 10-fold cross-validation helped determine the best-performing model,which was then tested externally.Model performance was assessed using metrics such as area under the curve(AUC) and other metrics.SHapley Additive exPlanations(SHAP) plots were used to interpret variable significance.
138/382(36.13 %) patients achieved functional cure.HBsAg at baseline,HBsAg decline at week12,non-alcoholic fatty liver disease(NAFLD) and age were identified as significant variables.RF performed the best,with AUC value of 0.988,and maintained good performance in test set.The SHapley Additive exPlanations(SHAP) plot highlighted HBsAg at baseline and HBsAg decline at week 12 are the top two predictors.The web-calculator was designed to predict functional cure more conveniently(https://www.xsmartanalysis.com/model/list/predict/model/html?mid = 17054&symbol = 317ad245Hx628ko3uW51).
We developed a prediction model,which can be used to not only accurately identifies advantageous populations with Peg-IFN alpha,but also determines whether to continue subsequent Peg-IFN alpha.
功能性治愈是目前乙型肝炎病毒（HBV）治疗的最高目标。聚乙二醇干扰素（Peg-IFN）α是实现这一目标的重要药物，但即使在乙型肝炎 e 抗原（HBeAg）阴性人群中，仍有一部分人群对此反应不佳。因此，探索影响 Peg-IFN α 应答率的影响因素并建立预测模型以进一步确定优势人群非常重要。
我们回顾性分析了 382 例患者。297 例患者来自训练集，85 例患者来自另一所医院的测试集。从训练数据集中使用递归特征消除（RFE）算法、Boruta 算法和最小绝对值收缩和选择算子（LASSO）回归算法提取所有变量的交集特征。然后，我们使用六种机器学习（ML）算法-逻辑回归（LR）、随机森林（RF）、支持向量机（SVM）、K 最近邻（KNN）、Light Gradient Boosting Machine（LightGBM）和极端梯度提升（XGBoost）-建立模型。内部 10 折交叉验证有助于确定表现最佳的模型，然后对其进行外部测试。使用曲线下面积（AUC）等指标评估模型性能。使用 SHapley Additive exPlanations（SHAP）图解释变量的重要性。
382 例患者中，有 138 例（36.13%）达到了功能性治愈。基线 HBsAg、第 12 周 HBsAg 下降、非酒精性脂肪性肝病（NAFLD）和年龄被确定为显著变量。RF 表现最佳，AUC 值为 0.988，在测试集中也保持了良好的性能。SHapley Additive exPlanations（SHAP）图突出显示基线 HBsAg 和第 12 周 HBsAg 下降是前两个预测因素。我们设计了一个网络计算器，以更方便地预测功能性治愈（https://www.xsmartanalysis.com/model/list/predict/model/html?mid=17054&symbol=317ad245Hx628ko3uW51）。
我们开发了一个预测模型，不仅可以准确识别 Peg-IFN α的优势人群，还可以确定是否继续后续的 Peg-IFN α治疗。
