Machine learning models to further identify advantaged populations that can achieve functional cure of chronic hepatitis B virus infection after receiving Peg-IFN alpha treatment

IF 3.7 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics Pub Date : 2024-10-22 DOI:10.1016/j.ijmedinf.2024.105660

Wenting Zhong , Che Wang , Jia Wang , Tianyan Chen

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引用次数: 0

Abstract

Objective

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.

Methods

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.

Results

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).

Conclusion

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.

查看原文本刊更多论文

通过机器学习模型，进一步确定接受 Peg-IFN alpha 治疗后可实现慢性乙型肝炎病毒感染功能性治愈的优势人群。

目的：功能性治愈是目前治疗乙型肝炎病毒（HBV）的最高目标：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，仍有一部分人对其反应不佳，因此，探讨影响聚乙二醇干扰素α反应率的影响因素并建立预测模型以进一步识别优势人群具有重要意义：我们对 382 名患者进行了回顾性分析。在训练数据集中，我们使用递归特征消除（RFE）算法、Boruta算法和最小绝对收缩和选择算子（LASSO）回归算法从所有变量中提取了交叉特征。然后，我们采用六种机器学习（ML）算法--逻辑回归（LR）、随机森林（RF）、支持向量机（SVM）、K 最近邻（KNN）、轻梯度提升机（LightGBM）和极端梯度提升（XGBoost）--来开发模型。使用曲线下面积（AUC）等指标评估模型性能，并使用SHAPLEY Additive exPlanations（SHAP）图解释变量的显著性：基线时的 HBsAg、第 12 周时的 HBsAg 下降情况、非酒精性脂肪肝和年龄被确定为重要变量。RF 的 AUC 值为 0.988，表现最佳，并在测试集中保持良好表现。SHapley Additive exPlanations(SHAP)图显示，基线时的HBsAg和第12周时的HBsAg下降是最主要的两个预测因子。设计网络计算器是为了更方便地预测功能性治愈(https://www.xsmartanalysis.com/model/list/predict/model/html?mid = 17054&symbol = 317ad245Hx628ko3uW51)：我们建立了一个预测模型，该模型不仅可用于准确识别使用 Peg-IFN alpha 的优势人群，还可用于决定是否继续使用 Peg-IFN alpha。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Informatics 医学-计算机：信息系统

CiteScore

8.90

自引率

4.10%

发文量

217

审稿时长

42 days

期刊介绍： International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The scope of journal covers: Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.; Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc. Educational computer based programs pertaining to medical informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.