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

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.