A predictive model for functional cure in chronic HBV patients treated with pegylated interferon alpha: a comparative study of multiple algorithms based on clinical data.

Abstract

Background: A multivariate predictive model was constructed using baseline and 12-week clinical data to evaluate the rate of clearance of hepatitis B surface antigen (HBsAg) at the 48-week mark in patients diagnosed with chronic hepatitis B who are receiving treatment with pegylated interferon α (PEG-INFα).

Methods: The study cohort comprised CHB patients who received pegylated interferon treatment at Mengchao Hepatobiliary Hospital, Fujian Medical University, between January 2019 and April 2024. Predictor variables were identified (LASSO), followed by multivariate analysis and logistic regression analysis. Subsequently, predictive models were developed via logistic regression, random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms. The efficacy of these models was assessed through various performance metrics, including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and F1 score.

Results: This study included a total of 224 individuals diagnosed with chronic hepatitis B. The variables baseline log₂(HBsAg), gender, age, neutrophil count at week 12, HBsAg decline rate at week 12, and HBcAb at week 12 were closely associated with functional cure and were included in the predictive model. In the validation term, the logistic regression model had an AUC of 0.858, which was better than that of the other machine learning models (AUC = 0.858,F1 = 0.753). Consequently, this model was selected for the development of the predictive tool.

Conclusions: The combined use of the baseline log₂(HBsAg) value, HBsAg decline rate at week 12, gender, neutrophil count at week 12, and age can serve as a foundational predicting model for anticipating the clearance of HBsAg in individuals with chronic hepatitis B who are receiving PEG-INFα therapy.