Interpretable machine learning model for prediction functional cure in chronic hepatitis B patients receiving Peg-IFN therapy: A multi-center study

Abstract

Background: Functional cure is the ideal treatment goal for chronic hepatitis B (CHB) treatment. We developed and validated machine learning (ML) models to predict functional cure in CHB patients.

Methods: This study retrospectively recruited 534 CHB patients who received Peg-IFN treatment to construct model and 269 patients for external validation. We analyzed three strategies: baseline, week 12, week 24. Seven ML models were constructed using selected variables by Boruta and least absolute shrinkage and selection operator regression algorithm, and performance metrics, including area under the curve (AUC), sensitivity, specificity, and F1 score were applied to determine the best model. We utilized SHapley Additive exPlanation to visualize and interpret the best model and built a website to conveniently predict functional cure of CHB.

Results: A total of 272 participants were cured in our study. Compared to baseline and week 12 strategies, week 24 using Support Vector Machine (SVM) model can better predict functional cure of CHB, with reliable predictive performance (AUC = 0.981), calibration and clinical applicability in external validation cohort. Age, ALT ratio at week 12, HBsAg at week 24 and HBsAg ratio at week 24 were important features. In order to enhance clinical convenience and effectiveness of the constructed model, a web-based dynamic nomogram was created (Dynamic Nomogram (shinyapps.io)).

Conclusion: This study developed SVM model to predict functional cure in CHB patients treated with Peg-IFN. Furthermore, we also built a website that clinicians can individualized predict the efficacy of Peg-IFN therapy in CHB patients.

Keywords: Chronic hepatitis B; Functional cure; Machine learning.