Using machine learning models to predict HBeAg seroconversion in CHB patients receiving pegylated interferon‐α monotherapy

• Published in: Journal of clinical laboratory analysis 2022-11, Vol.36 (11), p.e24667-n/a
• Main Authors: Shang, Hongyan, Hu, Yuhai, Guo, Hongyan, Lai, Ruimin, Fu, Ya, Xu, Siyi, Zeng, Yongbin, Xun, Zhen, Liu, Can, Wu, Wennan, Guo, Jianhui, Ou, Qishui, Chen, Tianbin
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Bayesian analysis; Biopsy; Calibration; CHB; Datasets; HBeAg seroconversion; Hepatitis B; Hepatitis B e antigen; Infections; Interferon; Laboratories; laboratory variables; Learning algorithms; Liver cancer; Liver cirrhosis; Machine learning; Normal distribution; Patients; pegylated interferon‐α; Regression analysis; Response rates; Seroconversion; Variables
• ISSN: 0887-8013; 1098-2825
• DOI: 10.1002/jcla.24667

Background and objective Though there are many advantages of pegylated interferon‐α (PegIFN‐α) treatment to chronic hepatitis B (CHB) patients, the response rate of PegIFN‐α is only 30 ~ 40%. Therefore, it is important to explore predictors at baseline and establish models to improve the response rate of PegIFN‐α. Methods We randomly divided 260 HBeAg‐positive CHB patients who were not previously treated and received PegIFN‐α monotherapy (180 μg/week) into a training dataset (70%) and testing dataset (30%). The intersect features were extracted from 50 routine laboratory variables using the recursive feature elimination method algorithm, Boruta algorithm, and Least Absolute Shrinkage and Selection Operator Regression algorithm in the training dataset. After that, based on the intersect features, eight machine learning models including Logistic Regression, k‐Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Naïve Bayes were applied to evaluate HBeAg seroconversion in HBeAg‐positive CHB patients receiving PegIFN‐α monotherapy in the training dataset and testing dataset. Results XGBoost model showed the best performance, which had largest AUROC (0.900, 95% CI: 0.85–0.95 and 0.910, 95% CI: 0.84–0.98, in training dataset and testing dataset, respectively), and the best calibration curve performance to predict HBeAg seroconversion. The importance of XGBoost model indicated that treatment time contributed greatest to HBeAg seroconversion, followed by HBV DNA(log), HBeAg, HBeAb, HBcAb, ALT, triglyceride, and ALP. Conclusions XGBoost model based on common laboratory variables had good performance in predicting HBeAg seroconversion in HBeAg‐positive CHB patients receiving PegIFN‐α monotherapy. Flowchart of analysis process.