Noninvasive Prediction of Significant Hepatic Injury in Treatment-Naïve Children with Chronic HBV Infection
Year: 2026
Type: article
Study design: Observational Study
Abstract: Jiaying Wu, Xiaorong Peng, Yunan Chang, Hongmei Xu Department of Infectious Diseases, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents’ Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Infection and Immunity, Chongqing, 401122, People’s Republic of ChinaCorrespondence: Hongmei Xu, Department of Infectious Diseases, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents’ Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Infection and Immunity, No. 20, Jinyu Avenue, Liangjiang New District, Chongqing, 401122, People’s Republic of China, Tel +86-13883589586, Email [email protected] Yunan Chang, Department of Infectious Diseases, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Children and Adolescents’ Health and Diseases, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Infection and Immunity, No. 20, Jinyu Avenue, Liangjiang New District, Chongqing, 401122, People’s Republic of China, Tel +86-13308320406, Email [email protected]: Significant hepatic injury (SHI) in children with chronic hepatitis B virus (HBV) infection requires timely identification, yet liver biopsy is invasive and not routinely feasible. This study aimed to develop a non-invasive predictive model for SHI.Methods: This retrospective analysis included treatment-naïve children with chronic HBV infection undergoing liver biopsy. Participants were randomly split into 70% training and 30% independent testing cohorts prior to preprocessing. Missing data imputation and least absolute shrinkage and selection operator (LASSO)-based feature selection were performed, and nine machine learning (ML) models were developed and optimized using nested cross-validation (CV). The optimal logistic regression (LR) model was used to construct and validate a predictive nomogram.Results: A total of 246 eligible children were included, of whom 147 (59.8%) had SHI. LASSO regression identified five predictors for model development. Among the evaluated ML models, LR showed stable performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.799 (95% CI: 0.740– 0.858) in the training folds, 0.781 (95% CI: 0.585– 0.972) in the validation folds, and 0.707 (95% CI: 0.562– 0.851) in the independent testing cohort. The nomogram showed moderate calibration and better clinical utility compared with alanine aminotransferase (ALT) alone across a broad range of threshold probabilities.Conclusion: We developed a non-invasive predictive model based on routine clinical data to detect SHI in treatment-naïve children with chronic HBV infection, which exhibited moderate predictive power and marginal improvement over ALT alone. An infographic is divided into four parts (A–D): Clinical Data Collection, Model Selection, Model Presentation, and Clinical Application. A: Clinical data collection includes demographic and laboratory data. B: Model construction and validation includes variable selection using LASSO and VIF, with variables such as ALT and HBeAb. Model selection features classifiers such as LR and RF, highlighting LR. C: LR model construction and evaluation includes 147 children with SHI and 99 without SHI, followed by nomogram construction. D: Model application includes model evaluation and clinical decision-making.An infographic workflow of clinical prediction modeling for SHI in children with chronic HBV infection.Keywords: chronic hepatitis B virus infection, significant hepatic injury, children, prediction
Language: English
Topic: Hepatitis B Virus Studies
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7166402310
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