Frontiers in public health · 2025
Predictive model for neonatal HBV infection risk in infants of HBV-infected mothers in China: an observational study.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Liu Xingzhu, Gong Chuxiong, Du Xiaoliang, Yang Yanfei, Kui Liyue, Wang Lin, Hao Tingting, Hou Yao, Wang Feng, Fan Na, Wu Yuqin
Аннотация
OBJECTIVE: In China, vertical transmission is the primary route of hepatitis B virus (HBV) transmission from mothers to their children. This study aimed to develop a predictive model for assessing the risk of HBV infection in newborns born to mothers with HBV infection. Additionally, the model was validated across subgroups based on child stage, gender, and race to facilitate the early identification of high-risk newborns and the development of personalized preventive measures.
METHODS: We collected medical records of 443 newborns whose mothers had a history of hepatitis B. We compared case characteristics between newborns with and without HBV infection and identified key factors using LASSO approach to construct a multivariate logistic regression prediction model. The model's performance was evaluated using the ROC curve, calibration curve, and decision curve analysis. The stability of the predictions was further validated through 5-fold cross-validation. Finally, subgroup analyses were conducted based on sex, age, and race.
RESULTS: We identified alanine aminotransferase, direct bilirubin, gamma-glutamyl transferase, HBsAg, and HBcAb as key factors for the prediction model. The model achieved an area under the ROC curve of 0.890 (95% CI: 0.831-0.949). The calibration curve and decision curve analysis confirmed the model's accuracy, and the 5-fold cross-validation reaffirmed its internal stability. The model also demonstrated robust validation across different age, gender, and race subgroups.
CONCLUSION: Our study developed a reliable predictive model for assessing the risk of HBV infection among newborns of HBV-infected mothers in China. The model performed well across various child stages, genders, and racial subgroups. This research provides a foundation for the early identification of newborns at high risk for HBV infection, thereby reducing the risk of neonatal HBV transmission and supporting the rationale for individualized precision treatment.
METHODS: We collected medical records of 443 newborns whose mothers had a history of hepatitis B. We compared case characteristics between newborns with and without HBV infection and identified key factors using LASSO approach to construct a multivariate logistic regression prediction model. The model's performance was evaluated using the ROC curve, calibration curve, and decision curve analysis. The stability of the predictions was further validated through 5-fold cross-validation. Finally, subgroup analyses were conducted based on sex, age, and race.
RESULTS: We identified alanine aminotransferase, direct bilirubin, gamma-glutamyl transferase, HBsAg, and HBcAb as key factors for the prediction model. The model achieved an area under the ROC curve of 0.890 (95% CI: 0.831-0.949). The calibration curve and decision curve analysis confirmed the model's accuracy, and the 5-fold cross-validation reaffirmed its internal stability. The model also demonstrated robust validation across different age, gender, and race subgroups.
CONCLUSION: Our study developed a reliable predictive model for assessing the risk of HBV infection among newborns of HBV-infected mothers in China. The model performed well across various child stages, genders, and racial subgroups. This research provides a foundation for the early identification of newborns at high risk for HBV infection, thereby reducing the risk of neonatal HBV transmission and supporting the rationale for individualized precision treatment.