Development and validation of a multivariable nomogram predictive of hepatitis B e antigen seroconversion after pregnancy in hepatitis B virus-infected mothers.

Abstract

Current guidelines are controversial regarding the continuation of nucleos(t)ide analogues (NAs) therapy after delivery in Hepatitis B virus (HBV)-infected pregnant women. The postpartum period may be an opportune moment for achieving hepatitis B e antigen (HBeAg) seroconversion earlier with constant NAs therapy due to the restoration of immune function after delivery. We investigated prenatal and pregnant factors associated with HBeAg seroconversion after pregnancy and developed a nomogram to predict HBeAg seroconversion rates, aiding decision-making for optimal management in women. We retrospectively included 489 HBeAg-positive mothers as the training cohort from January 2014 to December 2018 and prospectively enrolled 94 patients as the external validation cohort from January 2019 to December 2021 at the First Affiliated Hospital of Xi'an Jiaotong University. In the training cohort, independent predictors were identified using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Subsequently, multivariate logistic regression was employed to establish the nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Both discrimination and calibration were evaluated through bootstrapping with 1,000 resamples. The external validation cohort was subsequently used to validate the nomogram. Factors such as pregnancy hepatitis flare (OR: 5.122, 95% CI: 2.725-9.928, p < 0.001), NAs therapy after delivery (OR: 15.051, 95% CI: 6.954-37.895, p: <0.001), hepatitis B surface antigen (HBsAg) (OR: 0.549, 95% CI: 0.366-0.812, p: 0.003) and HBV DNA level at delivery (OR: 0.785, 95% CI: 0.619-0.986, p: 0.041) were included in the final risk model. The AUC in the training set was 0.873 (95% CI: 0.839-0.904). The calibration curve of the nomogram closely resembled the ideal diagonal line. DCA showed a significantly better net benefit in the model. External validation also confirmed the reliability of the prediction nomogram. The AUC in the external validation set was 0.889 (95% CI: 0.801-0.953). The calibration curve for the external validation set was in close proximity to the ideal diagonal line. DCA also demonstrated a significant net benefit associated with the predictive model, consistent with the findings in the training set. Finally, the nomogram has been translated into an online risk calculator that is freely available to the public (https://wendyzhong.shinyapps.io/DynNomapp/). We developed a nomogram based on prenatal and pregnant factors to estimate HBeAg seroconversion after delivery in women. This tool provides clinicians with a precise and effective way to identify individuals likely to undergo HBeAg seroconversion postpartum, aiding in decision-making for optimal management.