Bayesian network to predict hepatitis B surface antigen seroclearance in chronic hepatitis B patients.

Abstract

There is a need for an interpretable, accurate and interactions-considered model for predicting hepatitis B surface antigen (HBsAg) seroclearance. We aimed to construct a Bayesian network (BN) model using available medical records to predict HBsAg seroclearance in chronic hepatitis B (CHB) patients, and to evaluate the model's performance. This was a case-control study. A total of 1966 consecutive CHB patients (mean age 39.04±11.23years) between January 2006 and June 2015 were included. The demographic and clinical characteristics, laboratory data and imaging parameters were obtained and used to build a BN model to estimate the probability of HBsAg seroclearance. Baseline serum HBsAg and hepatitis Be antigen (HBeAg) levels, virological response and HBeAg seroclearance were the most significant predictors of HBsAg seroclearance. The post-test probability table showed that patients with baseline HBsAg concentrations ≤2000IU/mL, negative baseline HBeAg, an initial virological response and without HBeAg seroclearance (i.e. no recurrence of HBeAg positivity during follow-up) were most likely to have HBsAg seroclearance. The constructed BN model had an area under the receiver operating characteristic curves of 0.896 (95% confidence interval [CI]: 0.892, 0.899), a sensitivity of 0.840 (95% CI: 0.833, 0.846), a specificity of 0.880 (95% CI: 0.876, 0.884) and an accuracy of 0.878 (95% CI: 0.874, 0.882) for predicting HBsAg seroclearance. The established BN model accurately estimated the probability of HBsAg seroclearance and is a promising tool to assist clinical decision-making.