Early Serum HBsAg Drop Is a Strong Predictor of HBeAg Seroconversion and HBsAg Loss to Pegylated Interferon Alfa-2a in Chronic Hepatitis B Patients with Prior Nucleos(t)ide Analogue Exposure

Prediction model for hepatitis B e antigen seroconversion in chronic hepatitis B with peginterferon-alfa treated based on a response-guided therapy strategy

BACKGROUND

Models for predicting hepatitis B e antigen (HBeAg) seroconversion in patients with HBeAg-positive chronic hepatitis B (CHB) after nucleos(t)ide analog treatment are rare.

AIM

To establish a simple scoring model based on a response-guided therapy (RGT) strategy for predicting HBeAg seroconversion and hepatitis B surface antigen (HBsAg) clearance.

METHODS

In this study, 75 previously treated patients with HBeAg-positive CHB underwent a 52-week peginterferon-alfa (PEG-IFNα) treatment and a 24-wk follow-up. Logistic regression analysis was used to assess parameters at baseline, week 12, and week 24 to predict HBeAg seroconversion at 24 wk post-treatment. The two best predictors at each time point were used to establish a prediction model for PEG-IFNα therapy efficacy. Parameters at each time point that met the corresponding optimal cutoff thresholds were scored as 1 or 0.

RESULTS

The two most meaningful predictors were HBsAg ≤ 1000 IU/mL and HBeAg ≤ 3 S/CO at baseline, HBsAg ≤ 600 IU/mL and HBeAg ≤ 3 S/CO at week 12, and HBsAg ≤ 300 IU/mL and HBeAg ≤ 2 S/CO at week 24. With a total score of 0 vs 2 at baseline, week 12, and week 24, the response rates were 23.8%, 15.2%, and 11.1% vs 81.8%, 80.0%, and 82.4%, respectively, and the HBsAg clearance rates were 2.4%, 3.0%, and 0.0%, vs 54.5%, 40.0%, and 41.2%, respectively.

CONCLUSION

We successfully established a predictive model and diagnosis-treatment process using the RGT strategy to predict HBeAg and HBsAg seroconversion in patients with HBeAg-positive CHB undergoing PEG-IFNα therapy.

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

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 ± 11.23 years) 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 ≤2000 IU/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.