Background Few models to predict antiviral response of peginterferon were used in hepatitis B e antigen (HBeAg)-positive chronic hepatitis B patients and the prediction efficacy was unsatisfied. Quantitative antibody to hepatitis B core antigen (anti-HBc) is a new predictor of treatment response. We aimed to develop a new model to identify HBeAg-positive Chinese patients who were more likely to respond to peginterferon. Methods Data from 140 peginterferon recipients with HBeAg-positive were applied with generalized additive models and multiple logistic regression analysis to develop a baseline scoring system to predict serological response (SR: HBeAg loss and HBeAg seroconversion 24 weeks post-treatment) and combined response (CR: SR plus serum HBV DNA levels <2000 IU/mL 24 weeks post-treatment). Results Anti-HBc levels, alanine aminotransferase ratio, and HBeAg were retained in the final model. The new model scored from 0 to 3. Among patients with scores of 0, 1, or ≥2, SR was achieved in 6.45% (2/31), 13.21% (7/51), and 55.36% (31/56), respectively, and CR in 3.23% (1/31), 9.43% (5/53), and 25.00% (14/56), respectively. Our model has a higher AUROC for SR comparing to Chan’s (Z = 2.77 > 1.96, p < 0.05) and Lampertico’s (Z = 2.06 > 1.96, p < 0.05) model. The negative predictive value for SR and CR were both 100% in patients with score 0 and hepatitis B surface antigen ≥20,000 IU/mL at week 12. Conclusions Patients with higher scores at baseline were more likely to respond to peginterferon. This new model may predict the treatment response.
Dane particles-associated hepatitis B core antigen in patients with HBsAg-positive chronic hepatitis