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Published on: May 10, 2022
Models for predicting hepatitis B e antigen seroconversion in response to interferon-α in chronic hepatitis B
Chang-Tai Wang1, Ya-Fei Zhang1, Bing-Hu Sun1
1Chang-Tai Wang, Ya-Fei Zhang, Bing-Hu Sun, Yu Dai, Hui-Lan Zhu, Xu Li, Zhen-Hua Zhang, Department of Infectious Diseases, the First Affiliated Hospital, Anhui Medical University, Hefei 230022, Anhui Province, China.
Aim:
To develop models to predict hepatitis B e antigen (HBeAg) seroconversion in response to interferon (IFN)-α treatment in chronic hepatitis B patients.
Methods:
We enrolled 147 treatment-naïve HBeAg-positive chronic hepatitis B patients in China and analyzed variables after initiating IFN-α1b treatment. Patients were tested for serum alanine aminotransferase (ALT), hepatitis B virus-DNA, hepatitis B surface antigen (HBsAg), antibody to hepatitis B surface antigen, HBeAg, antibody to hepatitis B e antigen (anti-HBe), and antibody to hepatitis B core antigen (anti-HBc) at baseline and 12 wk, 24 wk, and 52 wk after initiating treatment. We performed univariate analysis to identify response predictors among the variables. Multivariate models to predict treatment response were constructed at baseline, 12 wk, and 24 wk.
Results:
At baseline, the 3 factors correlating most with HBeAg seroconversion were serum ALT level > 4 × the upper limit of normal (ULN), HBeAg ≤ 500 S/CO, and anti-HBc > 11.4 S/CO. At 12 wk, the 3 factors most associated with HBeAg seroconversion were HBeAg level ≤ 250 S/CO, decline in HBeAg > 1 log10 S/CO, and anti-HBc > 11.8 S/CO. At 24 wk, the 3 factors most associated with HBeAg seroconversion were HBeAg level ≤ 5 S/CO, anti-HBc > 11.4 S/CO, and decline in HBeAg > 2 log10 S/CO. Each variable was assigned a score of 1, a score of 0 was given if patients did not have any of the 3 variables. The 3 factors most strongly correlating with HBeAg seroconversion at each time point were used to build models to predict the outcome after IFN-α treatment. When the score was 3, the response rates at the 3 time points were 57.7%, 83.3%, and 84.0%, respectively. When the score was 0, the response rates were 2.9%, 0.0%, and 2.1%, respectively.
Conclusion:
Models with good negative and positive predictive values were developed to calculate the probability of response to IFN-α therapy.
