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Baseline prognostic factors and statistic model to predict early virological response in telbivudine-treated patients
Rui Zhou1, Yuan-Ping Zhou2, Chun Lin3
1Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China ; Infectious Disease Hospital, Meng Chao Hepatobiliary Hospital, Fujian Medical University, Fuzhou, China.
Background:
Hepatitis B virus (HBV) infection is still a worldwide disease, which may cause liver cirrhosis or even hepatocellular carcinoma. Telbivudine is a potent nucleoside analogue used in the treatment of chronic hepatitis B (CHB); however, drug resistance has remained a challenge. As early virological response can predict long-term efficacy of nucleotide analogue treatment, numerous studies have been conducted in this area.
Objectives:
The aim of this study was to establish baseline prognostic factors and a statistical model to predict early virological response in telbivudine-treated CHB patients.
Patients And Methods:
One hundred and eight CHB patients without any experience of nucleotide analogue therapy were assigned to receive telbivudine (600 mg, once daily) for at least 24 weeks, and then were followed up every two weeks. Cox proportional hazard regression model analyses were employed to evaluate baseline variables, and further developing a statistical model to predict early virological response.
Results:
Negative family history of HBV infection (P = 0.000235), baseline higher serum TBIL (P = 0.038714) and AST (P = 0.020684) concentrations, and lower level of HBV-DNA (P = 0.0034784) were identified to be associated with higher possibility of early virological response. A model was established based on these variables to calculate the risk scores (R) for CHB patients. R > -0.38 suggested early virological response to telbivudine. The model was validated among an independent set of 20 patients.
Conclusions:
Family history as well as baseline bilirubin, AST and HBV DNA levels can predict early virological response. The model provides a better tool for response prediction based on the four prognostic factors.
