Background & Aims: A new hepatocellular carcinoma risk prediction model, PAGE-B, which includes age, gender and platelet count as constituent variables, has recently been proposed in Caucasian chronic hepatitis B patients. We validated PAGE-B model and compared its accuracy with that of conventional risk prediction models in Asian chronic hepatitis B patients. Methods: Chronic hepatitis B patients treated with entecavir or tenofovir were consecutively recruited. The performance of PAGE-B and three conventional risk prediction models (CU-HCC, GAG-HCC and REACH-B) were analysed. Results: A total of 1092 chronic hepatitis B patients (668 men, 61.2%) were selected between August 2006 and January 2015. The mean age was 48 years. During the follow-up period (median, 43.6 months), 36 (3.3%) patients developed hepatocellular carcinoma. Older age (hazard ratio [HR]=1.077), male gender (HR=3.676) and lower platelet count (HR=0.984) were independent predictors of hepatocellular carcinoma development. The PAGE-B showed similar area under receiver operating characteristic curves (AUROCs) to GAG-HCC and CU-HCC at 3 years (0.777 vs 0.793 and 0.743, respectively; all P>.05) and 5 years (0.799 vs 0.803 and 0.744, respectively; all P>.05), whereas the AUROCs of PAGE-B were significantly higher than those of the REACH-B (0.602 at 3 years and 0.572 at 5 years, P<.05). Conclusions: Our study demonstrated that PAGE-B is applicable to Asian chronic hepatitis B patients receiving ETV or TDF therapy. The PAGE-B showed similar predictive performance to GAG-HCC and CU-HCC.
Bibliographical noteFunding Information:
This study was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2016R1A1A1A05005138), and the Ministry of Education, (2015R1D1A1A01058653). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
All Science Journal Classification (ASJC) codes