AASLD: American Association for the Study of Liver Diseases
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Predictive Models of Cirrhosis and HCC in CHB 

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Integrating host and HBV characteristics, this study aimed to develop models for predicting long-term cirrhosis and hepatocellular carcinoma (HCC) risk in chronic hepatitis B virus (HBV) patients. This analysis included hepatitis B surface antigen (HBsAg)-seropositive and anti-HCV-seronegative participants from the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer in HBV (R.E.V.E.A.L.-HBV) cohort. Newly developed cirrhosis and HCC were ascertained through regular follow-up ultrasonography, computerized linkage with national health databases, and medical chart reviews. Two-thirds of the participants were allocated for risk model derivation and another one-third for model validation. The risk prediction model included age, gender, HBV e antigen (HBeAg) serostatus, serum levels of HBV DNA, and alanine aminotransferase (ALT), quantitative serum HBsAg levels, and HBV genotypes. Additionally, the family history was included in the prediction model for HCC. Cox's proportional hazards regression coefficients for cirrhosis and HCC predictors were converted into risk scores. The areas under receiver operating curve (AUROCs) were used to evaluate the performance of risk models. Elder age, male, HBeAg, genotype C, and increasing levels of ALT, HBV DNA, and HBsAg were all significantly associated with an increased risk of cirrhosis and HCC. The risk scores estimated from the derivation set could accurately categorize participants with low, medium, and high cirrhosis and HCC risk in the validation set (P < 0.001). The AUROCs for predicting 3-year, 5-year, and 10-year cirrhosis risk ranged 0.83-0.86 and 0.79-0.82 for the derivation and validation sets, respectively. The AUROC for predicting 5-year, 10-year, 15-year risk of HCC ranged 0.86-0.89 and 0.84-0.87 in the derivation and validation sets, respectively. Conclusion: The risk prediction models of cirrhosis and HCC by integrating host and HBV profiles have excellent prediction accuracy and discriminatory ability. They may be used for clinical management of chronic hepatitis B patients.