Source
Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan.
Abstract
BACKGROUND & AIMS:
Integrating host and HBV characteristics, this study aimed to develop models for predicting long-term cirrhosis and hepatocellular carcinoma risk in chronic hepatitis B patients.
METHODS:
This analysis included HBsAg-seropositive and anti-HCV-seronegative participants from R.E.V.E.A.L.-HBV cohort. The newly-developed cirrhosis and hepatocellular carcinoma was ascertained through regular follow-up ultrasonography, computerized linkage with national health databases, and medical chart reviews. Two-thirds participants were allocated for risk model derivation and another one-third for model validation. The risk prediction model included age, gender, HBeAg serostatus, serum levels of HBV DNA, and alanine aminotransferase (ALT), quantitative serum HBsAg levels and HBV genotypes. The family history was included in prediction model for hepatocellular carcinoma additionally. Cox's proportional hazards regression coefficients for cirrhosis and hepatocellular carcinoma predictors were converted into risk scores. The areas under receiver operating curve (AUROCs) were used to evaluate the performance of risk models. Results: 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 hepatocellular carcinoma. The risk scores estimated from the derivation set could accurately categorize participants with low, medium and high cirrhosis and hepatocellular carcinoma risk in validation set (p<0.001). The AUROCs for predicting 3-, 5- and 10-year cirrhosis risk ranged 0.83-0.86 and 0.79-0.82 for the derivation and validation set, respectively. The AUROC for predicting 5-, 10-, 15-year risk of hepatocellular carcinoma ranged 0.86-0.89 and 0.84-0.87 in the derivation and validation set, respectively.
CONCLUSIONS:
The risk prediction models of cirrhosis and hepatocellular carcinoma 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.