Hepatocellular carcinoma risk prediction model for the general population: the predictive power of transaminases

Abstract

Background: Risk prediction models for hepatocellular carcinoma are available for individuals with chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections who are at high risk but not for the general population with average or unknown risk. We developed five simple risk prediction models based on clinically available data from the general population.

Methods: A prospective cohort of 428 584 subjects from a private health screening firm in Taiwan was divided into two subgroups-one with known HCV test results (n = 130 533 subjects) and the other without (n = 298 051 subjects). A total of 1668 incident hepatocellular carcinomas occurred during an average follow-up of 8.5 years. Model inputs included age, sex, health history-related variables; HBV or HCV infection-related variables; serum levels of alanine transaminase (ALT), aspartate transaminase (AST), and alfa-fetoprotein (AFP), as well as other variables of routine blood panels for liver function. Cox proportional hazards regression method was used to identify risk predictors of hepatocellular carcinoma. Receiver operating characteristic curves were used to assess discriminatory accuracy of the models. Models were internally validated. All statistical tests were two-sided.

Results: Age, sex, health history, HBV and HCV status, and serum ALT, AST, AFP levels were statistically significant independent predictors of hepatocellular carcinoma risk (all P < .05). Use of serum transaminases only in a model showed a higher discrimination compared with HBV or HCV only (for transaminases, area under the curve [AUC] = 0.912, 95% confidence interval [CI] = 0.909 to 0.915; for HBV, AUC = 0.840, 95% CI = 0.833 to 0.848; and for HCV, AUC = 0.841, 95% CI = 0.834 to 0.847). Adding HBV and HCV data to the transaminase-only model improved the discrimination (AUC = 0.933, 95% CI = 0.929 to 0.949). Internal validation showed high discriminatory accuracy and calibration of these models.

Conclusion: Models with transaminase data were best able to predict hepatocellular carcinoma risk even among subjects with unknown or HBV- or HCV-negative infection status.

Figure 1.

Discriminatory accuracy of the models. Discriminatory accuracy for predicting the development of hepatocellular carcinoma within 10 years was assessed by constructing receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). A) Subcohort without hepatitis C virus (HCV) test. Four models were developed in this subcohort: model 1 was based on health history; model 2 was based on transaminase only; model 3 was based on health history and transaminase; and model 4 was based on health history, transaminase level, alfa-fetoprotein level, and hepatitis B virus (HBV) status. B) Subcohort with HCV test. A fifth model was added to include the HCV status.

Figure 2.

Internal calibration of the risk prediction models. Calibration determined the extent of agreement between predicted and observed events in 10 years, and then a cross-validated calibration plot was generated for the different models. The dashed line indicates the reference line for an ideal model. Solid circles mark the apparent predictions for each decile, and the cross-validated predictions for each decile are marked by a cross symbol. Vertical bars indicate 95% confidence intervals around the apparent prediction. Model 1 was based on health history only; model 2 was based on transaminase only; model 3 was based on health history and transaminase; model 4 was based on health history, transaminase, alfa-fetoprotein level, and hepatitis B virus status; and in model 5, hepatitis C virus (HCV) status was added to model 4. A) Subcohort without HCV test. B) Subcohort with HCV test.

Figure 2.

Internal calibration of the risk prediction models. Calibration determined the extent of agreement between predicted and observed events in 10 years, and then a cross-validated calibration plot was generated for the different models. The dashed line indicates the reference line for an ideal model. Solid circles mark the apparent predictions for each decile, and the cross-validated predictions for each decile are marked by a cross symbol. Vertical bars indicate 95% confidence intervals around the apparent prediction. Model 1 was based on health history only; model 2 was based on transaminase only; model 3 was based on health history and transaminase; model 4 was based on health history, transaminase, alfa-fetoprotein level, and hepatitis B virus status; and in model 5, hepatitis C virus (HCV) status was added to model 4. A) Subcohort without HCV test. B) Subcohort with HCV test.