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


CONCLUSIONS
Models with transaminase data were best able to predict hepatocellular carcinoma risk even among subjects with unknown or HBV- or HCV-negative infection status.
Institute of Population Science, National Health Research Institutes, Zhunan, Taiwan.
Chi-Pang
CP
Wen
Jie
J
Lin
Yi Chen
YC
Yang
Min Kuang
MK
Tsai
Chwen Keng
CK
Tsao
Carol
C
Etzel
Maosheng
M
Huang
Chung Yi
CY
Hsu
Yuanqing
Y
Ye
Lopa
L
Mishra
Ernest
E
Hawk
Xifeng
X
Wu
D000328:Adult; D000368:Aged; D000410:Alanine Transaminase / Q000097:blood*; D019540:Area Under Curve; D001219:Aspartate Aminotransferases / Q000097:blood*; D014408:Biomarkers, Tumor / Q000097:blood*; D006528:Carcinoma, Hepatocellular / Q000201:enzymology* / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000201:enzymology* / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D013624:Taiwan / Q000453:epidemiology; D000509:alpha-Fetoproteins / Q000378:metabolism
D014408:Biomarkers, Tumor; D000509:alpha-Fetoproteins; D001219:Aspartate Aminotransferases; D000410:Alanine Transaminase