Serum models accurately predict liver-related clinical outcomes in chronic hepatitis C

BACKGROUND AND AIM

This study developed liver outcome scores in chronic hepatitis C (CHC) that directly predict liver-related death, hepatocellular carcinoma (HCC), and liver decompensation.

METHODS

Six hundred seventeen CHC patients were followed up for a mean of 6 years and randomized into a training set (n = 411) and a validation set (n = 206). Clinical outcomes were determined using a population-based data linkage system.

RESULTS

In the training set, albumin, gamma-glutamyl transpeptidase, hyaluronic acid, age, and sex were in the final model to predict 5-year liver-related death (area under receiver operating characteristic curve [AUROC] 0.95). Two cut points (4.0 and 5.5) defined three risk groups with an incidence rate for liver-related death of 0.1%, 2%, and 13.2%, respectively (P < 0.001). Albumin, gamma-glutamyl transpeptidase, hyaluronic acid, age, and sex were used to predict 5-year liver decompensation (AUROC 0.90). A cut point of 4.5 gave a sensitivity of 94% and a specificity of 84% to predict 5-year decompensation and defined two groups with an incidence rate for decompensation of 0.2% and 5.8%, respectively (P < 0.001). Alkaline phosphatase, α2-macroglobulin, age, and sex were used to predict 5-year HCC occurrence (AUROC 0.95). A cut-point of eight had a sensitivity of 90% and specificity of 88% to predict 5-year HCC occurrence and defined two groups with an incidence rate for HCC of 0.2% and 5.6%, respectively (P < 0.001). Similar results were obtained using the validation set.

CONCLUSIONS

All three liver outcome scores had excellent predictive accuracy and were able to stratify risk into clinical meaningful categories for CHC patients.