Serum models accurately predict liver related clinical outcomes in chronic hepatitis C.
Journal of Gastroenterology and Hepatology, Early View (Online First).
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: 617 CHC patients were followed for a mean of six 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 (GGT), hyaluronic acid (HA), age and sex were in the final model to predict five year liver related death (AUROC 0.95). Two cut points (4.0, 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, GGT, HA, age and sex were used to predict five year liver decompensation (AUROC 0.90). A cut point of 4.5 gave a sensitivity of 94% and a specificity of 84% to predict five 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 five year HCC occurrence (AUROC 0.95). A cut point of 8 had a sensitivity of 90% and specificity of 88% to predict five 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.
serum model, liver related death, liver decompensation, hepatocellular carcinoma