This probably relates to the fact that clinical decompensation is related to the severity of portal hypertension and selleck products synthetic dysfunction. Individual ALT and AST values correlate better with underlying necroinflammation and have been shown to be predictive of fibrosis progression.7, 14 The AST/ALT ratio has been shown to be a predictor of cirrhosis15 but not of hepatic synthetic dysfunction.11 It is possible that if changes in AST and ALT occur

in the same direction, it would have a minimal effect on the AST/ALT ratio. Thus, monitoring the AST/ALT ratio was not found to be helpful in predicting a clinical decompensation outcome. A model including baseline platelet count and albumin together with worsening serum albumin and AST/ALT ratio (Model IIIB) was the best predictor of find more liver-related death or liver transplant. Interestingly, neither baseline

serum bilirubin nor change in bilirubin level over 24 months was predictive of liver-related death or liver transplant. This may be related to the fact that a substantial number of deaths and liver transplants in this analysis were due to HCC and not decompensation. Removal of HCC as an outcome resulted in bilirubin being a significant predictor of liver-related outcomes. Many models have been developed to predict severity of fibrosis and cirrhosis in patients with chronic hepatitis C. Only a few have looked at clinical outcomes but these models have used laboratory tests that are not widely available or the sample size has been small with limited follow-up.16 The strength of click here this study was the large number of patients who were prospectively monitored for over 8 years for liver outcomes and each outcome was adjudicated by

a review panel. The combined model using baseline laboratory values in combination with a change in the laboratory value over a 24-month period was the most accurate at predicting risk of a clinical outcome. We chose not to consider the most recent laboratory values when determining whether a change in laboratory values was important. This analytic approach would necessitate selecting an arbitrary timepoint as “current.” Moreover, the approach we selected more closely resembles that used in clinical practice, beginning with a baseline laboratory value and monitoring the change prospectively. We selected a 24-month period for calculating change in laboratory values from baseline. This was a compromise over an earlier timepoint which would not allow sufficient time for the laboratory values to change (or only detect patients with rapid changes) and a later time period such as 48 months, which meant patients with early outcomes, would be excluded. We believe using changes in laboratory values over a shorter time period (<24 months) in our model would underestimate the risk of a clinical outcome.