Dynamic prediction of risk of liver-related outcomes in chronic hepatitis C using routinely collected data

Accuracy of risk assessments for clinical outcomes in patients with chronic liver disease has been limited given the nonlinear nature of disease progression. Longitudinal prediction models may more accurately capture this dynamic risk. The aim of this study was to construct accurate models of short-and long-term risk of disease progression in patients with chronic hepatitis C by incorporating longitudinal clinical data. Data from the Hepatitis C Antiviral Long-term Treatment Against Cirrhosis trial were analysed (n = 533 training cohort; n = 517 validation cohort). Outcomes included a composite liver outcome (liver-related death, decompensation, hepatocellular carcinoma (HCC) or liver transplant), decompensation, HCC and overall mortality. Longitudinal models were constructed for risk of outcomes at 1, 3 and 5 years and compared with models using data at baseline only or baseline and a single follow-up time point. A total of 25.1% of patients in the training and 20.8% in the validation cohort had an outcome during a median follow-up of 6.5 years (range 0.5-9.2). The most important predictors were as follows: albumin, aspartate aminotransferase/alanine aminotransferase ratio, bilirubin, alpha-fetoprotein and platelets. Longitudinal models outperformed baseline models with higher true-positive rates and negative predictive values. The areas under the receiver-operating characteristic curve for the composite longitudinal model were 0.89 (0.80-0.96), 0.83 (0.76-0.88) and 0.81 (0.75-0.87) for 1-, 3-, and 5-year risk prediction, respectively. Model performance was retained for decompensation and overall mortality but not HCC. Longitudinal prediction models provide accurate risk assessments and identify patients in need of intensive monitoring and care.