Dynamic Prediction of the Risk of Hepatocellular Carcinoma After DAA Treatment for Hepatitis C Patients.

Abstract

Objectives: The aim of this study was to develop and internally validate a hepatocellular carcinoma (HCC) risk prediction model incorporating repeated-measures data (longitudinal model), and compare with baseline predictions.

Methods: A total of 1097 participants with chronic hepatitis C after direct-acting antivirals (DAA) treatment were included in this prospective cohort study. The framework of joint models for longitudinal and survival data was used to construct the longitudinal prediction model. For comparison, a baseline model incorporating the same predictors was constructed through the multivariate Cox regression models. Model performance was evaluated using dynamic discrimination index (DDI), areas under the receiver-operating characteristics curves (AUROC), and Brier scores.

Results: Over a median follow-up of 7.25 years, 60 patients (5.5%) developed HCC. Key risk factors identified were aspartate aminotransferase (AST), cholinesterase, gamma-glutamyl transferase (GGT), albumin, hemoglobin (Hb), platelet count, alpha-fetoprotein (AFP), antigen-125 (CA-125), and carcinoembryonic antigen (CEA). The final joint model, with GGT and CEA removed, showed superior average predictive performance (DDI = .871) compared to models with all predictors included. Validation showed high predictive accuracy for HCC, with AUROCs above .9 for 1-, 3-, 4-, and 5-year predictions. In comparison, the baseline Cox model only achieved mediocre AUROCs of .7 (.75, .67, .69, and .67, respectively).

Conclusion: Compared to static models, our dynamic prediction model can predict the risk of HCC in patients after DAA treatment more accurately, providing better information to distinguish high-risk populations.