Chronic hepatitis C (HCV) is a primary cause of hepatocellular carcinoma (HCC). Although antiviral treatment reduces risk of HCC, few studies quantify the impact of treatment on long-term risk in the era of direct-acting antivirals (DAA). Using data from the Chronic Hepatitis Cohort Study, we evaluated the impact of treatment type (DAA, interferon-based [IFN], or none) and outcome (sustained virological response [SVR] or treatment failure [TF]) on risk of HCC. We then developed and validated a predictive risk model. 17186 HCV patients were followed until HCC, death or last follow-up. We used extended landmark modelling, with time-varying covariates and propensity score justification and generalized estimating equations with a link function for discrete time-to-event data. Death was considered a competing risk. We observed 586 HCC cases across 104,000 interval-years of follow-up. SVR from DAA or IFN-based treatment reduced risk of HCC (aHR 0.13, 95% CI 0.08-0.20; and aHR 0.45, 95% CI 0.31-0.65); DAA SVR reduced risk more than IFN SVR (aHR 0.29, 95% CI 0.17-0.48). Independent of treatment, cirrhosis was the strongest risk factor for HCC (aHR 3.94, 95% CI 3.17-4.89 vs. no cirrhosis). Other risk factors included male sex, White race and genotype 3. Our six-variable predictive model had 'excellent' accuracy (AUROC 0.94) in independent validation. Our novel landmark interval-based model identified HCC risk factors across antiviral treatment status and interactions with cirrhosis. This model demonstrated excellent predictive accuracy in a large, racially diverse cohort of patients and could be adapted for 'real world' HCC monitoring.
Keywords: gender; genotype 3; liver cancer; machine learning; predictive modelling.
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