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Background & Aims: Aspirin and statins have been suggested to prevent hepatocellular carcinoma (HCC). However, the combined effects of aspirin and statins on HCC risk in patients with chronic hepatitis B (CHB) are not clear. Methods: A nationwide nested case‐control study was performed with data from the National Health Insurance Service gathered between 2005 and 2015 in Korea. In a cohort of 538,135 treatment‐naïve, non‐cirrhotic patients with CHB, 6,539 HCC cases were matched to 26,156 controls and were analysed by conditional logistic regression. Separate historical cohort studies for each drug were analysed by time‐dependent Cox regression as a sensitivity analysis. Results: In the nested case‐control study, statins (OR 0.34; 95% CI 0.32‐0.37) and aspirin (OR 0.92; 95% CI 0.85‐0.99) were significantly associated with a HCC risk reduction. However, dose‐dependent risk reduction was observed only with statins. By sensitivity analysis in the historical cohorts, statin users (n = 244,455; HR 0.67; 95% CI 0.66‐0.68) and aspirin users (n = 288,777; HR 0.81; 95% CI 0.80‐0.82) had significantly lower HCC risk. In the drug‐stratified analyses, statins were associated with significantly reduced risk of HCC regardless of aspirin, whereas aspirin did not show such associations. Conclusions: In this nationwide population‐based study of patients with CHB, statin use was consistently associated with a significant and dose‐dependent reduction in HCC risk. In contrast, the association between aspirin use and HCC risk reduction was not dose‐dependent and was suggested to be confounded by statins. [ABSTRACT FROM AUTHOR]

Background & Aims: There are currently several prediction models for hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) receiving oral antiviral therapy. However, most models are based on pre‐treatment clinical parameters. The current study aimed to develop a novel and practical prediction model for HCC by using both pre‐ and post‐treatment parameters in this population. Methods: We included two treatment‐naïve CHB cohorts who were initiated on oral antiviral therapies: the derivation cohort (n = 1480, Korea prospective SAINT cohort) and the validation cohort (n = 426, the US retrospective Stanford Bay cohort). We employed logistic regression, decision tree, lasso regression, support vector machine and random forest algorithms to develop the HCC prediction model and selected the most optimal method. Results: We evaluated both pre‐treatment and the 12‐month clinical parameters on‐treatment and found the 12‐month on‐treatment values to have superior HCC prediction performance. The lasso logistic regression algorithm using the presence of cirrhosis at baseline and alpha‐foetoprotein and platelet at 12 months showed the best performance (AUROC = 0.843 in the derivation cohort. The model performed well in the external validation cohort (AUROC = 0.844) and better than other existing prediction models including the APA, PAGE‐B and GAG models (AUROC = 0.769 to 0.818). Conclusions: We provided a simple‐to‐use HCC prediction model based on presence of cirrhosis at baseline and two objective laboratory markers (AFP and platelets) measured 12 months after antiviral initiation. The model is highly accurate with excellent validation in an external cohort from a different country (AUROC 0.844) (Clinical trial number: KCT0003487). [ABSTRACT FROM AUTHOR]