Background and aims To explore the incidence of alcohol-related hepatocellular carcinoma (HCC), evaluate possible synergisms between alcohol and well-known risk factors associated with HCC and establish a nomogram to predict alcohol-associated liver disease (ALD)-related HCC risk.

Methods A database of 136 571 inpatients in the Fifth Medical Center of Chinese PLA General Hospital from 2002 to 2018 with chronic liver disease was established. Data were collected by medical records review. Multivariate logistic regression was used to identify the independent high-risk factors associated with HCC, and then were incorporated into a novel nomogram. Afterward, the new established model was validated using external cohort by receiver operating characteristic curves analysis. For external cohort, 1646 patients with ALD admitted to our hospital from 2019 to 2021 were included. ALD was diagnosed on the basis of a history of sustained heavy alcohol intake greater than 40 g/day for men and 20 g/day for women for >5 years, clinical evidence of liver disease and supporting laboratory abnormalities.

Results Over the last 17 years, trends showed obviously increases in ALD. ALD-related HCC experienced a significant increase from 5.8% to 30.7%, whereas hepatitis B virus (HBV)-related HCC declined from 77.6% to 52.0%. In patients with ALD-related HCC (5119), 3816 (74.54%) cases had HBV infection, 493 (9.63%) cases had hepatitis C virus (HCV) infection, 71 (1.39%) cases were coinfected with both HBV and HCV, and 739 (14.44%) cases had neither HBV nor HCV infection. Drinking years, age, diabetes mellitus, HBV infection, liver cirrhosis and male sex were associated with increased risk of HCC in patients with ALD. A nomogram had a concordance index of 0.786 and had well-fitted calibration curves. These results were successfully validated both in the internal cohort and external cohort.

Conclusion The prevalence of ALD and ALD-related HCC has been increased dramatically. The nomogram model established here with its high accuracy and easy-to-use features achieved an optimal prediction of HCC development in patients with ALD, which can help clinicians to develop an individualised and precise treatment strategy.