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Hepatitis B surface antigen (HBsAg) seroclearance is considered the functional cure and the optimal treatment endpoint for chronic hepatitis B (CHB). Patients with CHB who cleared HBsAg generally have a favorable clinical course with minimal risk of developing hepatocellular carcinoma (HCC) or cirrhotic complications. Nevertheless, a minority of patients still develop HCC despite HBsAg seroclearance. While patients with liver cirrhosis are still recommended for HCC surveillance, whether other non-cirrhotic patients who achieved HBsAg seroclearance should remain on HCC surveillance remains unclear. This review provides an overview of the incidence of HBsAg seroclearance, the factors associated with the occurrence of HBsAg seroclearance, the durability of HBsAg seroclearance, the risk of developing HCC after HBsAg seroclearance, the risk factors associated with HCC development after HBsAg seroclearance, the role of HCC risk scores, and the implications on HCC surveillance. Existing HCC risk scores have a reasonably good performance in patients after HBsAg seroclearance. In the era of artificial intelligence, future HCC risk prediction models based on artificial intelligence and longitudinal clinical data may further improve the prediction accuracy to establish a foundation of a risk score-based HCC surveillance strategy. As different novel hepatitis B virus (HBV) antiviral agents aiming at HBsAg seroclearance are under active development, new knowledge is anticipated on the natural history and HCC risk prediction of patients treated with new HBV drugs.

Background & Aims: Alcohol-associated liver disease (ALD) burden has been rising globally, fueled by increases in high-risk alcohol use following the coronavirus disease 2019 (COVID-19) pandemic. We evaluated trends in annual incidence of alcohol-associated hepatitis (AH) before and following the onset of the COVID-19 pandemic across two geographically distinct populations in the USA and Hong Kong. Methods: Using US national Veterans Affairs (VA) data and Hong Kong territory-wide data, trends in annual incidence of AH were evaluated from 2000 to 2023. AH was identified using a combination of International Classification of Diseases (ICD)-9/10 diagnostic codes, laboratory data, and available alcohol use data. Results: Among the VA data, annual incidence of AH rose steadily from 39.4 to 53.7 per 100,000 persons (2010–2020), then declined to 36.2 per 100,000 persons in 2023. Annual AH incidence was substantially lower in Hong Kong, but demonstrated similar trends, peaking at 0.28 per 100,000 persons during the first year of the pandemic. Among both cohorts, incidence of AH was significantly higher in men vs. women, but particularly for the VA cohort, the increase in AH incidence was more rapid in women. Among both cohorts, the highest incidence of AH in 2023 was among the 40–49-year age group (VA: 72.7 per 100,000 persons; Hong Kong: 1.89 per 100,000 persons). Conclusions: We provide a comprehensive analysis of epidemiological trends in AH incidence across two distinct populations, highlighting the need for continued awareness of targeted interventions to curb unhealthy alcohol use and its complications. Impact and implications:: Alcohol-associated hepatitis (AH) is a severe complication of high-risk alcohol use associated with significant morbidity and mortality. Increasing alcohol use fueled by the COVID-19 pandemic has led to parallel increases in alcohol-related comorbidities. The current study provides a comprehensive analysis of trends in the incidence of AH across two distinct world regions before and following the onset of the COVID-19 pandemic. Better identification of epidemiological trends in AH incidence as well as highlighting populations most affected can help target public health resources and health system interventions to address the dangers of high-risk alcohol use more effectively.

Background & Aims: The latest Baveno VII consensus has provided guidance for identifying patients who have truly recompensated from those with hepatic decompensation. This study aimed to evaluate patients’ transplant-free survival in three different stages of cirrhosis. Methods: All patients with chronic HBV infection and liver cirrhosis treated with oral nucleos(t)ide analogues from March 2006 to December 2022 were identified from a territory-wide database in Hong Kong. Patients with follow-up duration of

Background/Aims We aimed to determine the association between blood urea level and incident cirrhosis, hepatic decompensation, and hepatocellular carcinoma in chronic liver disease (CLD) patients. Methods The association between blood urea level and liver fibrosis/liver-related events were evaluated on continuous scale with restricted cubic spline curves based on generalized additive model or Cox proportional hazards models. Then, the above associations were evaluated by urea level within intervals. Results Among 4,282 patients who had undergone liver stiffness measurement (LSM) by transient elastography, baseline urea level had a U-shaped association with LSM and hepatic decompensation development after a median follow-up of 5.5 years. Compared to patients with urea of 3.6–9.9 mmol/L, those with urea ≤3.5 mmol/L (adjusted hazard ratio [aHR], 4.15; 95% confidence interval [CI], 1.68–10.24) and ≥10 mmol/L (aHR, 5.22; 95% CI, 1.86–14.67) had higher risk of hepatic decompensation. Patients with urea ≤3.5 mmol/L also had higher risk of incident cirrhosis (aHR, 3.24; 95% CI, 1.50–6.98). The association between low urea level and incident cirrhosis and hepatic decompensation was consistently observed in subgroups by age, gender, albumin level, and comorbidities. The U-shaped relationship between urea level and LSM was validated in another population screening study (n=917). Likewise, urea ≤3.5 mmol/L was associated with a higher risk of incident cirrhosis in a territory-wide cohort of 12,476 patients with nonalcoholic fatty liver disease at a median follow-up of 9.9 years (aHR, 1.27; 95% CI, 1.03–1.57). Conclusions We identified a U-shaped relationship between the urea level and liver fibrosis/incident cirrhosis/hepatic decompensation in patients with CLD.

Background & Aims: Accurate hepatocellular carcinoma (HCC) risk prediction facilitates appropriate surveillance strategy and reduces cancer mortality. We aimed to derive and validate novel machine learning models to predict HCC in a territory-wide cohort of patients with chronic viral hepatitis (CVH) using data from the Hospital Authority Data Collaboration Lab (HADCL). Methods: This was a territory-wide, retrospective, observational, cohort study of patients with CVH in Hong Kong in 2000–2018 identified from HADCL based on viral markers, diagnosis codes, and antiviral treatment for chronic hepatitis B and/or C. The cohort was randomly split into training and validation cohorts in a 7:3 ratio. Five popular machine learning methods, namely, logistic regression, ridge regression, AdaBoost, decision tree, and random forest, were performed and compared to find the best prediction model. Results: A total of 124,006 patients with CVH with complete data were included to build the models. In the training cohort (n = 86,804; 6,821 HCC), ridge regression (area under the receiver operating characteristic curve [AUROC] 0.842), decision tree (0.952), and random forest (0.992) performed the best. In the validation cohort (n = 37,202; 2,875 HCC), ridge regression (AUROC 0.844) and random forest (0.837) maintained their accuracy, which was significantly higher than those of HCC risk scores: CU-HCC (0.672), GAG-HCC (0.745), REACH-B (0.671), PAGE-B (0.748), and REAL-B (0.712) scores. The low cut-off (0.07) of HCC ridge score (HCC-RS) achieved 90.0% sensitivity and 98.6% negative predictive value (NPV) in the validation cohort. The high cut-off (0.15) of HCC-RS achieved high specificity (90.0%) and NPV (95.6%); 31.1% of patients remained indeterminate. Conclusions: HCC-RS from the ridge regression machine learning model accurately predicted HCC in patients with CVH. These machine learning models may be developed as built-in functional keys or calculators in electronic health systems to reduce cancer mortality. Lay summary: Novel machine learning models generated accurate risk scores for hepatocellular carcinoma (HCC) in patients with chronic viral hepatitis. HCC ridge score was consistently more accurate than existing HCC risk scores. These models may be incorporated into electronic medical health systems to develop appropriate cancer surveillance strategies and reduce cancer death.

Background & Aims: Antiviral treatment is known to improve survival in patients with chronic hepatitis B (CHB)-related hepatocellular carcinoma (HCC). Yet, the treatment uptake in CHB patients remains low. We aimed to report the secular trend in antiviral treatment uptake from 2007–2017, and to compare the effect of different nucleos(t)ide analogue (NA) initiation times (before vs. after HCC diagnosis) on survival. Methods: A 3-month landmark analysis was used to compare overall survival in patients not receiving NA treatment (i.e. no NA), patients receiving NAs after their first HCC treatment (i.e. post-HCC NA), and patients receiving NAs ≤3 months before their first HCC treatment (i.e. pre-HCC NA). A propensity score-weighted Cox proportional hazards model was used to balance clinical characteristics between the 3 groups and to estimate hazard ratios (HRs). Results: The uptake of antiviral treatment in HCC patients increased from 47.3% in 2007 to 98.3% in 2017. The pre-HCC NA group contributed mostly to the uptake rate, which increased from 72.7% to 96.0% in the past decade. In addition, 3,843 CHB patients (407 no NA; 2,932 pre-HCC NA; 504 post-HCC NA) with HCC, receiving at least 1 type of HCC treatment, were included in the analysis. Lack of NA treatment at the time of HCC diagnosis increased the risk of death (weighted HR 3.05; 95% CI 2.70–3.44; p