Unmet needs in the clinical management of chronic hepatitis B infection.

The hepatitis B virus (HBV) remains a global problem despite effective tools to prevent, diagnosis, and control it. Unmet needs are identifiable across its clinical care cascade, underlining the challenges providers face in delivering effective care for patients with chronic hepatitis B. The review herein will focus on three timely clinical issues in HBV. This includes efforts to optimize delivery of perinatal HBV care, improve HBV-related hepatocellular carcinoma risk stratification models, and clarify the role of finite therapy in the HBV treatment algorithm. Important developments within these three topics will be addressed with the goal to motivate further investigation and optimization of these treatment strategies for HBV.

Outcomes of a 1-Year Pilot Study Implementing Universal Hepatitis Delta Virus Testing Among Veterans With Chronic Hepatitis B.

Current US guidelines recommend risk-based testing for hepatitis delta virus (HDV) in persons with chronic hepatitis B (CHB). While there is debate as to whether a risk-based or universal testing approach is most effective, limited data exist on universal HDV testing programs in the United States. We performed a 1-year pilot study evaluating the outcomes of a universal HDV testing approach among US veterans with CHB. All consecutive adults with CHB receiving care at hepatology clinics at a single-centre Veterans Affairs Health System from 1 October 2022 to 30 September 2023 were prospectively tested for anti-HDV antibody (anti-HDV). Patients who were anti-HDV Ab-positive were subsequently tested for HDV RNA. Comparison of HDV testing between groups utilised chi-square testing. A total of 91 consecutive CHB patients (90.0% male, mean age 60.9 ± 14.1 years, 73.9% Asian, 26.1% non-Asia, 16.5% cirrhosis and 17.1% with active or past history of drug use) had anti-HDV ordered. Overall, 76.9% (n = 70) completed anti-HDV testing; 4.3% (n = 3) were positive. HDV RNA testing was ordered in all three patients; two patients completed HDV RNA testing and one had detectable HDV RNA. No significant differences in completion of anti-HDV testing was observed by age, sex, race/ethnicity, cirrhosis status or drug use history. Among a single-centre prospective cohort study piloting a universal HDV testing approach, one patient with viremic HDV was identified. Implementing true reflex testing of all CHB patients with anti-HDV, followed by automated HDV RNA testing for anti-HDV-positive patients would improve the HDV testing cascade and timely diagnosis of HDV.

Association of Baseline Hepatitis B Virus DNA and On-Treatment Risk of Cirrhosis and Hepatocellular Carcinoma.

Background: Recent studies suggest an inverse relationship between baseline levels of hepatitis B virus (HBV) DNA and on-treatment risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). However, data are limited to Asian cohorts, and it is unclear if similar associations hold true for non-Asians with CHB. We aimed to evaluate association of baseline HBV DNA with long-term risks of cirrhosis and HCC among a predominantly non-Asian cohort of CHB patients in the USA. Methods: Using longitudinal data from the national Veterans Affairs database, we evaluated the risk of cirrhosis or HCC among adults with non-cirrhotic CHB who are on continuous antiviral therapy, stratified by moderate levels of baseline HBV DNA (4.00 - 6.99 log10 IU/mL) vs. high levels of baseline HBV DNA (7.00 log10 IU/mL or higher). Propensity score weighting was applied, and competing risks cumulative incidence functions and Cox proportional hazards models were utilized. Results: Among 1,129 non-cirrhotic CHB patients (41% non-Hispanic White, 36% African American, mean age 57.0 years, 62.2% hepatitis B e antigen (HBeAg) positive), 585 had moderate levels of baseline HBV DNA and 544 had high HBV DNA. After propensity score weighting, no significant difference in risk of cirrhosis was observed between moderate vs. high baseline HBV DNA (4.55 vs. 5.22 per 100 person-years, hazard ratio (HR): 0.87, 95% confidence interval (CI): 0.69 - 1.09, P = 0.22), but risk of HCC was significantly higher in patients with moderate vs. high baseline HBV DNA (0.84 vs. 0.69 per 100 person-years, HR: 1.33, 95% CI: 1.09 - 1.62, P < 0.01). Conclusions: Among a national cohort of predominantly non-Asian US veterans with non-cirrhotic CHB on antiviral therapy, moderate levels of baseline HBV DNA was associated with higher risk of HCC than high HBV DNA.

Identification of hepatic steatosis among persons with and without HIV using natural language processing.

BACKGROUND: Steatotic liver disease (SLD) is a growing phenomenon, and our understanding of its determinants has been limited by our ability to identify it clinically. Natural language processing (NLP) can potentially identify hepatic steatosis systematically within large clinical repositories of imaging reports. We validated the performance of an NLP algorithm for the identification of SLD in clinical imaging reports and applied this tool to a large population of people with and without HIV. METHODS: Patients were included in the analysis if they enrolled in the Veterans Aging Cohort Study between 2001 and 2017, had an imaging report inclusive of the liver, and had ≥2 years of observation before the imaging study. SLD was considered present when reports contained the terms "fatty," "steatosis," "steatotic," or "steatohepatitis." The performance of the SLD NLP algorithm was compared to a clinical review of 800 reports. We then applied the NLP algorithm to the first eligible imaging study and compared patient characteristics by SLD and HIV status. RESULTS: NLP achieved 100% sensitivity and 88.5% positive predictive value for the identification of SLD. When applied to 26,706 eligible Veterans Aging Cohort Study patient imaging reports, SLD was identified in 72.2% and did not significantly differ by HIV status. SLD was associated with a higher prevalence of metabolic comorbidities, alcohol use disorder, and hepatitis B and C, but not HIV infection. CONCLUSIONS: While limited to those undergoing radiologic study, the NLP algorithm accurately identified SLD in people with and without HIV and offers a valuable tool to evaluate the determinants and consequences of hepatic steatosis.

Severe Acute Liver Injury After Hepatotoxic Medication Initiation in Real-World Data.

Importance: Current approaches to classify the hepatotoxic potential of medications are based on cumulative case reports of acute liver injury (ALI), which do not consider the size of the exposed population. There is little evidence from real-world data (data relating to patient health status and/or the delivery of health care routinely collected from sources outside of a research setting) on incidence rates of severe ALI after initiation of medications, accounting for duration of exposure. Objective: To identify the most potentially hepatotoxic medications based on real-world incidence rates of severe ALI and to examine how these rates compare with categorization based on case reports. Design, Setting, and Participants: This series of cohort studies obtained data from the US Department of Veterans Affairs on persons without preexisting liver or biliary disease who initiated a suspected hepatotoxic medication in the outpatient setting between October 1, 2000, and September 30, 2021. Data were analyzed from June 2020 to November 2023. Exposures: Outpatient initiation of any one of 194 medications with 4 or more published reports of hepatotoxicity. Main Outcomes and Measures: Hospitalization for severe ALI, defined by either inpatient: (1) alanine aminotransferase level greater than 120 U/L plus total bilirubin level greater than 2.0 mg/dL or (2) international normalized ratio of 1.5 or higher plus total bilirubin level greater than 2.0 mg/dL recorded within the first 2 days of admission. Acute or chronic liver or biliary disease diagnosis recorded during follow-up or as a discharge diagnosis of a hospitalization for severe ALI resulted in censoring. This study calculated age- and sex-adjusted incidence rates of severe ALI and compared observed rates with hepatotoxicity categories based on cumulative published case reports. Results: The study included 7 899 888 patients across 194 medication cohorts (mean [SD] age, 64.4 [16.4] years, 7 305 558 males [92.5%], 4 354 136 individuals [55.1%] had polypharmacy). Incidence rates of severe ALI ranged from 0 events per 10 000 person-years (candesartan, minocycline) to 86.4 events per 10 000 person-years (stavudine). Seven medications (stavudine, erlotinib, lenalidomide or thalidomide, chlorpromazine, metronidazole, prochlorperazine, and isoniazid) exhibited rates of 10.0 or more events per 10 000 person-years, and 10 (moxifloxacin, azathioprine, levofloxacin, clarithromycin, ketoconazole, fluconazole, captopril, amoxicillin-clavulanate, sulfamethoxazole-trimethoprim, and ciprofloxacin) had rates between 5.0 and 9.9 events per 10 000 person-years. Of these 17 medications with the highest observed rates of severe ALI, 11 (64%) were not included in the highest hepatotoxicity category when based on case reports. Conclusions and Relevance: In this study, incidence rates of severe ALI using real-world data identified the most potentially hepatotoxic medications and can serve as a tool to investigate hepatotoxicity safety signals obtained from case reports. Case report counts did not accurately reflect the observed rates of severe ALI after medication initiation.

Advances in discovery of novel investigational agents for functional cure of chronic hepatitis B: A comprehensive review of phases II and III therapeutic agents.

Chronic hepatitis B virus (HBV) infection affects over 295 million people globally and an estimated 1.6 million people in the United States. It is associated with significant morbidity and mortality due to cirrhosis, liver failure, and liver cancer. Antiviral therapy with oral nucleos(t)ide analogues is associated with high rates of virologic suppression, which in turn has been associated with a decreased risk of liver complications. However, current antiviral regimens are limited by concerns with adverse effects, adherence, resistance, long-term treatment, and ongoing risk for liver events. Novel investigational agents are currently in development and are targeted at achieving functional cure with sustained hepatitis B surface antigen (HBsAg) loss and suppression of HBV DNA. Herein we review key evidence from phases II and III trials defining the efficacy and safety profiles for key investigational agents for functional cure of chronic hepatitis B, including core/capsid inhibitors, entry inhibitors, RNA interference (siRNA/ASO), HBsAg inhibitors, Toll-like receptor agonists, checkpoint inhibitors, and therapeutic vaccines.

An explainable machine learning model for prediction of high-risk nonalcoholic steatohepatitis.

Early identification of high-risk metabolic dysfunction-associated steatohepatitis (MASH) can offer patients access to novel therapeutic options and potentially decrease the risk of progression to cirrhosis. This study aimed to develop an explainable machine learning model for high-risk MASH prediction and compare its performance with well-established biomarkers. Data were derived from the National Health and Nutrition Examination Surveys (NHANES) 2017-March 2020, which included a total of 5281 adults with valid elastography measurements. We used a FAST score ≥ 0.35, calculated using liver stiffness measurement and controlled attenuation parameter values and aspartate aminotransferase levels, to identify individuals with high-risk MASH. We developed an ensemble-based machine learning XGBoost model to detect high-risk MASH and explored the model's interpretability using an explainable artificial intelligence SHAP method. The prevalence of high-risk MASH was 6.9%. Our XGBoost model achieved a high level of sensitivity (0.82), specificity (0.91), accuracy (0.90), and AUC (0.95) for identifying high-risk MASH. Our model demonstrated a superior ability to predict high-risk MASH vs. FIB-4, APRI, BARD, and MASLD fibrosis scores (AUC of 0.95 vs. 0.50, 0.50, 0.49 and 0.50, respectively). To explain the high performance of our model, we found that the top 5 predictors of high-risk MASH were ALT, GGT, platelet count, waist circumference, and age. We used an explainable ML approach to develop a clinically applicable model that outperforms commonly used clinical risk indices and could increase the identification of high-risk MASH patients in resource-limited settings.