Ahn, Joseph

Background: Hepatocellular carcinoma (HCC) has a poor long-term prognosis due to high recurrence and cirrhosis-related mortality, even after potentially curative treatments such as liver transplantation (LT), surgical resection, or ablation. This study aimed to identify factors associated with ≥10-year survival in HCC patients. Methods: A retrospective cohort study was conducted among HCC patients diagnosed between 2004 and 2022 using the National Cancer Database. Multivariable Cox regression was used to identify predictors of overall survival, and logistic regression was used to identify predictors of ≥10-year survival. Results: Among 249,600 HCC patients, 177,585 (71.2%) died within 5 years, 8613 (3.5%) died at 5–10 years, 54,988 (22.0%) were alive with <10 years of follow-up, and 8219 (3.3%) survived ≥10 years. LT, resection, and ablation were performed in 6.6%, 9.3%, and 11% of patients, respectively. Compared with ablation as the reference group, LT [adjusted odds ratio (aOR) 11.96, 95% confidence interval (CI): 11.27–13.29] and resection (aOR: 2.83, 95% CI: 2.57–3.08) increased the odds of ≥10-year survival, while non-curative treatments reduced the odds compared with ablation (aOR: 0.50, 95% CI: 0.47–0.55). Cox regression results were consistent with the logistic model, confirming the association. Decision tree analysis confirmed LT as the dominant determinant of long-term survival. Black individuals were associated with lower odds of ≥10-year survival (aOR: 0.88, 95% CI: 0.820–0.96) and decreased likelihood of receiving LT (aOR: 0.73, 95% CI: 0.55–0.96). Conclusions: LT offers the best chance of ≥10-year survival in HCC. Ensuring equitable access is essential, especially for Black patients who have lower transplant rates and worse outcomes.

INTRODUCTION: Chronic gastrointestinal (GI) conditions, including irritable bowel syndrome and other disorders of gut-brain interaction, are prevalent and costly, and fragmented care contributes to suboptimal outcomes. Virtual and multidisciplinary care models may improve outcomes. Objectives: We aimed to (i) characterize patients receiving virtual GI care, (ii) report patient engagement with and patient-reported outcomes after virtual multidisciplinary care, and (iii) report healthcare utilization and costs from a payer perspective among patients vs propensity score-matched controls identified from claims data. METHODS: We collected descriptive data on patient demographics, clinic provider visits, symptoms, and patient-reported outcomes from 3/2021 to 9/2022. To evaluate healthcare utilization and costs, we conducted difference-in-difference analyses using claims data compared with a propensity score-matched control group. RESULTS: Among 234 patients (71% women, mean age 45.4 ± 13.2 years), 51% received a new GI diagnosis, predominately disorders of gut-brain interaction (63%). Engagement criteria were met by 80% (n = 187), who completed a mean of 10 visits and median time-to-first appointment of 6 days. Dietitians (93%), psychologists (76%), and health coaches (64%) were frequently used. Engaged patients exhibited significant improvements from preclinic to postclinic in irritable bowel syndrome severity (226.7–145.6), symptom control (20.2%–86.6%), quality-of-life dysphoria (27.5–32.8), missed workdays (1.0–0.7), and satisfaction (37.4%–96.3%) (all P < 0.05). Compared with matched controls, a significantly smaller percent of patients had imaging (15% vs 44%; P < 0.0001) and GI-related emergency department visits (4% vs 10%; P = 0.0028), which contributed to reductions in GI-related costs ($443 per member per month; P = 0.047) and all-cause costs ($676 per member per month; P = 0.043), equating to annualized savings of $5,316 and $8,112, respectively. DISCUSSION: Virtual multidisciplinary GI care was associated with high engagement, improved clinical outcomes, and reduced healthcare utilization and costs.

INTRODUCTION: The American College of Gastroenterology assembled a multidisciplinary task force to evaluate the current state and future direction of artificial intelligence (AI) in gastroenterology, hepatology, and endoscopy leading to the development of consensus-based recommendations for responsible AI integration in clinical practice. METHODS: A total of 32 subject-matter experts and 12 industry partners, representing diverse practice settings and expertise, conducted subgroup literature reviews across 5 key areas (endoscopy, practice management clinical applications, training and education, inflammatory bowel disease and liver disease, ethics and equity). Draft statements were developed and rated on a 5-point Likert scale using a modified Delphi process. A consensus was set at ≥70% combined agreement. Nonconsensus items were revised and revoted electronically. RESULTS: A total of 43 statements, 40 (93%) reached consensus in round 1 and the remaining 3 achieved consensus after round 2. Evidence supports computer-aided detection improving adenoma detection rate and miss rate in controlled studies, with mixed real-world impact and insufficient long-term outcomes (e.g., interval colon cancer rate). Recommendations emphasize thorough validation and reduction of bias by heterogeneous Data sets. Outside endoscopy, ambient AI scribes, natural language processing (NLP)-enabled coding, workflow optimization, and previous authorization support show potential. Training recommendations endorse a structured AI curriculum while preserving independent procedural competence to avoid deskilling. In inflammatory bowel disease and hepatology, AI could help improve diagnostic accuracy, help predict risk of disease progression, and help guide therapy. Equity, governance, and reimbursement statements call for chain-of-custody data protections, specialty-society oversight, and payment models that reward quality and cost reduction. DISCUSSION: This consensus outlines how AI can augment rather than replace clinical expertise while promoting safety, transparency, interoperability, and equity. Priorities include pragmatic and prospective trials, multi-institutional data-sharing consortia, bias mitigation, and workforce training to enable trustworthy and clinically impactful AI adoption in gastroenterology, liver, and endoscopy care.

Background Antibiotic use may alter gut microbiota, which could be associated with various diseases, including cardiovascular disease (CVD). Accordingly, further epidemiological evidence is needed to clarify the potential risk of CVD associated with antibiotic exposure. We evaluated the association between long‐term antibiotic exposure and CVD risk in South Korea. Methods and Results This nationwide retrospective cohort study used data from the Korean National Health Insurance Service database. A total of 2 159 965 individuals aged 40 to 79 years who had no history of antibiotic prescriptions from 2004 to 2005 were included and followed through December 31, 2021. Antibiotic exposure was defined as the cumulative days of antibiotic prescriptions from 2006 to 2010. The primary end point was incident CVD with 2 or more days of hospitalization from 2011 to 2021. Adjusted hazard ratios (aHR) and 95% CIs for CVD according to antibiotic exposure were estimated using multivariable Cox proportional hazards models, adjusting for a range of covariates. Longer cumulative antibiotic prescription days were associated with an increased risk of CVD. The aHRs for CVD associated with 1 to 14, 15 to 90, 91 to 180, 181 to 364, and ≥365 days of antibiotic exposure were 0.98 (95% CI, 0.96–1.01), 1.01 (95% CI, 0.99–1.03), 1.05 (95% CI, 1.03–1.08), 1.06 (95% CI, 1.04–1.09), and 1.10 (95% CI, 1.07–1.13) compared with those with no antibiotic prescriptions. Conclusions In this large population‐based cohort study of Korean adults, long‐term antibiotic exposure may be associated with a higher risk of CVD. Further research is warranted to establish causality and elucidate the underlying mechanisms for this association.

Background and Aims: Immunotherapy has emerged as an effective treatment for advanced HCC. We aimed to investigate the real-world effectiveness of immunotherapy compared to lenvatinib in HCC. Approach and Results: From the TriNetX database, we used a target trial emulation framework and identified patients with HCC who received first-line treatment with immunotherapy (atezolizumab/bevacizumab or tremelimumab/durvalumab) or lenvatinib between or between August 2018 and December 2023. OS was compared using Kaplan-Meier analysis and Cox proportional hazards regression. After propensity score matching, 1203 patients were included in each group. Immunotherapy was associated with improved OS versus lenvatinib (median survival: 545 vs. 425 d; HR: 0.86, 95% CI: 0.76–0.97). Regarding treatment type, atezolizumab plus bevacizumab showed improved survival compared to lenvatinib (n=1070 in each group; HR: 0.87, 95% CI: 0.77–0.99), while the point estimate favored durvalumab plus tremelimumab versus lenvatinib (HR: 0.81, 95% CI: 0.59–1.12), though this difference was not statistically significant, likely due to small sample size. Regarding etiology, immunotherapy had improved OS compared to lenvatinib in viral hepatitis (n=510 in each group; HR: 0.74, 95% CI: 0.61–0.89) and alcohol-associated liver disease (n=190 in each group; HR: 0.65, 95% CI: 0.49–0.87), but not in metabolic dysfunction–associated steatotic liver diseases (n=156 in each group; HR: 0.96, 95% CI: 0.70–1.31). Conclusions: In this real-world analysis, immunotherapy was associated with improved OS compared to lenvatinib in advanced HCC, with consistent benefit across most subgroups. These findings support the use of immunotherapy as a first-line treatment for advanced HCC.

INTRODUCTION: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD). METHODS: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier. RESULTS: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850–0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16–13.63). DISCUSSION: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.

Background and Aims: Clinical hepatology research often faces limited data availability, underrepresentation of minority groups, and complex data-sharing regulations. Synthetic data—artificially generated patient records designed to mirror real-world distributions—offers a potential solution. We hypothesized that diffusion models, a state-of-the-art generative technique, could produce synthetic liver transplant waitlist data from the United Network for Organ Sharing database that maintains statistical fidelity, replicates clinical correlations and survival patterns, and ensures robust privacy protection. Approach and Results: Diffusion models were used to generate synthetic patient cohorts mirroring the United Network for Organ Sharing liver transplant waitlist database between the years 2019 and 2023. Statistical fidelity was assessed using maximum mean discrepancy (MMD) and Wasserstein distance, correlation analysis, and variable-level metrics. Clinical utility was evaluated by comparing transplant-free survival via Kaplan–Meier curves and the MELD score performance. Privacy was quantified using the Distance to Closest Record (DCR) and attribute disclosure risk assessments. The synthetic dataset was nearly indistinguishable from the original dataset (MMD=0.002, standardized Wasserstein distance <1.0), preserving clinically relevant correlations and survival patterns as evidenced by similar median survival times (110 vs. 101 days) and 5-year survival rates (22.2% vs. 22.8%). MELD-based 90-day mortality prediction was maintained (original AUC=0.839 vs. synthetic AUC=0.844). Privacy metrics indicated no identifiable patient matches, and mean DCR values ensured that synthetic individuals were not direct replicas of real patients. Conclusion: Artificial intelligence–generated synthetic data derived from diffusion models can faithfully replicate complex hepatology datasets, maintain key clinical signals, and ensure strong privacy safeguards. This approach can help address data scarcity, enhance model generalizability, foster multi-institutional collaboration, and accelerate progress in hepatology research.