Ahn, Joseph C

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.

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.

Background and Aims: Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence in a global cohort, we sought to derive and validate an enhanced prognostic model. Approach and Results: The Global AlcHep initiative, a retrospective study across 23 centers in 12 countries, enrolled patients with AH per National Institute for Alcohol Abuse and Alcoholism criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day postadmission mortality, 3 artificial intelligence algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) informed an ensemble model, subsequently refined through Bayesian updating, integrating the derivation cohort’s average 90-day mortality with each center’s approximate mortality rate to produce posttest probabilities. The ALCoholic Hepatitis Artificial INtelligence Ensemble score integrated age, gender, cirrhosis, and 9 laboratory values, with center-specific mortality rates. Mortality was 18.7% (30 d) and 27.9% (90 d) in the derivation cohort versus 21.7% and 32.5% in the validation cohort. Validation cohort 30 and 90-day AUCs were 0.811 (0.779–0.844) and 0.799 (0.769–0.830), significantly surpassing legacy models like Maddrey’s Discriminant Function, Model for End-Stage Liver Disease variations, age-serum bilirubin-international normalized ratio-serum Creatinine score, Glasgow, and modified Glasgow Scores (p < 0.001). ALCoholic Hepatitis Artificial INtelligence Ensemble score also showcased superior calibration against MELD and its variants. Steroid use improved 30-day survival for those with an ALCoholic Hepatitis Artificial INtelligence Ensemble score > 0.20 in both derivation and validation cohorts. Conclusions: Harnessing artificial intelligence within a global consortium, we pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions. Beneficial for clinical trials, steroid therapy, and transplant indications, it’s accessible at: https://aihepatology.shinyapps.io/ALCHAIN/.

Nonalcoholic fatty liver disease (NAFLD) is considered an independent risk factor for the development of cardiovascular disease. However, the association between changes in NAFLD status and the risk of cardiovascular disease (CVD) remains uncertain. Starting January 1, 2013, participants were followed until the occurrence of CVD event, death, or December 31, 2020. This was a population-based cohort study that included data from adults aged ≥ 20, who underwent 2 consecutive health screenings from 2009 to 2012. NAFLD was defined as a Fatty Liver Index ≥ 60 at each screening. The primary endpoint was a CVD event, which encompassed ischemic heart disease and cerebrovascular disease. The association between changes in NAFLD status and the risk of CVD was determined using multivariable Cox proportional hazards regression. This cohort comprised 4656,305 adults with a median age of 53 years. During 36,396,968 person-years of follow-up, 238,933 (5.1%) CVD events were observed. Compared to patients with no NAFLD at both screenings, patients who developed NAFLD at the second screening exhibited an increased risk of CVD (adjusted hazard ratio, 1.15; 95% confidence interval, 1.13–1.17). In contrast, individuals who recovered from NAFLD at the second screening demonstrated a reduced CVD risk compared to those with persistent NAFLD (adjusted hazard ratio, 0.91; 95% confidence interval, 0.90–0.92). The reversal of NAFLD is associated with a reduced risk of CVD. Therefore, focusing on NAFLD treatment could serve as a clinical target for lowering CVD risk.