Citations

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/.

Background: The natural history of primary sclerosing cholangitis (PSC) among African Americans (AA) is not well understood. Methods: Transplant-free survival and hepatic decompensation–free survival were assessed using a retrospective research registry from 16 centers throughout North America. Patients with PSC alive without liver transplantation after 2008 were included. Diagnostic delay was defined from the first abnormal liver test to the first abnormal cholangiogram/liver biopsy. Socioeconomic status was imputed by the Zip code. Results: Among 850 patients, 661 (77.8%) were non-Hispanic Whites (NHWs), and 85 (10.0%) were AA. There were no significant differences by race in age at diagnosis, sex, or PSC type. Inflammatory bowel disease was more common in NHWs (75.8% vs. 51.8% p =0.0001). The baseline (median, IQR) Amsterdam-Oxford Model score was lower in NHWs (14.3, 13.4–15.2 vs. 15.1, 14.1–15.7, p =0.002), but Mayo risk score (0.03, −0.8 to 1.1 vs. 0.02, −0.7 to 1.0, p =0.83), Model for End-stage Liver Disease (5.9, 2.8–10.7 vs. 6.4, 2.6–10.4, p =0.95), and cirrhosis (27.4% vs. 27.1%, p =0.95) did not differ. Race was not associated with hepatic decompensation, and after adjusting for clinical variables, neither race nor socioeconomic status was associated with transplant-free survival. Variables independently associated with death/liver transplant (HR, 95% CI) included age at diagnosis (1.04, 1.02–1.06, p <0.0001), total bilirubin (1.06, 1.04–1.08, p <0.0001), and albumin (0.44, 0.33–0.61, p <0.0001). AA race did not affect the performance of prognostic models. Conclusions: AA patients with PSC have a lower rate of inflammatory bowel disease but similar progression to hepatic decompensation and liver transplant/death compared to NHWs.

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.

Background: Alcohol-associated hepatitis (AH) is among the deadliest liver diseases, but its incidence is poorly defined. The aim of our study was to define the incidence of AH meeting the National Institute on Alcohol Abuse and Alcoholism criteria and to identify risk factors for AH. Methods: We conducted a retrospective cohort study using the Rochester epidemiology project database on adult patients hospitalized with AH between January 1, 2000 and December 31, 2018. Patients were screened using ICD-9 codes and then included if they met the National Institute on Alcohol Abuse and Alcoholism criteria on manual chart review. Baseline demographics, comorbidities, access to care, liver-related complications, and outcomes were obtained. The HOUsing-based index of SocioEconomic status index was used to measure socioeconomic status. Incidence rates were calculated in cases per 100,000 person-years of follow-up. Results: Among 204 patients, the cumulative AH incidence was 6.8 per 100,000 person-years. Between 2000–2004 and 2015–2018, AH incidence among males increased from 8.4 to 14.7 per 100,000 py, whereas AH incidence among females increased by 7-fold from 0.8 to 5.9 per 100,000 py. Such increases among females were accompanied by increases in comorbid depression and anxiety. The proportion of patients with AH in the lower socioeconomic status quartiles increased from 62.9% between 2000 and 2004 to 73.3% between 2015 and 2019. Conclusions: The incidence of AH is increasing rapidly, especially among females and individuals of lower socioeconomic status. There are areas of unmet need in preventative measures and treatments for comorbid psychiatric disorders in patients at high risk of AH.

(1) Background: The association between proton pump inhibitor (PPI) use and hepatocellular carcinoma (HCC) has been controversial, especially in the general population. We aimed to determine the impact of PPI on HCC risk in participants without liver cirrhosis or chronic hepatitis virus infection. (2) Methods: We assessed 406,057 participants from the Korean National Health Insurance Service database who underwent health screening from 2003 to 2006. We evaluated exposure to PPI before the index date using a standardized daily defined dose (DDD) system. The association of proton pump inhibitor use with the risk of HCC was evaluated using multivariable-adjusted Cox proportional hazards regression. (3) Results: Compared with non-users, PPI use was not associated with the HCC risk in low (<30 DDDs; aHR, 1.07; 95% CI, 0.91–1.27), intermediate (30 ≤ PPI < 60 DDDs; aHR, 0.96; 95% CI, 0.73–1.26), and high (≥60 DDDs; aHR, 0.86; 95% CI, 0.63–1.17) PPI groups in the final adjustment model. In addition, risks of cirrhosis-associated HCC and non-cirrhosis-associated HCC were not significantly associated with PPI use. The results remained consistent after excluding events that occurred within 1, 2, and 3 years to exclude pre-existing conditions that may be associated with the development of HCC. We also found no PPI-associated increase in HCC risk among the selected population, such as those with obesity, older age, and chronic liver diseases. (4) Conclusions: PPI use may not be associated with HCC risk regardless of the amount. We call for future studies conducted in other regions to generalize our findings.