Buryska, Seth

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: 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.

INTRODUCTION: Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score that would correlate with disease severity. METHODS: A review of Mayo Clinic's electronic health records identified 5,212 patients with advanced cirrhosis ≥18 years who underwent liver transplantation at the 3 Mayo Clinic transplant centers between 1988 and 2019. The patients were matched by age and sex in a 1:4 ratio to controls without liver disease and then divided into training, validation, and test sets using a 70%-10%-20% split. The primary outcome was the performance of the model in distinguishing patients with cirrhosis from controls using their ECGs. In addition, the association between the ACE score and the severity of patients' liver disease was assessed. RESULTS: The model's area under the curve in the test set was 0.908 with 84.9% sensitivity and 83.2% specificity, and this performance remained consistent after additional matching for medical comorbidities. Significant elevations in the ACE scores were seen with increasing model for end-stage liver disease-sodium score. Longitudinal trends in the ACE scores before and after liver transplantation mirrored the progression and resolution of liver disease. DISCUSSION: The ACE score, a deep learning model, can accurately discriminate ECGs from patients with and without cirrhosis. This novel relationship between AI-enabled ECG analysis and cirrhosis holds promise as the basis for future low-cost tools and applications in the care of patients with liver disease.