Background aims: Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence (AI) 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 AH patients per NIAAA criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day post-admission mortality, three AI algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) informed an ensemble model, subsequently refined via Bayesian updating, integrating the derivation cohort's average 90-day mortality with each center's approximate mortality rate to produce post-test probabilities. The ALCoholic Hepatitis Artificial INtelligence (ALCHAIN) Ensemble score integrated age, gender, cirrhosis, and 9 laboratory values, with center-specific mortality rates. Mortality was 18.7% (30-day) and 27.9% (90-day) 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, MELD variations, ABIC, Glasgow, and modified Glasgow Scores (p<0.001). ALCHAIN Ensemble score also showcased superior calibration against MELD and its variants. Steroid use improved 30-day survival for those with an ALCHAIN Ensemble score>0.20 in both derivation and validation cohorts.
Conclusions: Harnessing AI 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/.
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