Gastroenterology January 2026 Enhancement of Inpatient Mortality Prognostication With Machine Learning in a Prospective Global Cohort of Patients With Cirrhosis With External Validation

In a prospectively recruited cohort of 7239 patients hospitalized with cirrhosis (Chronic Liver Disease Evolution and Registry for Events and Decompensation [CLEARED]) from 115 centers across all 6 populated continents, it was determined that a random forest machine learning model significantly enhanced inpatient mortality prediction over other models and traditional logistic regression. This random forest model also showed consistently good performance and calibration on internal 75/25 validation, and when the prediction was performed within low-income, upper middle-income, and high-income countries within the CLEARED cohort. The same model was externally validated with good performance in 28,670 hospitalized US veterans with cirrhosis, which have a distinct demographic and cirrhosis complication profile.