Researchers employed a machine learning technique known as random forest analysis and found that it significantly outperformed traditional methods in predicting which hospitalized patients with cirrhosis are at risk of death, according to a paper published in Gastroenterology.

“This gives us a crystal ball—it helps hospital teams, transplant centers, GI and ICU services to triage and prioritize patients more effectively,” said Dr. Jasmohan S. Bajaj, the study’s corresponding author.

Key findings:

• Data analyzed from 121 hospitals worldwide, which were part of the CLEARED consortium.
• The model performed consistently across both high- and low-income countries.
• It was validated using National U.S. veterans’ data and remained accurate.
• The tool maintained strong performance even when limited to just 15 key variables.
• Patients were accurately grouped into high-risk and low-risk categories, making the model scalable and clinically practical.

This paper is one of three studies recently published on this topic in the American Gastroenterological Association’s journals. One was a worldwide consensus statement on organ failures, including liver in cirrhosis patients, while the second study identified specific blood markers and complications that influence the risk of in-hospital death, focusing on liver failure biomarkers.

“Liver disease is one of the most underappreciated causes of death worldwide—alcohol, viral hepatitis, and late diagnoses are major drivers,” Bajaj said. “When someone is hospitalized, it’s often because everything upstream—prevention, screening, primary care—has already failed.”