ArticleGastroenterology2026
Enhancement of Inpatient Mortality Prognostication With Machine Learning in a Prospective Global Cohort of Patients With Cirrhosis With External Validation.
Article in Gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension.World journal of gastroenterology · 2026Article
- Unbiased clustering of acute-on-chronic liver failure patients using machine learning in a real-world ICU cohort.Nature communications · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Interpretable machine learning for prognostic prediction in critically ill patients with coronary artery disease: a multicenter study.Frontiers in medicine · 2026Article
- The Kidney in the Shadow of Cirrhosis: A Critical Review of Renal Failure.Biomedicines · 2025Review
- Machine learning-based hepatocellular carcinoma risk prediction model for patients with HBV-related compensated advanced chronic liver disease.Journal of cancer research and clinical oncology · 2025Article
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58 authors.
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Abstract
BACKGROUND &
aimsCirrhosis is a major global burden requiring frequent hospitalizations and has high inpatient mortality. Traditional prognostic tools focused on inpatient mortality are affected by global disparities, which impacts timely management. We aimed to deploy machine learning (ML) approaches to enhance inpatient mortality prognostication.
methodsUsing the prospective Chronic Liver Disease Evolution and Registry for Events and Decompensation (CLEARED) cohort, which enrolled inpatients with cirrhosis globally, we used admission-day data to predict inpatient mortality with ML approaches vs logistic regression. Internal validation (75/25 split) and subdivision using World Bank income status (low/low-middle-income countries, upper-middle income countries, and high-income countries) were performed. The ML model with the best area under the receiver operating characteristic curve (AUROC) was externally validated in a US veteran inpatient population with cirrhosis.
resultsThe CLEARED cohort included 7239 inpatients with cirrhosis (64% were men; mean [SD] age, 56 [13] years; median Model for End-Stage Liver Disease-Sodium score = 25) from 115 centers globally; 22.5% were from low/low-middle-income countries, 41% were from upper-middle-income countries, and 34% were from high-income countries; 11.1% of patients (n = 808) died in the hospital. Random forest (RF) showed the best AUROC (0.815) with high calibration, which was significantly better vs parametric logistic regression and LASSO models (AUROC, 0.774; P < .001 and AUROC, 0.787; P = .004, respectively). RF was the ML method with the highest AUROC and remained better than logistic regression, regardless of country income level (high-income countries: AUROC, 0.806; upper middle-income countries: AUROC, 0.867; and low/low-middle-income countries: AUROC, 0.768). External validation was performed in 28,670 veterans (96% were men, mean [SD] age, 67.8 [10.3] years, median Model for End-Stage Liver Disease-Sodium score = 15) with 4% (n = 1158) inpatient mortality. The AUROC using the CLEARED-derived RF model was 0.859.
conclusionsRF analysis trained on a global prospective cirrhosis cohort enhances mortality prediction over traditional methods, was consistent across country income levels, and was successfully validated externally in a US veteran population.
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