ArticleClinical and molecular hepatology2025
Predictive machine learning model in intensive care unit patients with acute-on-chronic liver failure and two or more organ failures.
Article in Clinical and molecular hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Metabolomics and Machine Learning Uncover 5'-Methylthioadenosine as a Metabolic Indicator of Mixed-Pattern Drug-Induced Liver Injury.Metabolites · 2026Article
- Correspondence to editorial on "Predictive machine learning model in intensive care unit patients with acute-on-chronic liver failure and two or more organ failures".Clinical and molecular hepatology · 2026Article
- Rethinking ACLF prognosis: Can machine learning outperform conventional scores?: Editorial on "Predictive machine learning model in intensive care unit patients with acute-on-chronic liver failure and two or more organ failures".Clinical and molecular hepatology · 2026Article
- Harmonized ACLF prognostication with explainable machine learning models: Traversing from counts to composition: Editorial on "Predictive machine learning model in intensive care unit patients with acute-on-chronic liver failure and two or more organ failures".Clinical and molecular hepatology · 2026Article
- Beyond the Tipping Point: Advances in the Diagnosis and Management of Acute-on-Chronic Liver Failure and End-Stage Liver Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasets.iScience · 2026Article
- [Research progress in the field of liver failure with artificial livers in 2025].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
- Unbiased clustering of acute-on-chronic liver failure patients using machine learning in a real-world ICU cohort.Nature communications · 2026Article
- Dynamic prediction of the development of extrahepatic organ failures in ICU patients with decompensated cirrhosis.eGastroenterology · 2026Article
- Gut-Liver Axis Failure in Critical Alcohol-Associated Liver Disease: From ICU Secondary Hits to Microbiome-Targeted Therapy.Mediators of inflammation · 2026Review
- A machine learning model for 90-day mortality prediction in hepatitis B virus-related acute-on-chronic liver failure: the pivotal role of CALLY index.Frontiers in medicine · 2026Article
Corrections and comments
- Commented on by
- Commented on by
Authors and funding
17 authors.
Funding
Abstract
BACKGROUND/
aimsPrediction of short-term mortality in patients with acute-on-chronic liver failure (ACLF) admitted to the intensive care unit (ICU) may enhance effective management.
methodsTo develop, explain, and validate a predictive machine learning (ML) model for short-term mortality in patients with ACLF with two or more organ failures (OFs). Utilizing a large ICU cohort with detailed clinical information, we identified ACLF patients with two or more OFs according to the EASL-CLIF and NACSELD definitions. ML model was developed for each definition to predict 30-day mortality. The Shapley value was estimated to explain the models. Validation and calibration of these models were performed.
resultsOf 5,994 patients with cirrhosis admitted to ICU, 1,511 met NACSELD criteria, and 1,692 met EASL-CLIF grade II or higher criteria. The CatBoost ACLF (CBA) model had the greatest accuracy in the NACSELD cohort (area under curve [AUC] of 0.87), while the Random Forest ACLF (RFA) model performed best in the EASL-CLIF cohort (AUC of 0.83). Both models showed robust calibration. The models were explained by SHAP score analysis, yielding a rank list, and the top twelve predictors were selected. Both simplified models demonstrated similar performance (CBA model: AUC 0.89, RFA model: AUC 0.81) and significantly outperformed contemporary scoring systems, including CLIF-C ACLF and MELD 3.0. The models were validated in both internal and external cohorts. A simple-to-use online tool was created to predict mortality rates.
conclusionWe presented explainable, well-validated, and calibrated predictive models for ACLF patients with two or more OFs, which outperformed existing predictive scores.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.