Correspondence to editorial on “Human cytomegalovirus reactivation in cirrhosis patients with acute decompensation” Changze Hong, Jinjun Chen Clinical and Molecular Hepatology.2026; 32(3): e336. CrossRef
Background/Aims Prediction of short-term mortality in patients with acute-on-chronic liver failure (ACLF) admitted to the intensive care unit (ICU) may enhance effective management.
Methods To 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.
Results Of 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.
Conclusions We presented explainable, well-validated, and calibrated predictive models for ACLF patients with two or more OFs, which outperformed existing predictive scores.
Citations
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Unbiased clustering of acute-on-chronic liver failure patients using machine learning in a real-world ICU cohort Mengyi Zhang, Fanpu Ji, Jian Zu, Yingli He, Tao Chen, Yi Liu, Hirsh D. Trivedi, Ju Dong Yang, Vinay Sundaram, Yuan Wang, Xiaodan Sun, Zhujun Cao, Chun-Ying Wu, Yee Hui Yeo, Rajiv Jalan Nature Communications.2026;[Epub] CrossRef
Systematic evaluation of machine learning models for clinical risk prediction on real-world hospital datasets Qingwen Wu, Ziyou Qi, Qingwei Li, Cuiping Hao, Sujuan Tang iScience.2026; 29(5): 115654. CrossRef
Navigating the therapeutic tightrope: Precision use of plasmapheresis and continuous renal replacement therapy in liver failure Manish Manrai, Aditya V Pachisia, Saurabh Dawra, Siddharth Shukla, Atul A Jha World Journal of Hepatology.2026;[Epub] CrossRef
Beyond the Tipping Point: Advances in the Diagnosis and Management of Acute-on-Chronic Liver Failure and End-Stage Liver Disease Jonathan Soldera Diagnostics.2026; 16(10): 1548. CrossRef
Gut–Liver Axis Failure in Critical Alcohol‐Associated Liver Disease: From ICU Secondary Hits to Microbiome‐Targeted Therapy Yuting Zhang, Yiyu Wang, Yong Yang, Hong Mei, Xinxin Liu, Yuanxiu He, Song Qin, Banghai Feng, Ranjitsinh V. Devkar Mediators of Inflammation.2026;[Epub] CrossRef
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 Yijun Zhang, Chunyan Li, Shaohui Su, Jilin Huang, Siyu Fu, Yong Zhang, Shanhong Tang Frontiers in Medicine.2026;[Epub] CrossRef
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 fail Sagnik Biswas, Akash Roy Clinical and Molecular Hepatology.2026; 32(3): 1386. CrossRef
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” Jung Hee Kim Clinical and Molecular Hepatology.2026; 32(3): 1383. CrossRef
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” Mengyi Zhang, Yee Hui Yeo, Jian Zu, Jonel Trebicka, Fanpu Ji Clinical and Molecular Hepatology.2026; 32(3): e346. CrossRef
Metabolomics and Machine Learning Uncover 5′-Methylthioadenosine as a Metabolic Indicator of Mixed-Pattern Drug-Induced Liver Injury Jinghui Zhang, Hongchuan Liu, Qingrong Qiu, Kongcai Zhu, Xian Ding, Rui Zhao, Ting Hu, Zhuoling An Metabolites.2026; 16(9): 614. CrossRef
Dynamic prediction of the development of extrahepatic organ failures in ICU patients with decompensated cirrhosis Yee Hui Yeo, Linjie Que, Mengyi Zhang, Jian Zu, Yuankai Wu, Dian-Jung Chiang, Tao Chen, Yu Shi, Yingli He, Fengping Wu, Feng Han, Qing-Lei Zeng, Yu-Chen Fan, Hirsh D. Trivedi, Xiaogang Xiang, Yadong Wang, Yu Chen, Ju Dong Yang, Jun Li, Rajiv Jalan, Fanpu eGastroenterology.2026; 4(3): e100463. CrossRef
Backgrounds/Aims Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model.
Methods Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort.
Results In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM).
Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.
Citations
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Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension Yan-Qiu Li, Zhuo-Jun Li, Yong-Qi Li, Ying Feng, Xian-Bo Wang World Journal of Gastroenterology.2026;[Epub] CrossRef
Carvedilol Versus Endoscopic Variceal Ligation for Prophylaxis of Esophageal Variceal Bleeding in Patients With Liver Cirrhosis Luiz G.S. Almeida, Ocílio R. Gonçalves, Thiago H.F. de Oliveira, João V. Andrade Fernandes, Hildel F.L. Filho, Maria J.G. Siqueira, Paweł Łajczak, Wagner Rios-Garcia, Jefferson H. Marques Fontes, Lucas L. Mendes, Marcos de Vasconcelos Carneiro Journal of Clinical Gastroenterology.2026; 60(7): 569. CrossRef
Endoscopic variceal ligation combined with carvedilol versus endoscopic variceal ligation combined with propranolol for the treatment of oesophageal variceal bleeding in cirrhosis: study protocol for a multicentre, randomised controlled trial Yiling Li, Li Du, Shuairan Zhang, Chuan Liu, Chao Ma, Xiaochao Liu, Huanhai Xu, Zhixu Fan, Shengjuan Hu, Jing Wang, Lichun Shao, Lijun Peng, Huiling Xiang, Xuan Liang, Wenhui Zhang, Hongyun Zhao, Pengyuan He, Jingyi Xu, Qianlong Li, Ling Yang, Yunhai Wu, BMJ Open.2025; 15(4): e093866. CrossRef
Relative change rate of liver stiffness measurements predicts the risk of liver decompensation in compensated advanced chronic liver disease Yanqiu Li, Zihang Qiao, Jinze Li, Bingbing Zhu, Yu Lu, Ying Feng, Xianbo Wang Clinical and Experimental Medicine.2025;[Epub] CrossRef
Revolutionising portal hypertension diagnosis: the rise of non-invasive techniques in liver cirrhosis Bocheng Gao, Yumeng Lin, Huimin Zhang, Yulin Li, Shuhua Gou, Peiling Ma, Xueni Zhao, Yue Zhou, Qian Chen, Lan Yuan, Zhongyu Han, Chang Yu Frontiers in Medicine.2025;[Epub] CrossRef
Editorial: Non‐selective beta‐blockers: A lifesaving shield for critically ill patients with acute decompensation of cirrhosis? Ling Yang, Chuan Liu, Jimmy Che‐To Lai, Xiaolong Qi Alimentary Pharmacology & Therapeutics.2024; 60(7): 965. CrossRef
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