Del Cioppo S, Faccioli J, Ridola L. Hepatic cirrhosis and decompensation: Key indicators for predicting mortality risk. World J Hepatol 2025; 17(3): 104580 [DOI: 10.4254/wjh.v17.i3.104580]
Corresponding Author of This Article
Lorenzo Ridola, PhD, Associate Professor, Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, Viale dell'Università 37, Rome 00185, Italy. lorenzo.ridola@uniroma1.it
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Hepatol. Mar 27, 2025; 17(3): 104580 Published online Mar 27, 2025. doi: 10.4254/wjh.v17.i3.104580
Hepatic cirrhosis and decompensation: Key indicators for predicting mortality risk
Sara Del Cioppo, Jessica Faccioli, Lorenzo Ridola
Sara Del Cioppo, Lorenzo Ridola, Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome 00185, Italy
Jessica Faccioli, Department of Translational and Precision Medicine, Sapienza University of Rome, Rome 00185, Italy
Co-first authors: Sara Del Cioppo and Jessica Faccioli.
Author contributions: Del Cioppo S and Faccioli J were responsible for conceptualization and manuscript writing; Ridola L was responsible for conceptualization, manuscript writing, and key revisions of important knowledge content; all of the authors read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Lorenzo Ridola, PhD, Associate Professor, Department of Medical and Surgical Sciences and Biotechnologies, Sapienza University of Rome, Viale dell'Università 37, Rome 00185, Italy. lorenzo.ridola@uniroma1.it
Received: December 25, 2024 Revised: February 28, 2025 Accepted: March 10, 2025 Published online: March 27, 2025 Processing time: 91 Days and 15.1 Hours
Core Tip
Core Tip: Identifying prognostic factors is crucial for improving risk prediction and guide clinical management in cirrhotic patients. While traditional models like model for end-stage liver disease and Child-Turcotte-Pugh are useful and provide important prognostic information, incorporating variables such as nutrition assessment, sarcopenia and muscle function, may offer a more comprehensive understanding of disease progression. This approach facilitates early detection of high-risk patients and enable timely interventions to avoid decompensation. So, considering additional prognostic factors can help clinicians to improve both outcomes and quality of life of cirrhotic patients. Furthermore, today, artificial intelligence can enhance the assessment of prognostic factors by analyzing complex data patterns.
