Wang C, Hu H, Song Y, Wang YG, Shi M. Future directions in prognostic modeling for dengue-induced severe hepatitis. World J Hepatol 2025; 17(6): 107299 [DOI: 10.4254/wjh.v17.i6.107299]
Corresponding Author of This Article
Min Shi, Professor, Department of Gastroenterology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai 200336, China. sm1790@shtrhospital.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Letter to the Editor
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. Jun 27, 2025; 17(6): 107299 Published online Jun 27, 2025. doi: 10.4254/wjh.v17.i6.107299
Future directions in prognostic modeling for dengue-induced severe hepatitis
Chen Wang, Hong Hu, Yun Song, Yu-Gang Wang, Min Shi
Chen Wang, Yu-Gang Wang, Min Shi, Department of Gastroenterology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China
Hong Hu, Yun Song, Department of Gastroenterology, Shanghai Tongren Hospital, Shanghai 200000, China
Co-first authors: Chen Wang and Hong Hu.
Co-corresponding authors: Min Shi and Yu-Gang Wang.
Author contributions: Wang C and Hu H designed this letter; Wang C, Hu H, and Song Y wrote this comment; Wang YG and Shi M reviewed and supervised this manuscript; all authors approved the final version of the article.
Supported by The Natural Science Foundation of the Science and Technology Commission of Shanghai Municipality, No. 23ZR1458300; Key Discipline Project of Shanghai Municipal Health System, No. 2024ZDXK0004; Doctoral Innovation Talent Base Project for Diagnosis and Treatment of Chronic Liver Diseases, No. RCJD2021B02; and Pujiang Project of Shanghai Magnolia Talent Plan, No. 24PJD098.
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: Min Shi, Professor, Department of Gastroenterology, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Changning District, Shanghai 200336, China. sm1790@shtrhospital.com
Received: March 24, 2025 Revised: April 6, 2025 Accepted: April 23, 2025 Published online: June 27, 2025 Processing time: 97 Days and 22.7 Hours
Core Tip
Core Tip: A study by Teerasarntipan et al highlights the utility of the model for end-stage liver disease score as the most accurate predictor of in-hospital mortality in dengue-induced severe hepatitis and validates the Easy Albumin-Bilirubin score as a simpler alternative for resource-limited settings. However, current prognostic models face limitations, including static assessments, reliance on non-specific biomarkers, and applicability constraints in diverse healthcare settings. Future directions emphasize the development of dengue-specific scores incorporating novel biomarkers (e.g., tumor necrosis factor-alpha and interleukin-6), leveraging artificial intelligence (AI) for dynamic risk assessment, and multicenter validation to enhance generalizability. Additionally, insights from this research can inform prognostic models for liver dysfunction caused by other viral infections, such as hepatitis viruses and severe acute respiratory syndrome coronavirus 2. Key strategies include integrating AI-driven models into electronic health records, refining dynamic risk stratification, and standardizing tools across healthcare infrastructures. Addressing these challenges will improve early risk stratification, clinical decision-making, and patient outcomes in viral-induced liver failure.
