Letter to the Editor
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
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 22.8 Hours
Abstract

A study published by Teerasarntipan et al in the World Journal of Gastroenterology provides valuable insights into prognostic scoring for acute liver failure and in-hospital mortality in patients with dengue-induced severe hepatitis. Their findings validate the model for end-stage liver disease score as the most reliable predictor while demonstrating the utility of the simpler Easy Albumin-Bilirubin score. Despite these findings, current prognostic models face limitations in real-world clinical applications. This letter discusses the strengths and weaknesses of current prognostic models, proposes future directions for improving prognostic accuracy and clinical implementations. This letter also broadens the horizons of prognostic models for liver dysfunction caused by other viral infections.

Keywords: Dengue; Hepatitis; Prognosis; Models; Artificial intelligence

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.