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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Apr 27, 2025; 17(4): 103330
Published online Apr 27, 2025. doi: 10.4254/wjh.v17.i4.103330
Published online Apr 27, 2025. doi: 10.4254/wjh.v17.i4.103330
Multivariable prognostic models for post-hepatectomy liver failure: An updated systematic review
Xiao Wang, Ying-Dong Du, Department of Hepatobiliary Surgery, Chinese PLA 970th Hospital, Yantai 264001, Shandong Province, China
Xiao Wang, Ming-Xiang Zhu, Pan Liu, You Zhou, Xi-Xiang Lin, Kun-Lun He, Medical Big Data Research Center, Chinese PLA General Hospital, Beijing 100853, China
Ming-Xiang Zhu, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing 100853, China
Jun-Feng Wang, Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht 358 4CG, Netherlands
Li-Yuan Zhang, China National Clinical Research Center for Neurological Diseases, Beijing 100853, China
You Zhou, School of Medicine, Nankai University, Tianjin 300071, China
Co-first authors: Xiao Wang and Ming-Xiang Zhu.
Co-corresponding authors: Ying-Dong Du and Kun-Lun He.
Author contributions: Wang X, Zhu MX, Wang JF, Du YD, and He KL conceptualized and designed the research; Wang X and Wang JF developed the literature search strategy; Wang X and Zhu MX completed the literature screening and data extraction; Wang X, Zhu MX, Liu P, Zhang LY, Zhou Y, and Lin XX organized and counted the extracted data; Wang X and Zhu MX completed the quality assessment and performed the statistical analysis; Wang X, Zhu MX, Du YD and He KL wrote the paper; All the authors have read and approved the final manuscript. Wang X proposed, designed and conducted literature screening, completed the quality assessment, performed data analysis, and prepared the first draft of the manuscript. Zhu MX was also mainly responsible for data extraction, quality appraisal, and data analysis. Both authors have made crucial and indispensable contributions towards the completion of the project and thus qualified as the co-first authors of the paper. Both Du YD and He KL have played essential and indispensable roles in the study design, data interpretation, and manuscript preparation as the co-corresponding authors. He KL applied for and obtained the funds for this research project. He KL conceptualized, designed, and supervised the whole process of the project. He revised and submitted the early version of the manuscript with a focus on the predictive performance, quality assessment, and clinical application of current PHLF models. Du YD was instrumental and responsible for data re-analysis and re-interpretation, figure plotting, preparation and submission of the current version of the manuscript with a new focus on the PHLF models based on AI technology and their further development trends. This collaboration between He KL and Du YD is crucial for the publication of this manuscript and other manuscripts still in preparation.
Supported by The Science and Technology Innovation 2030 - Major Project, No. 2021ZD0140406.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: Kun-Lun He, Medical Big Data Research Center, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing 100853, China.
Received: November 18, 2024
Revised: February 28, 2025
Accepted: March 21, 2025
Published online: April 27, 2025
Processing time: 161 Days and 5.4 Hours
Revised: February 28, 2025
Accepted: March 21, 2025
Published online: April 27, 2025
Processing time: 161 Days and 5.4 Hours
Core Tip
Core Tip: Currently, with the exploration of new meaningful predictive variables and modeling methods, post-hepatectomy liver failure prognostic models are developing rapidly. However, for existing models, the issues of the analysis domain are the main reason for being assessed as having a high risk of bias. Therefore, researchers should also concentrate on validating existing models to realize their clinical application potential. Additionally, the development of new tools for evaluating model quality may be necessary, and future efforts must follow strict guidelines to maintain the integrity of the models.