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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Multivariable prognostic models for post-hepatectomy liver failure: An updated systematic review
Xiao Wang, Ming-Xiang Zhu, Jun-Feng Wang, Pan Liu, Li-Yuan Zhang, You Zhou, Xi-Xiang Lin, Ying-Dong Du, Kun-Lun He
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.
hekunlun301dr@163.com
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
BACKGROUND
Partial hepatectomy continues to be the primary treatment approach for liver tumors, and post-hepatectomy liver failure (PHLF) remains the most critical life-threatening complication following surgery.
AIM
To comprehensively review the PHLF prognostic models developed in recent years and objectively assess the risk of bias in these models.
METHODS
This review followed the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Three databases were searched from November 2019 to December 2022, and references as well as cited literature in all included studies were manually screened in March 2023. Based on the defined inclusion criteria, articles on PHLF prognostic models were selected, and data from all included articles were extracted by two independent reviewers. The PROBAST was used to evaluate the quality of each included article.
RESULTS
A total of thirty-four studies met the eligibility criteria and were included in the analysis. Nearly all of the models (32/34, 94.1%) were developed and validated exclusively using private data sources. Predictive variables were categorized into five distinct types, with the majority of studies (32/34, 94.1%) utilizing multiple types of data. The area under the curve for the training models included ranged from 0.697 to 0.956. Analytical issues resulted in a high risk of bias across all studies included.
CONCLUSION
The validation performance of the existing models was substantially lower compared to the development models. All included studies were evaluated as having a high risk of bias, primarily due to issues within the analytical domain. The progression of modeling technology, particularly in artificial intelligence modeling, necessitates the use of suitable quality assessment tools.
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.
