Multivariable prognostic models for post-hepatectomy liver failure:An updated systematic review  

作　　者：Xiao Wang Ming-Xiang Zhu Jun-Feng Wang Pan Liu Li-Yuan Zhang You Zhou Xi-Xiang Lin Ying-Dong Du Kun-Lun He 

机构地区：[1]Department of Hepatobiliary Surgery,Chinese PLA 970th Hospital,Yantai 264001,Shandong Province,China [2]Medical Big Data Research Center,Chinese PLA General Hospital,Beijing 100853,China [3]Medical School of Chinese PLA,Chinese PLA General Hospital,Beijing 100853,China [4]Division of Pharmacoepidemiology and Clinical Pharmacology,Utrecht Institute for Pharmaceutical Sciences,Utrecht University,Utrecht 3584CG,Netherlands [5]China National Clinical Research Center for Neurological Diseases,Beijing 100853,China [6]School of Medicine,Nankai University,Tianjin 300071,China

出　　处：《World Journal of Hepatology》2025年第4期85-104,共20页世界肝病学杂志(英文)

基　　金：Supported by The Science and Technology Innovation 2030-Major Project,No.2021ZD0140406.

摘　　要：BACKGROUND Partial hepatectomy continues to be the primary treatment approach for liver tumors,and post-hepatectomy liver failure(PHLF)remains the most critical lifethreatening 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.

关 键 词：Hepatocellular carcinoma Postoperative liver failure Prognostic model Systematic review Risk of bias 

分 类 号：R73[医药卫生—肿瘤]