Journal of Clinical and Translational Hepatology

Journal of Clinical and Translational Hepatology

Thursday, 12 / 09 / 2021



Development and Validation of a Nomogram Based on Perioperative Factors to Predict Post-hepatectomy Liver Failure

Bin Xu1,2,#, Xiao-Long Li1,2,#, Feng Ye3,#, Xiao-Dong Zhu1,2, Ying-Hao Shen1,2, Cheng Huang1,2, Jian Zhou1,2Jia Fan1,2, Yong-Jun Chen3,*and Hui-Chuan Sun1,2,*

1  Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
2  Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Shanghai, China
3  Department of Hepatobiliary Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
#These authors contributed equally to this work.
*Correspondence to:Hui-Chuan Sun, Liver Cancer Institute and Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai 200032, China. ORCID: Tel: +86-21-3115-1990, Fax: +86-21-6403-7181, E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. ; Yong-Jun Chen, Department of Hepatobiliary Surgery and Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Second Ruijin Road, Shanghai 200025, China. ORCID: Tel: +86-21-6431-4781, Fax: +86-21-6431-4781, E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Journal of Clinical and Translational Hepatology 2021;9(3):291-300 DOI: 10.14218/JCTH.2021.00013
Received: January 5, 2021 Accepted: February 17, 2021 Published online: March 15, 2021


Background and Aims:Post-hepatectomy liver failure (PHLF) is a severe complication and main cause of death in patients undergoing hepatectomy. The aim of this study was to build a predictive model of PHLF in patients undergoing hepatectomy.

Methods: We retrospectively analyzed patients undergoing hepatectomy at Zhongshan Hospital, Fudan University from July 2015 to June 2018, and randomly divided them into development and internal validation cohorts. External validation was performed in an independent cohort. Least absolute shrinkage and selection operator (commonly referred to as LASSO) logistic regression was applied to identify predictors of PHLF, and multivariate binary logistic regression analysis was performed to establish the predictive model, which was visualized with a nomogram.

Results:A total of 492 eligible patients were analyzed. LASSO and multivariate analysis identified three preoperative variables, total bilirubin (p=0.001), international normalized ratio (p<0.001) and platelet count (p=0.004), and two intraoperative variables, extent of resection (p=0.002) and blood loss (p=0.004), as independent predictors of PHLF. The area under receiver operating characteristic curve (referred to as AUROC) of the predictive model was 0.838 and outperformed the model for end-stage liver disease score, albumin-bilirubin score and platelet-albumin-bilirubin score (AUROCs: 0.723, 0.695 and 0.663, respectively; p<0.001 for all). The optimal cut-off value of the predictive model was 14.7. External validation showed the model could predict PHLF accurately and distinguish high-risk patients.

Conclusions:PHLF can be accurately predicted by this model in patients undergoing hepatectomy, which may significantly contribute to the postoperative care of these patients.


Hepatectomy, Post-hepatectomy liver failure, LASSO, Nomogram

Journal of Clinical and Translational Hepatology 2021 vol. 9, 291-300  [ Html  ] [ PDF Full-text ]

© 2021 Authors. This is an Open Access article distributed under the terms of the  Creative Commons Attribution-Noncommercial 4.0 License(CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


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