Sun K, Li JB, Chen YF, Zhai ZJ, Chen L, Dong R. Predicting post-hepatectomy liver failure using a nomogram based on portal vein width, inflammatory indices, and the albumin-bilirubin score. World J Gastrointest Surg 2025; 17(2): 99529 [DOI: 10.4240/wjgs.v17.i2.99529]
Corresponding Author of This Article
Rui Dong, MD, Affiliate Associate Professor, Associate Chief Physician, Department of General Surgery, The Second Affiliated Hospital of Air Force Medical University, No. 569 Xinsi Road, Baqiao District, Xi’an 710000, Shaanxi Province, China. s1208532322@outlook.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Surg. Feb 27, 2025; 17(2): 99529 Published online Feb 27, 2025. doi: 10.4240/wjgs.v17.i2.99529
Predicting post-hepatectomy liver failure using a nomogram based on portal vein width, inflammatory indices, and the albumin-bilirubin score
Ke Sun, Jiang-Bin Li, Ya-Feng Chen, Zhong-Jie Zhai, Lang Chen, Rui Dong
Ke Sun, Xi’an Medical College, Xi’an 710000, Shaanxi Province, China
Jiang-Bin Li, Ya-Feng Chen, Lang Chen, Rui Dong, Department of General Surgery, The Second Affiliated Hospital of Air Force Medical University, Xi’an 710000, Shaanxi Province, China
Zhong-Jie Zhai, Statistics Teaching and Research Office, Air Force Medical University, Xi’an 710038, Shaanxi Province, China
Author contributions: Sun K designed the research study; Li JB, Chen YF and Chen L collected and assembled data; Sun K and Zhai ZJ performed data analysis; Sun K wrote the manuscript; Dong R critically revised the manuscript for important intellectual content.
Supported by Shaanxi Provincial Social Development Fund, No. 2024SF-YBXM-140.
Institutional review board statement: This study was approved by the Ethics Committee of The Second Affiliated Hospital of Air Force Medical University, No. K202407-01.
Informed consent statement: This study was retrospective, and the Ethics Committee of the Second Affiliated Hospital of Air Force Medical University approved the waiver of informed consent.
Conflict-of-interest statement: All authors declare no conflicts of interest.
Data sharing statement: The data for this study is available from the corresponding author upon reasonable request at s1208532322@outlook.com.
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: Rui Dong, MD, Affiliate Associate Professor, Associate Chief Physician, Department of General Surgery, The Second Affiliated Hospital of Air Force Medical University, No. 569 Xinsi Road, Baqiao District, Xi’an 710000, Shaanxi Province, China. s1208532322@outlook.com
Received: July 24, 2024 Revised: October 6, 2024 Accepted: October 30, 2024 Published online: February 27, 2025 Processing time: 181 Days and 19.4 Hours
Abstract
BACKGROUND
Post-hepatectomy liver failure (PHLF) after liver resection is one of the main complications causing postoperative death in patients with hepatocellular carcinoma (HCC). It is crucial to help clinicians identify potential high-risk PHLF patients as early as possible through preoperative evaluation.
AIM
To identify risk factors for PHLF and develop a prediction model.
METHODS
This study included 248 patients with HCC at The Second Affiliated Hospital of Air Force Medical University between January 2014 and December 2023; these patients were divided into a training group (n = 164) and a validation group (n = 84) via random sampling. The independent variables for the occurrence of PHLF were identified by univariate and multivariate analyses and visualized as nomograms. Ultimately, comparisons were made with traditional models via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
RESULTS
In this study, portal vein width [odds ratio (OR) = 1.603, 95%CI: 1.288-1.994, P ≤ 0.001], the preoperative neutrophil-to-lymphocyte ratio (NLR) (OR = 1.495, 95%CI: 1.126-1.984, P = 0.005), and the albumin-bilirubin (ALBI) score (OR = 8.868, 95%CI: 2.144-36.678, P = 0.003) were independent risk factors for PHLF. A nomogram prediction model was developed using these factors. ROC and DCA analyses revealed that the predictive efficacy and clinical value of this model were better than those of traditional models.
CONCLUSION
A new Nomogram model for predicting PHLF in HCC patients was successfully established based on portal vein width, the NLR, and the ALBI score, which outperforms the traditional model.
Core Tip: Post-hepatectomy liver failure, a serious complication, poses a serious threat to patients’ postoperative survival rate and quality of life. Therefore, to avoid this, the preoperative prediction of post-hepatectomy liver failure is crucial. This study revealed that portal vein width, the neutrophil-to-lymphocyte ratio, and the albumin-bilirubin score are independent risk factors for post-hepatectomy liver failure (PHLF). We developed a nomogram model based on blood indicators combined with imaging indicators, and this study was the first to combine imaging indicators of portal vein width to construct a prediction model that can highly predict PHLF.
