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Article: Analysis of risk factors for early mortality following hepatectomy in hepatocellular carcinoma patients

TitleAnalysis of risk factors for early mortality following hepatectomy in hepatocellular carcinoma patients
Authors
Issue Date16-Dec-2025
PublisherBioMed Central
Citation
BMC Surgery, 2025, v. 25 How to Cite?
Abstract

Objective

This study aimed to identify risk factors associated with early mortality (within 2 years) following hepatectomy in patients with hepatocellular carcinoma (HCC), with the goal of informing more rational treatment strategies.

Methods

A retrospective analysis was conducted on clinical data from 236 HCC patients who underwent hepatectomy at the University of Hong Kong-Shenzhen Hospital between 2013 and 2021. Patients were categorized into early mortality (EM) and non-early mortality (NEM) groups based on survival status at 24 months post-surgery. Univariate and multivariable logistic regression analyses were employed to identify independent risk factors for early mortality. Based on the availability of clinical information, preoperative and early postoperative prediction models were developed, and corresponding nomograms were constructed. The predictive performance of the model was assessed using receiver operating characteristic (ROC) curves, and patients were stratified into different risk groups based on the predicted probabilities for further evaluation.

Results

Independent risk factors for EM in the preoperative assessment included Child–Pugh grade B (P < .001), advanced BCLC staging (P = .010), and high Tumor burden score (TBS) (P < .001). For the early postoperative assessment, independent risk factors were Child–Pugh grade B (P < .001), high TBS (P < .001), non-Textbook Outcome (P < .001), and presence of Microvascular invasion (MVI) (P = .014). The preoperative model achieved an area under the ROC curve (AUC) of 0.832, while the postoperative model yielded an AUC of 0.865, indicating strong predictive performance. Stratification of patients based on predicted probabilities into distinct risk groups revealed statistically significant differences in both 2-year and 5-year survival rates, demonstrating robust risk-discriminatory ability.

Conclusion

Based on the availability of clinical information, this study identified independent risk factors associated with EM in both the preoperative and early postoperative phases, and constructed two nomograms to visualize the corresponding risks. Patients with multiple risk factors—particularly those deemed high-risk for EM (predicted probability > 66%) according to the nomogram—faced a significantly higher risk of early death.


Persistent Identifierhttp://hdl.handle.net/10722/368576
ISSN
2023 Impact Factor: 1.6
2023 SCImago Journal Rankings: 0.520

 

DC FieldValueLanguage
dc.contributor.authorKou, Chunwei-
dc.contributor.authorZhu, Hongtao-
dc.contributor.authorFan, Limin-
dc.contributor.authorFan, Weitian-
dc.contributor.authorCheung, Tan To-
dc.contributor.authorJi, Ren-
dc.date.accessioned2026-01-14T00:35:30Z-
dc.date.available2026-01-14T00:35:30Z-
dc.date.issued2025-12-16-
dc.identifier.citationBMC Surgery, 2025, v. 25-
dc.identifier.issn1471-2482-
dc.identifier.urihttp://hdl.handle.net/10722/368576-
dc.description.abstract<h3>Objective</h3><p>This study aimed to identify risk factors associated with early mortality (within 2 years) following hepatectomy in patients with hepatocellular carcinoma (HCC), with the goal of informing more rational treatment strategies.</p><h3>Methods</h3><p>A retrospective analysis was conducted on clinical data from 236 HCC patients who underwent hepatectomy at the University of Hong Kong-Shenzhen Hospital between 2013 and 2021. Patients were categorized into early mortality (EM) and non-early mortality (NEM) groups based on survival status at 24 months post-surgery. Univariate and multivariable logistic regression analyses were employed to identify independent risk factors for early mortality. Based on the availability of clinical information, preoperative and early postoperative prediction models were developed, and corresponding nomograms were constructed. The predictive performance of the model was assessed using receiver operating characteristic (ROC) curves, and patients were stratified into different risk groups based on the predicted probabilities for further evaluation.</p><h3>Results</h3><p>Independent risk factors for EM in the preoperative assessment included Child–Pugh grade B (<em>P</em> < .001), advanced BCLC staging (<em>P</em> = .010), and high Tumor burden score (TBS) (<em>P</em> < .001). For the early postoperative assessment, independent risk factors were Child–Pugh grade B (<em>P</em> < .001), high TBS (<em>P</em> < .001), non-Textbook Outcome (<em>P</em> < .001), and presence of Microvascular invasion (MVI) (<em>P</em> = .014). The preoperative model achieved an area under the ROC curve (AUC) of 0.832, while the postoperative model yielded an AUC of 0.865, indicating strong predictive performance. Stratification of patients based on predicted probabilities into distinct risk groups revealed statistically significant differences in both 2-year and 5-year survival rates, demonstrating robust risk-discriminatory ability.</p><h3>Conclusion</h3><p>Based on the availability of clinical information, this study identified independent risk factors associated with EM in both the preoperative and early postoperative phases, and constructed two nomograms to visualize the corresponding risks. Patients with multiple risk factors—particularly those deemed high-risk for EM (predicted probability > 66%) according to the nomogram—faced a significantly higher risk of early death.</p>-
dc.languageeng-
dc.publisherBioMed Central-
dc.relation.ispartofBMC Surgery-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleAnalysis of risk factors for early mortality following hepatectomy in hepatocellular carcinoma patients-
dc.typeArticle-
dc.identifier.doi10.1186/s12893-025-03333-6-
dc.identifier.volume25-
dc.identifier.eissn1471-2482-
dc.identifier.issnl1471-2482-

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