Yin H, Liu Y, Chen JT, Li Z, Pan Y, Zhu F. Predictive value of serum biochemical indicators combined with imaging scores for prognosis of traumatic liver rupture. World J Gastrointest Surg 2026; 18(3): 113773 [DOI: 10.4240/wjgs.v18.i3.113773]
Corresponding Author of This Article
Feng Zhu, Department of General Surgery, Sir Run Run Hospital, Nanjing Medical University, No. 109 Longmian Avenue, Jiangning District, Nanjing 211166, Jiangsu Province, China. zhufeng1524000@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
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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/
Mar 27, 2026 (publication date) through Mar 31, 2026
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Journal Information of This Article
Publication Name
World Journal of Gastrointestinal Surgery
ISSN
1948-9366
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Yin H, Liu Y, Chen JT, Li Z, Pan Y, Zhu F. Predictive value of serum biochemical indicators combined with imaging scores for prognosis of traumatic liver rupture. World J Gastrointest Surg 2026; 18(3): 113773 [DOI: 10.4240/wjgs.v18.i3.113773]
Hang Yin, Yong Liu, Jia-Tong Chen, Zhuang Li, Yue Pan, Feng Zhu, Department of General Surgery, Sir Run Run Hospital, Nanjing Medical University, Nanjing 211166, Jiangsu Province, China
Author contributions: Yin H and Zhu F conceived and designed the study; Yin H, Liu Y, Chen JT, and Pan Y collected the data; Yin H and Li Z performed the statistical analysis; Liu Y performed the imaging scoring; Yin H drafted the manuscript; Zhu F provided critical revision and supervision; and all authors participated in the discussion and revision of the manuscript and approved the final version for publication.
Institutional review board statement: This study was approved by the Medical Ethics Committee of Sir Run Run Hospital, Nanjing Medical University, approval No. 2024-SR-070.
Informed consent statement: As this was a retrospective study using anonymized clinical data, the requirement for informed consent was waived by the Institutional Review Board of Sir Run Run Hospital, Nanjing Medical University.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
Corresponding author: Feng Zhu, Department of General Surgery, Sir Run Run Hospital, Nanjing Medical University, No. 109 Longmian Avenue, Jiangning District, Nanjing 211166, Jiangsu Province, China. zhufeng1524000@163.com
Received: October 17, 2025 Revised: November 23, 2025 Accepted: January 9, 2026 Published online: March 27, 2026 Processing time: 161 Days and 4 Hours
Abstract
BACKGROUND
Traumatic liver rupture has high mortality (10%-15%) but current prognostic tools show limited accuracy. This study develops an integrated model combining serum biomarkers and imaging scores for improved risk stratification.
AIM
To investigate the predictive value of serum biochemical indicators combined with imaging scores for the prognosis of patients with traumatic liver rupture, establish a comprehensive prediction model, and provide scientific evidence for early clinical identification of high-risk patients.
METHODS
A single-center retrospective cohort study was conducted, including 186 patients with traumatic liver rupture treated from June 2020 to June 2024. Patient baseline data, serum biochemical indicators within 2 hours of admission (including liver function indicators, coagulation function, inflammatory indicators), and imaging scores were collected. The primary outcome was adverse prognosis within 30 days, including death, need for emergency surgical intervention, severe complications, or need for Intensive care unit treatment. Univariate and multivariate logistic regression analyses were used to screen independent predictive factors, establish a prediction model, and evaluate its predictive efficacy.
RESULTS
Among 186 patients, 142 cases (76.3%) had good prognosis and 44 cases (23.7%) had poor prognosis. Age, liver function indicators, coagulation function indicators, inflammatory indicators, and imaging scores in the poor prognosis group were significantly higher than those in the good prognosis group (all P < 0.001). Multivariate logistic regression analysis showed that aspartate aminotransferase, international normalized ratio, C-reactive protein, Glasgow Coma Scale score, and long-term services and supports (LTSS) total score were independent predictive factors for prognosis (all P < 0.05). The combined prediction model based on these 5 indicators had a receiver operating characteristic area under the curve (AUC) of 0.923 (95%confidence interval: 0.881-0.965), with sensitivity of 86.4%, specificity of 91.5%, and Youden index of 0.779, significantly superior to using serum biochemical indicators alone (AUC = 0.887) or long-term services and supports score alone (AUC = 0.845) (all P < 0.05). Decision curve analysis showed that the model had good clinical net benefit.
CONCLUSION
The comprehensive prediction model established by combining serum biochemical indicators with imaging scores can effectively predict the prognosis of patients with traumatic liver rupture, providing important scientific evidence for early clinical identification of high-risk patients.
Core Tip: This study established a comprehensive prognostic prediction model for traumatic liver rupture by integrating serum biochemical indicators with clinical assessment and imaging evaluation. In a cohort of 186 patients, the integrated model demonstrated superior predictive performance compared to single-modality approaches. The model showed excellent discrimination and clinical utility, offering a practical tool for early identification of high-risk patients and guiding individualized treatment strategies in traumatic liver rupture management. All predictive variables are routinely available within 2 hours of hospital admission, ensuring rapid risk assessment without additional costs or specialized testing.
