Wang D, Zhang JY, Xie Y, Zhang KN, Jiang WT. Interpretable machine learning model for early complication prediction after split liver transplantation. World J Gastroenterol 2025; 31(47): 114370 [PMID: 41479751 DOI: 10.3748/wjg.v31.i47.114370]
Corresponding Author of This Article
Wen-Tao Jiang, Chief Physician, Dean, Full Professor, Department of Liver Transplantation, First Central Hospital of Tianjin Medical University, No. 2 Baoshan West Road, Xiqing District, Tianjin 300380, China. jiangwentao@nankai.edu.cn
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Gastroenterology & Hepatology
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Retrospective Study
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Dec 21, 2025 (publication date) through Mar 18, 2026
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World Journal of Gastroenterology
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1007-9327
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Wang D, Zhang JY, Xie Y, Zhang KN, Jiang WT. Interpretable machine learning model for early complication prediction after split liver transplantation. World J Gastroenterol 2025; 31(47): 114370 [PMID: 41479751 DOI: 10.3748/wjg.v31.i47.114370]
World J Gastroenterol. Dec 21, 2025; 31(47): 114370 Published online Dec 21, 2025. doi: 10.3748/wjg.v31.i47.114370
Interpretable machine learning model for early complication prediction after split liver transplantation
Di Wang, Jun-Yan Zhang, Yan Xie, Kun-Ning Zhang, Wen-Tao Jiang
Di Wang, Jun-Yan Zhang, Yan Xie, Wen-Tao Jiang, Department of Liver Transplantation, First Central Hospital of Tianjin Medical University, Tianjin 300380, China
Kun-Ning Zhang, School of Medicine, Nankai University, Tianjin 300192, China
Co-first authors: Di Wang and Jun-Yan Zhang.
Co-corresponding authors: Yan Xie and Wen-Tao Jiang.
Author contributions: Wang D and Zhang JY made equal contributions as co-first authors; Wang D, Zhang JY, and Xie Y contributed to conceptualization and investigation; Jiang WT did project administration; Wang D and Zhang KN contributed to methodology and supervision; Jiang WT and Xie Y acquired the funding and contributed equally as co-corresponding authors; Wang D and Xie Y performed validation and visualization; all authors contributed to manuscript writing and approved the final version to publish.
Supported by Tianjin Key Medical Discipline Construction Project, No. TJYXZDXK-3-006A; Tianjin Municipal Health Commission General Fund Project, No. TJWJ2024MS017; Key Project of Tianjin Science and Technology Bureau Applied Basic Research, No. 23JCZDJC01200; The Independent Research Fund of the Institute of Transplant Medicine at Nankai University, No. NKTM2023004; The General Project of the China Medicine Education Association, No. ZJWYH-2023-YIZHI-028; and General Project of Scientific Research Plan of Tianjin Municipal Education Commission, No. 2024ZX013.
Institutional review board statement: The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the First Central Hospital of Tianjin Medical University, No. 2019N168KY.
Informed consent statement: Due to the retrospective study design, this study waived the need for written informed consent.
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 on reasonable request.
Corresponding author: Wen-Tao Jiang, Chief Physician, Dean, Full Professor, Department of Liver Transplantation, First Central Hospital of Tianjin Medical University, No. 2 Baoshan West Road, Xiqing District, Tianjin 300380, China. jiangwentao@nankai.edu.cn
Received: September 22, 2025 Revised: October 7, 2025 Accepted: November 4, 2025 Published online: December 21, 2025 Processing time: 89 Days and 21.1 Hours
Abstract
BACKGROUND
Split liver transplantation (SLT) effectively expands the donor pool but carries a higher risk of early postoperative complications (EPC) due to the extensive transection surface and altered hemodynamics of partial grafts.
AIM
To establish an interpretable machine learning framework to identify risk factors for EPC in adult recipients undergoing right tri-segment SLT.
METHODS
We retrospectively analyzed 109 adult SLT recipients, including 37 who developed EPC. A comprehensive set of perioperative donor and recipient variables was evaluated using four machine learning algorithms (random forest, support vector machine, extreme gradient boosting, and logistic regression). SHapley Additive exPlanations were employed to rank variable importance. Independent predictors were further validated through multivariate logistic regression, and a diagnostic nomogram was constructed. Restricted cubic spline, receiver operating characteristic, and survival analyses were conducted to evaluate model performance and clinical outcomes.
RESULTS
EPC occurred in 33.9% of recipients. Among the machine learning models, random forest demonstrated the best predictive performance. SHapley Additive exPlanations analysis identified the log-transformed systemic immune-inflammation index (LnSII), albumin-to-fibrinogen ratio, model for end-stage liver disease (MELD) score, partial lobectomy of segment IV (IV PL), intraoperative blood loss, and operation time as major contributors to the model. Multivariate logistic regression confirmed LnSII, MELD scores, IV PL, and blood loss as independent predictors of EPC. The nomogram constructed from these factors showed good discrimination and calibration (area under the curve = 0.788, 95% confidence interval: 0.734-0.906). Kaplan-Meier analysis revealed that both LnSII and MELD scores were associated with five-year overall survival (P < 0.05), while MELD score and IV PL were significantly correlated with early postoperative liver function recovery.
CONCLUSION
IV PL during right tri-segment SLT appears to reduce the risk of EPC and enhance postoperative liver function recovery. Together with LnSII, blood loss, and MELD score, these factors offer a reliable foundation for individualized perioperative risk stratification and management.
Core Tip: This study employed an interpretable machine learning framework to assess risk factors for early postoperative complications in adult recipients of right tri-segment split liver transplantation. We identified systemic immune-inflammation index, model for end-stage liver disease score, intraoperative blood loss, and partial lobectomy of segment IV as independent predictors. A nomogram incorporating these variables demonstrated robust predictive accuracy. These findings highlight the clinical utility of integrating inflammatory status, surgical factors, and intraoperative variables for individualized perioperative management in split liver transplantation.
