Publication Name	World Journal of Gastroenterology
Manuscript ID	114370
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Study
Article Title	Interpretable machine learning model for early complication prediction after split liver transplantation
Manuscript Source	Unsolicited Manuscript
All Author List	Di Wang, Jun-Yan Zhang, Yan Xie, Kun-Ning Zhang and Wen-Tao Jiang
Funding Agency and Grant Number	Funding Agency Grant Number Tianjin Key Medical Discipline Construction Project TJYXZDXK-3-006A Tianjin Municipal Health Commission General Fund Project TJWJ2024MS017 Key Project of Tianjin Science and Technology Bureau Applied Basic Research 23JCZDJC01200 The Independent Research Fund of the Institute of Transplant Medicine at Nankai University NKTM2023004 The General Project of the China Medicine Education Association ZJWYH-2023-YIZHI-028 General Project of Scientific Research Plan of Tianjin Municipal Education Commission 2024ZX013
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
Key Words	Early postoperative complications; Machine learning; Partial lobectomy of segment IV; Split liver transplantation; Systemic immune-inflammation index
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.
Citation	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; In press

Received	2025-09-22 06:59
Peer-Review Started	2025-09-22 06:59
To Make the First Decision
Return for Revision	2025-09-29 09:19
Revised	2025-10-07 13:52
Second Decision	2025-11-04 02:33
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-11-04 09:16
Articles in Press	2025-11-04 09:16
Publication Fee Transferred	2025-10-13 04:22
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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