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Articles Published Processes
7/27/2026 9:36:16 AM | Browse: 1 | Download: 0
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Received |
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2026-03-13 01:12 |
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Peer-Review Started |
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2026-03-13 01:15 |
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First Decision by Editorial Office Director |
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2026-04-10 09:43 |
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Return for Revision |
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2026-04-11 01:55 |
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Revised |
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2026-04-14 14:22 |
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Publication Fee Transferred |
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2026-04-21 09:13 |
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Second Decision by Editor |
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2026-05-08 02:37 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-05-08 09:52 |
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Articles in Press |
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2026-05-08 09:52 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-06-24 05:53 |
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Publish the Manuscript Online |
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2026-07-27 09:36 |
| ISSN |
1948-9366 (online) |
| Open Access |
Open-Access: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Surgery |
| Manuscript Type |
Retrospective Study |
| Article Title |
Machine learning models for predicting acute kidney injury after pediatric living donor liver transplantation in biliary atresia
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Rong-Rong Wang, Min Zhu, Heng-Chang Ren and Wen-Li Yu |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Tianjin Key Clinical Specialty Construction Project, Tianjin Key Medical Discipline Construction Project |
TJYXZDXK-3-022C |
| Scientific Research Program of the Tianjin Municipal Education Commission |
2025ZD40 |
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| Corresponding Author |
Wen-Li Yu, PhD, Department of Anesthesiology, Tianjin First Central Hospital, No. 24 Fukang Road, Tianjin 300192, China. yzxyuwenli@163.com |
| Key Words |
Pediatric living donor liver transplantation; Acute kidney injury; Machine learning; Risk prediction |
| Core Tip |
This study included 340 children with biliary atresia who underwent liver transplantation. Seven key predictors of acute kidney injury (AKI) were screened out by least absolute shrinkage and selection operator algorithm, including pre-operative/post-operative creatinine (Cr), blood calcium and lactic acid levels during the anhepatic phase, gender, the amount of fresh frozen plasma infused during the operation, and post-operative aspartate aminotransferase level. Nine machine learning methods, including XGBoost, were used to construct the postoperative AKI prediction model based on other features after excluding postoperative Cr, and their prediction performance was compared. This study aims to assist clinicians in early intervention and improve the prognosis of children. |
| Publish Date |
2026-07-27 09:36 |
| Citation |
Wang RR, Zhu M, Ren HC, Yu WL. Machine learning models for predicting acute kidney injury after pediatric living donor liver transplantation in biliary atresia. World J Gastrointest Surg 2026; 18(7): 120759
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| URL |
https://www.wjgnet.com/1948-9366/full/v18/i7/120759.htm |
| DOI |
https://doi.org/10.4240/wjgs.v18.i7.120759 |
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