| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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: https://creativecommons.org/Licenses/by-nc/4.0/ |
| 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 |
Development and validation of an interpretable machine learning model for predicting acute kidney injury after pancreatic surgery
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Chen Lin, Ren-Kui Fu, Hua Zheng, Tian-Yu Li, Jia-Shu Han, Georgios A Margonis, Jaeyun J Wang, Liang-Bo Dong, Na-Su Wang, Yi-Xuan Sun, Yao-Zong Wang, Chang Liu, Qiang Xu, Xian-Lin Han, Tai-Ping Zhang, Jun-Chao Guo, Meng-Hua Dai, Peng Xia, Li-Meng Chen and Wei-Bin Wang |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
No. 82573412 |
| National Natural Science Foundation of China |
No. 82173074 |
| Nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences |
No. 2018PT32014 |
| Capital’s Funds for Health Improvement and Research |
No. 2024-2-4017 |
| National Multidisciplinary Cooperative Diagnosis and Treatment Capacity Building Project for Major Diseases |
No. ZK12101 |
| National High Level Hospital Clinical Research Funding |
No. 2025-PUMCH-A-073 |
|
| Corresponding Author |
Wei-Bin Wang, Chief, MD, PhD, Postdoc, Professor, Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifu Yuan, Dongcheng District, Beijing 100730, China. wwb_xh@163.com |
| Key Words |
Acute kidney injury; Machine learning; Pancreatic surgery; Prediction model; Risk factors |
| Core Tip |
Using a decade-long pancreatic surgery cohort, we developed and validated an interpretable machine learning model to identify patients at risk for postoperative acute kidney injury using routinely available perioperative data. The final model achieved good discrimination and highlighted modifiable drivers of acute kidney injury, including longer operative time, early postoperative serum creatinine elevation, admission to the intensive care unit, increased postoperative white blood cell count, and greater intraoperative blood loss. An online risk calculator is provided to support bedside individualized prediction and early renoprotective management. |
| Publish Date |
2026-05-18 10:29 |
| Citation |
Lin C, Fu RK, Zheng H, Li TY, Han JS, Margonis GA, Wang JJ, Dong LB, Wang NS, Sun YX, Wang YZ, Liu C, Xu Q, Han XL, Zhang TP, Guo JC, Dai MH, Xia P, Chen LM, Wang WB. Development and validation of an interpretable machine learning model for predicting acute kidney injury after pancreatic surgery. World J Gastroenterol 2026; 32(19): 116271 |
| URL |
https://www.wjgnet.com/1007-9327/full/v32/i19/116271.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v32.i19.116271 |
