| ISSN |
1948-9366 (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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved. |
| 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 |
Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease: A machine learning-based study
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Fang-Tao Wang, Yin Lin, Xiao-Qi Yuan, Ren-Yuan Gao, Xiao-Cai Wu, Wei-Wei Xu, Tian-Qi Wu, Kai Xia, Yi-Ran Jiao, Lu Yin and Chun-Qiu Chen |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Horizontal Project of Shanghai Tenth People’s Hospital |
DS05!06!22016, DS05!06!22017 |
|
| Corresponding Author |
Chun-Qiu Chen, MD, PhD, Associate Professor, Chief Doctor, Diagnostic and Treatment Center for Refractory Diseases of Abdomen Surgery, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yanchang Middle Road, Shanghai 200072, China. chenchunqiu6@126.com |
| Key Words |
Crohn’s disease; Postoperative complications; Nomogram; Random forest; Intestinal resection |
| Core Tip |
Given the unique characteristics of Crohn’s disease (CD), the incidence of postoperative complications is notably high. Previous studies, while identifying risk factors influencing these complications, often yield inconsistent results due to the heterogeneity of patient populations. This machine learning-based study included data from a single center over a relatively short period in China. Novel models employing logistic regression and random forest were developed to inform individualized perioperative management of patients with CD. The models, particularly the random forest, demonstrated robust performance, highlighting the significance of preoperative CD activity index, serum albumin levels, and operation time as crucial predictors. |
| Publish Date |
2024-03-22 11:13 |
| Citation |
Wang FT, Lin Y, Yuan XQ, Gao RY, Wu XC, Xu WW, Wu TQ, Xia K, Jiao YR, Yin L, Chen CQ. Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease: A machine learning-based study. World J Gastrointest Surg 2024; 16(3): 717-730 |
| URL |
https://www.wjgnet.com/1948-9366/full/v16/i3/717.htm |
| DOI |
https://dx.doi.org/10.4240/wjgs.v16.i3.717 |
