| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| 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
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Machine learning-driven pathogen cluster analysis identifies high-risk subtypes of infected pancreatic necrosis in a multi-center cohort
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Bai-Qi Liu, Ze-Fang Sun, Cai-Hong Ning, Jie Xiao, Di Wu, Chia-Yen Lin, Xiao-Yue Hong, Rong Guo, Lu Chen, Xin-Tong Cao, Ding-Cheng Shen and Geng-Wen Huang |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
82570772 and 82403227 |
| China Postdoctoral Science Foundation |
2024M763715 |
|
| Corresponding Author |
Geng-Wen Huang, Full Professor, MD, PhD, Division of Pancreatic Surgery, Department of General Surgery, Xiangya Hospital, Central South University, No. 97 Xiangya Road, Kaifu District, Changsha 410008, Hunan Province, China. huanggengwen@csu.edu.cn |
| Key Words |
Infected pancreatic necrosis; Machine learning; Pathogen clustering; Multidrug-resistant organisms; Risk stratification |
| Core Tip |
Infected pancreatic necrosis (IPN) is clinically heterogeneous, however current risk stratification relies largely on physiological scores and overlooks pathogen patterns. Using unsupervised machine learning, we identified four reproducible pathogen-based IPN subtypes with markedly different mortality risks in a multicenter cohort. High-risk subtypes were driven by co-occurrence of multidrug-resistant organisms and fungi, whereas Escherichia coli–dominant polymicrobial infections were associated with favorable outcomes. This study highlights pathogen clustering as a clinically actionable approach for prognostic stratification and personalized infection management in IPN. |
| Publish Date |
2026-07-14 07:33 |
| Citation |
Liu BQ, Sun ZF, Ning CH, Xiao J, Wu D, Lin CY, Hong XY, Guo R, Chen L, Cao XT, Shen DC, Huang GW. Machine learning-driven pathogen cluster analysis identifies high-risk subtypes of infected pancreatic necrosis in a multi-center cohort. World J Gastroenterol 2026; 32(27): 118717
|
| URL |
https://www.wjgnet.com/1007-9327/full/v32/i27/118717.htm |
| DOI |
https://doi.org/10.3748/wjg.118717 |
