Copyright: ©Author(s) 2026. 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.
World J Gastroenterol. Jul 21, 2026; 32(27): 118717
Published online Jul 21, 2026. doi: 10.3748/wjg.118717
Published online Jul 21, 2026. doi: 10.3748/wjg.118717
Machine learning-driven pathogen cluster analysis identifies high-risk subtypes of infected pancreatic necrosis in a multi-center cohort
Bai-Qi Liu, Ze-Fang Sun, Cai-Hong Ning, Chia-Yen Lin, Xiao-Yue Hong, Rong Guo, Lu Chen, Xin-Tong Cao, Ding-Cheng Shen, Geng-Wen Huang, Division of Pancreatic Surgery, Department of General Surgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan Province, China
Jie Xiao, Di Wu, Department of Emergency, The Third Xiangya Hospital, Central South University, Changsha 410008, Hunan Province, China
Author contributions: Liu BQ designed the study and drafted the manuscript; Liu BQ, Sun ZF, Ning CH, Xiao J, Wu D and Shen DC extracted and collected; Liu BQ analyzed the collected data; Sun ZF, Ning CH, Lin CY, Hong XY, Guo R, Cao XT, Shen DC, Chen L and Huang GW reviewed the results and revised the manuscript; Huang GW supervised the study; and the corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.
AI contribution statement: ChatGPT was used exclusively for language polishing and translation. All scientific content was drafted and revised by the authors. All figures were created by the authors from original data.
Supported by National Natural Science Foundation of China, No. 82570772 and No. 82403227; and China Postdoctoral Science Foundation, No. 2024M763715.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Xiangya Hospital (No. 201012067) and the Third Xiangya Hospital (No. 21019).
Informed consent statement: Written informed consent was obtained from all participants or their legal representatives for the use of their clinical data for research purposes.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data supporting the findings of this study are available upon reasonable request from the corresponding author.
Corresponding author: Geng-Wen Huang, MD, PhD, Full Professor, Division of Pancreatic Surgery, Department of General Surgery, Xiangya Hospital, Central South University, No. 97 Xiangya Road, Kaifu District, Changsha 410008, Hunan Province, China.
Received: January 12, 2026
Revised: February 2, 2026
Accepted: March 30, 2026
Published online: July 21, 2026
Processing time: 178 Days and 4.7 Hours
Revised: February 2, 2026
Accepted: March 30, 2026
Published online: July 21, 2026
Processing time: 178 Days and 4.7 Hours
Core Tip
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.