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 [DOI: 10.3748/wjg.118717]
Corresponding Author of This Article
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. huanggengwen@csu.edu.cn
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Gastroenterology & Hepatology
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research-article
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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 [DOI: 10.3748/wjg.118717]
World J Gastroenterol. Jul 21, 2026; 32(27): 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, Jie Xiao, Di Wu, Chia-Yen Lin, Xiao-Yue Hong, Rong Guo, Lu Chen, Xin-Tong Cao, Ding-Cheng Shen, Geng-Wen Huang
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. huanggengwen@csu.edu.cn
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
Abstract
BACKGROUND
Infected pancreatic necrosis (IPN) presents with highly variable clinical trajectories that are significantly influenced by the underlying microbial profile. Although established prognostic models, such as the Acute Physiology and Chronic Health Evaluation II and Bedside Index of Severity in Acute Pancreatitis scoring systems, evaluate host physiological severity, they do not adequately account for the impact of specific pathogen compositions on patient survival.
AIM
To utilize machine learning-driven pathogen cluster analysis to stratify patients with IPN into distinct clusters for precise risk prediction.
METHODS
We conducted a hierarchical clustering analysis on microbiological data from 396 IPN patients, using the Jaccard distance to identify distinct pathogen-driven clusters. Clinical outcomes were compared across these clusters. External cohort validation was conducted to assess the effect of clusters.
RESULTS
Four distinct pathogen clusters (α–δ) were identified, including an Enterococcus faecium/Enterobacter cloacae–predominant cluster (α), an Escherichia coli–dominant cluster (β), a multidrug-resistant organism-enriched cluster (γ), and an Acinetobacter baumannii–Candida glabrata co-infection cluster (δ). Cluster α, characterized by Enterococcus faecium and Enterobacter cloacae, demonstrated moderate severity and 20.5% in-hospital mortality. Cluster β, dominated by Escherichia coli, showed the lowest mortality (10.3%) and clinical severity. Cluster γ, enriched with multidrug-resistant organisms (e.g., Klebsiella pneumoniae and Pseudomonas aeruginosa), had a high mortality (26.3%) and more severe clinical manifestations. Cluster δ, marked by Acinetobacter baumannii and Candida glabrata, had the highest mortality (31.5%). External cohort validation confirmed the robustness of these four subtypes, supporting their clinical relevance.
CONCLUSION
The study highlights the role of pathogen compositions in IPN, revealing how specific microbial profiles influence prognosis. It provides a novel approach for pathogen-based risk stratification in IPN.
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.
