Copyright
©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Risk prediction of biliary infection after endoscopic drainage for malignant perihilar biliary obstruction: A 10-year multicenter retrospective study
Yi-Fei Wang, Ke Han, Na An, Ya-Nan Sun, Feng Gao, Yong Sun, Di Zhang, Zhi-Feng Zhao, Qing Guo, Jiang-Ning Gu, Zhuo Yang
Yi-Fei Wang, Ke Han, Na An, Ya-Nan Sun, Feng Gao, Yong Sun, Di Zhang, Jiang-Ning Gu, Zhuo Yang, Department of Endoscopy, General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
Di Zhang, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
Zhi-Feng Zhao, Department of Gastroenterology, The Fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning Province, China
Qing Guo, Surgical Endoscopy Center, The Northeast International Hospital, Shenyang 110015, Liaoning Province, China
Co-first authors: Yi-Fei Wang and Ke Han.
Co-corresponding authors: Jiang-Ning Gu and Zhuo Yang.
Author contributions: Wang YF and Han K conceived and designed the study, drafted the manuscript, and approved the final version for publication; Wang YF contributed to data curation, formal analysis, and data visualization; An N, Sun YN, and Gao F validated the results and revised the manuscript; Sun Y, Zhang D, Zhao ZF, and Guo Q contributed to the methodology and manuscript editing; Gu JN and Yang Z played key roles in the experimental design, data interpretation, manuscript preparation, and project supervision as co-corresponding authors; All authors reviewed and approved the final version and agree to be accountable for the integrity of the work. Wang YF and Han K made substantial contributions to the work and are designated as co-first authors.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of the General Hospital of Northern Theater Command [Approval No. Y (2024) 336].
Informed consent statement: This retrospective study used existing clinical data and was approved by the Ethics Committee, with informed consent waived.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Data sharing statement: No additional data are available.
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/ Corresponding author: Zhuo Yang, MD, Chief Physician, Professor, Department of Endoscopy, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenhe District, Shenyang 110016, Liaoning Province, China.
yangzhuocy@163.com
Received: August 18, 2025
Revised: September 16, 2025
Accepted: October 28, 2025
Published online: December 21, 2025
Processing time: 124 Days and 3.8 Hours
BACKGROUND
As a prominent drainage method, endoscopic retrograde cholangiopancreatography (ERCP) with stenting has been universally employed to treat malignant perihilar biliary obstruction (MPHBO). Nonetheless, postoperative biliary infection (PBI) constitutes a remarkable complication associated with this procedure, which can result in fatal outcomes under some circumstances.
AIM
To investigate the risk factors and predict the occurrence of PBI following ERCP drainage in patients suffering from MPHBO.
METHODS
This retrospective study analyzed data from patients who underwent ERCP drainage at three different centers. Independent risk factors for PBI were identified by adopting multivariate analyses. Logistic regression model and artificial neural network (ANN) models were developed and validated to predict PBI.
RESULTS
A total of 288 patients who underwent 403 ERCP procedures were included in the study. The incidence of PBI was 39% (158/403). As evidently demonstrated by multivariate analysis, the Bismuth-Corlett classification (odds ratio [OR] = 1.412; 95% confidence interval [CI]: 1.144-1.743; P = 0.001), hypokalemia (OR = 4.080; 95%CI: 1.958-8.505; P < 0.001), and aspartate transaminase (AST) (OR = 1.003; 95%CI: 1.000-1.006; P = 0.021) were independent risk factors for PBI. Simultaneously, both a logistic regression model (area under the curve [AUC] = 0.734) and an ANN model (AUC = 0.867) were developed by adopting these factors. As suggested by a validation through 45 additional cases, the ANN model demonstrated an AUC of 0.940, surpassing the logistic regression model’s AUC of 0.791.
CONCLUSION
The Bismuth-Corlett classification, hypokalemia, and AST levels were identified as independent risk factors for PBI following ERCP drainage. The ANN model was proven to be an effective approach for the anticipation of the PBI occurrence.
Core Tip: This study established logistic regression and artificial neural network (ANN) models to preoperatively predict postoperative biliary infection—a serious complication worsening surgical outcomes and short-term prognosis in patients with malignant perihilar biliary obstruction undergoing endoscopic retrograde cholangiopancreatography drainage. Multivariate logistic regression identified key preoperative risk factors, notably hypokalemia, Bismuth-Corlett classification, and elevated aspartate transaminase levels. The ANN model demonstrated markedly superior predictive performance compared with logistic regression. These models offer clinicians a practical tool for early identification of high-risk patients, enabling timely, targeted interventions to mitigate infection-related complications and improve postoperative outcomes.
