Development and external validation of a model for post-endoscopic retrograde cholangiopancreatography pancreatitis

Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is a common complication in patients undergoing ERCP for choledocholithiasis, yet effective predictive models are lacking. This study included 2,247 patients who underwent ERCP for complete stone removal at the First Affiliated Hospital of USTC from January 2015 to January 2023. Six machine learning algorithms were utilized, incorporating 25 clinical parameters, to develop a predictive model for PEP risk. The random forest (RF) algorithm achieved the highest accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.947 in the internal dataset. Key risk factors for PEP identified include difficult cannulation, a history of pancreatitis, smaller common bile duct diameter, and female gender. Validation with datasets from 12 external centers showed AUC values ranging from 0.576 to 0.913, with an average of 0.768. An interactive R Shiny web application was also developed, offering a user-friendly tool for predicting PEP risk and enabling individualized management.

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Publication Details

Journal
iScience
Published
2025-05-02
DOI
https://doi.org/10.1016/j.isci.2025.112570
Citations
3
Primary Topic
Gallbladder and Bile Duct Disorders
Type
article
Field-Weighted Citation Impact
4.56
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article

Development and external validation of a model for post-endoscopic retrograde cholangiopancreatography pancreatitis

Jinxin Zheng, Shibo Xu, Mingming Song, Zhiyuan Jin et al.
3 citations
iScience
Gallbladder and Bile Duct Disorders
4.56
article

Development and external validation of a model for post-endoscopic retrograde cholangiopancreatography pancreatitis

Jinxin Zheng, Shibo Xu, Mingming Song, Zhiyuan Jin, Qiuyan Zhang, Song Qiao, Xiangkun He, Yue Gao, Chao-Qun Yan, Gang Hu, Wen Li, Qingfeng Sun, Gang Wang, Xiuhua Zhang, Yue Chen, Peng Xu, Cheng Wang, Xiangyu He, Xiaosi Hu, Zhenyu Feng, Haizheng Tang, Hai Zhu, Yajun Wei
article en
3 citations

Abstract

Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is a common complication in patients undergoing ERCP for choledocholithiasis, yet effective predictive models are lacking. This study included 2,247 patients who underwent ERCP for complete stone removal at the First Affiliated Hospital of USTC from January 2015 to January 2023. Six machine learning algorithms were utilized, incorporating 25 clinical parameters, to develop a predictive model for PEP risk. The random forest (RF) algorithm achieved the highest accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.947 in the internal dataset. Key risk factors for PEP identified include difficult cannulation, a history of pancreatitis, smaller common bile duct diameter, and female gender. Validation with datasets from 12 external centers showed AUC values ranging from 0.576 to 0.913, with an average of 0.768. An interactive R Shiny web application was also developed, offering a user-friendly tool for predicting PEP risk and enabling individualized management.

iScienceVol. 28(6)
Shanghai Jiao Tong University (CN), Northern Jiangsu People's Hospital (CN), Second Affiliated Hospital of Nanjing Medical University (CN), First Affiliated Hospital of Bengbu Medical College (CN), Yangzhou University (CN)
Good health and well-being
Openalex Percentile: Top 6%
Gallbladder and Bile Duct Disorders
4.56
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