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Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis
Wenjun Ping1, Peng Cao1, Jiangtao Li1
1The First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Surgical Endoscopy
|June 15, 2026
Summary
Machine learning models can predict post-ERCP pancreatitis (PEP) in patients taking NSAIDs. A novel fusion model shows high accuracy, aiding clinical decisions for this common ERCP complication.
Area of Science:
- Gastroenterology and Hepatology
- Medical Informatics
- Artificial Intelligence in Medicine
Background:
- Endoscopic retrograde cholangiopancreatography (ERCP) is a frequent procedure for biliary and pancreatic conditions.
- Post-ERCP pancreatitis (PEP) is a significant complication, occurring in 3.5–9.7% of cases.
Purpose of the Study:
- To develop machine learning models predicting PEP risk in patients receiving prophylactic non-steroidal anti-inflammatory drugs (NSAIDs).
Main Methods:
- Retrospective analysis of 991 patients undergoing ERCP (Jan 2018–Feb 2024).
- Utilized logistic regression, LightGBM, MLP, Random Forest, and XGBoost models.
- Employed mixed sampling for data imbalance and model fusion for enhanced accuracy.
- Validated models on a prospective cohort of 124 patients.
Main Results:
- PEP developed in 5.75% (57/991) of patients.
- Identified key pre- and post-procedure risk factors for PEP.
- The fusion model achieved AUCs of 0.84 (pre-procedure) and 0.86 (post-procedure).
- Demonstrated high specificity and negative predictive values.
Conclusions:
- A novel fusion model effectively predicts PEP in NSAID-treated patients undergoing ERCP.
- The model offers improved risk stratification and clinical decision support.
- Further multi-center validation and inclusion of additional factors are recommended.
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