AUTHOR=De Tianyu , Du Guohui , Yin Hongkun , Wang Hao , Wang Wei , Ma Tian , Ma Junbai , Wang Hao , Wang Qi 

TITLE=Development and validation of a practical prediction model for post-ERCP pancreatitis using machine learning

JOURNAL=Frontiers in Surgery

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/surgery/articles/10.3389/fsurg.2025.1628956

DOI=10.3389/fsurg.2025.1628956

ISSN=2296-875X

ABSTRACT=BackgroundPost-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is one most frequent and severe complication of ERCP. In consideration of recent advancements in both endoscopic and artificial intelligence research, it is possible to construct a practical risk prediction model to facilitate the identification of PEP patients at elevated risk.AimWe developed and validated a concise predictive model for post-ERCP pancreatitis risk with logistic regression (LR), LightGBM, Support Vector Machine (SVM), XGBoost, and Multilayer Perceptron (MLP) neural network models.MethodsWe selected 688 patients undergone ERCP to form the basic dataset, with 70% for training and 30% for validation. Subsequently, Stepwise Backward Selection Based on Logistic Regression was utilized to select pertinent clinical features, incorporating the machine learning (ML) models to construct the final predictive model. The efficacy of the model was evaluated by various metrics. These newly identified clinical features were then incorporated into a simplified, points-based risk scoring system for potential bedside application and further evaluation.ResultsBased on the collected data and the results of stepwise backward regression, we identified the following features as potentially significant clinical variables that influence the risk of post-ERCP pancreatitis: periampullary diverticulum, pancreatic stent placement, pancreatic guidewire passages, dilation of the extrahepatic bile duct, age, and coronary artery disease, and constructed a prediction model. Following this, several ML models were constructed to assess the performance of this model. All ML models demonstrated superior performance to conventional logistic regression (LR) models in terms of AUC curves, with XGBoost, SVM, LightGBM, and MLP models all achieving at least acceptable performance levels. Finally, we developed a simplified scoring system based on LightGBM model with an AUC of 0.75.ConclusionsWe developed and validated a concise predictive model for post-ERCP pancreatitis risk, and a simplified scoring system based on the LightGBM model. This model facilitates individual risk prediction and preventive strategy selection.