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Predictive models for post-ERCP pancreatitis: a systematic review and meta-analysis.

Zhihang Zhong^1,2, Li Liu³, Jia Liu¹

¹Department of Gastroenterology, Southwest Hospital, Army Medical University, Chongqing, China.

Frontiers in Gastroenterology (Lausanne, Switzerland)

|March 23, 2026

Summary

Predictive models for post-ERCP pancreatitis (PEP) show promise but need improvement. Further validation and bias reduction are crucial for clinical use in identifying high-risk patients.

Keywords:

AUC Post-ERCP pancreatitis meta-analysis model performance predictive models

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Area of Science:

Gastroenterology and Hepatology
Medical Informatics
Biostatistics

Background:

Post-ERCP pancreatitis (PEP) is a significant complication of ERCP, incurring substantial clinical and economic burdens.
Predictive models aim to identify patients at high risk for PEP, enabling targeted preventive strategies.
Existing PEP predictive models exhibit variable performance, necessitating a comprehensive evaluation of their accuracy, reliability, and risk of bias.

Purpose of the Study:

To systematically evaluate the accuracy, reliability, and risk of bias of existing predictive models for post-ERCP pancreatitis (PEP).
To assess the performance of different types of predictive models, including machine learning and logistic regression, for PEP.
To identify common predictive factors associated with PEP development.

Main Methods:

A systematic literature search was conducted across five major databases up to January 2025.
Studies developing or validating PEP predictive models were included; external validation studies were meta-analyzed.
Model performance was assessed using AUC, sensitivity, specificity, and calibration, with risk of bias evaluated using CHARMS and PROBAST tools.

Main Results:

Twenty-three studies identified 21 PEP predictive models; nine models had external validation.
Externally validated models achieved a pooled AUC of 0.79, with machine learning models (AUC 0.84) outperforming logistic regression models (AUC 0.76).
Difficult cannulation, female sex, pancreatic duct dilation, and prior pancreatitis history were common predictors.

Conclusions:

Predictive models for PEP offer potential for improved patient risk stratification.
Variability in performance, limited external validation, and significant bias in many studies hinder clinical applicability.
Further external validation, model refinement, and enhanced bias control are essential for widespread clinical implementation of PEP predictive models.