Background Endoscopic retrograde cholangiopancreatography (ERCP) is a cornerstone of minimally invasive diagnosis and treatment for pancreaticobiliary diseases. However, post-ERCP pancreatitis (PEP), the most common complication, occurs with a relatively high incidence and can lead to organ failure or even death in severe cases. The effectiveness of existing preventive measures is highly dependent on accurate risk stratification, while empirical clinical judgment lacks consistency, underscoring the urgent need for objective and individualized predictive tools. Objective To identify independent risk factors for PEP and to construct a nomogram prediction model for individualized risk assessment in patients undergoing ERCP. Methods Clinical data from 289 patients who underwent ERCP at Jinzhong First People’s Hospital between January 2020 and October 2025 were retrospectively analyzed. Patients were divided into PEP and non-PEP groups according to the consensus diagnostic criteria. Univariate analysis was performed to compare demographic characteristics, clinical factors, procedural details, and laboratory parameters between the two groups. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, followed by multivariate logistic regression analysis to identify independent risk factors for PEP. A nomogram prediction model was constructed based on these factors. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) in the training cohort (n = 202) and validation cohort (n = 87). Results The incidence of PEP in this cohort was 10.73% (31/289). Multivariate logistic regression analysis identified five independent risk factors for PEP: sphincter of Oddi dysfunction, history of pancreatitis, pancreatic duct opacification, difficult cannulation, and guidewire access to the pancreatic duct (≥ 3 times). Elevated total bilirubin (TBil) level was identified as an independent protective factor against PEP. The nomogram incorporating these factors demonstrated excellent predictive performance, with AUC values of 0.922 (95%CI: 0.865–0.980) in the training cohort and 0.915 (95%CI: 0.878–0.951) in the validation cohort. Calibration curves showed good agreement between predicted probabilities and actual observations (Hosmer-Lemeshow test P > 0.05), and DCA confirmed the model’s clinical utility across a wide range of threshold probabilities. Conclusion The nomogram model constructed in this study integrates six easily accessible clinical and operational factors, provides an effective and reliable tool for individualized prediction of PEP risk. This model can assist clinicians in early identification of high-risk patients, facilitating targeted preventive interventions and potentially improving the safety of ERCP procedures.

Predictive factors and nomogram for post-endoscopic retrograde cholangiopancreatography pancreatitis

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2026年7月2日

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Huaizhou Yuan,
Haiping Wang

摘要

Background

Endoscopic retrograde cholangiopancreatography (ERCP) is a cornerstone of minimally invasive diagnosis and treatment for pancreaticobiliary diseases. However, post-ERCP pancreatitis (PEP), the most common complication, occurs with a relatively high incidence and can lead to organ failure or even death in severe cases. The effectiveness of existing preventive measures is highly dependent on accurate risk stratification, while empirical clinical judgment lacks consistency, underscoring the urgent need for objective and individualized predictive tools.

Objective

To identify independent risk factors for PEP and to construct a nomogram prediction model for individualized risk assessment in patients undergoing ERCP.

Methods

Clinical data from 289 patients who underwent ERCP at Jinzhong First People’s Hospital between January 2020 and October 2025 were retrospectively analyzed. Patients were divided into PEP and non-PEP groups according to the consensus diagnostic criteria. Univariate analysis was performed to compare demographic characteristics, clinical factors, procedural details, and laboratory parameters between the two groups. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, followed by multivariate logistic regression analysis to identify independent risk factors for PEP. A nomogram prediction model was constructed based on these factors. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) in the training cohort (n = 202) and validation cohort (n = 87).

Results

The incidence of PEP in this cohort was 10.73% (31/289). Multivariate logistic regression analysis identified five independent risk factors for PEP: sphincter of Oddi dysfunction, history of pancreatitis, pancreatic duct opacification, difficult cannulation, and guidewire access to the pancreatic duct (≥ 3 times). Elevated total bilirubin (TBil) level was identified as an independent protective factor against PEP. The nomogram incorporating these factors demonstrated excellent predictive performance, with AUC values of 0.922 (95%CI: 0.865–0.980) in the training cohort and 0.915 (95%CI: 0.878–0.951) in the validation cohort. Calibration curves showed good agreement between predicted probabilities and actual observations (Hosmer-Lemeshow test P > 0.05), and DCA confirmed the model’s clinical utility across a wide range of threshold probabilities.

Conclusion

The nomogram model constructed in this study integrates six easily accessible clinical and operational factors, provides an effective and reliable tool for individualized prediction of PEP risk. This model can assist clinicians in early identification of high-risk patients, facilitating targeted preventive interventions and potentially improving the safety of ERCP procedures.