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Background <p>Endoscopic retrograde cholangiopancreatography (ERCP) is a common procedure for treating biliary and pancreatic disorders; however, it is associated with significant complications, notably post-ERCP pancreatitis (PEP), which has an incidence ranging from 3.5 to 9.7%. Objective: This study aims to develop machine learning-based predictive models for PEP in patients receiving prophylactic non-steroidal anti-inflammatory drugs (NSAIDs).</p> Methods <p>We retrospectively analyzed the data from 991 patients who underwent ERCP between January 2018 and February 2024. A mixed sampling technique was employed to address data imbalance. Predictive models were constructed using logistic regression, light gradient boosting machine, multi-layer perceptron, random forest, and extreme gradient boosting. The top-performing models were combined to enhance predictive accuracy. Model evaluation was conducted using a prospective cohort of 124 patients as an independent internal test set.</p> Results <p>Among the patients, 57 (5.75%) developed PEP. The models identified significant pre-procedure and post-procedure risk factors. The fusion model demonstrated an area under the curve of 0.84 for pre-procedure predictions and 0.86 for post-procedure predictions. High specificity and negative predictive values were noted, indicating potential clinical utility.</p> Conclusion <p>This study presents a novel fusion model for predicting PEP in NSAID-treated patients, offering improved tools for risk stratification and clinical decision-making. Future research should focus on multi-center validation and the integration of additional predictive factors to enhance model robustness and applicability.</p> Graphical abstract <p></p>

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Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Wenjun Ping,
  • Peng Cao,
  • Jiangtao Li,
  • Bibo Zhao,
  • Yunpeng Lin,
  • Cheng Liu,
  • Huiqing Wang,
  • Guodong Chen

摘要

Background

Endoscopic retrograde cholangiopancreatography (ERCP) is a common procedure for treating biliary and pancreatic disorders; however, it is associated with significant complications, notably post-ERCP pancreatitis (PEP), which has an incidence ranging from 3.5 to 9.7%. Objective: This study aims to develop machine learning-based predictive models for PEP in patients receiving prophylactic non-steroidal anti-inflammatory drugs (NSAIDs).

Methods

We retrospectively analyzed the data from 991 patients who underwent ERCP between January 2018 and February 2024. A mixed sampling technique was employed to address data imbalance. Predictive models were constructed using logistic regression, light gradient boosting machine, multi-layer perceptron, random forest, and extreme gradient boosting. The top-performing models were combined to enhance predictive accuracy. Model evaluation was conducted using a prospective cohort of 124 patients as an independent internal test set.

Results

Among the patients, 57 (5.75%) developed PEP. The models identified significant pre-procedure and post-procedure risk factors. The fusion model demonstrated an area under the curve of 0.84 for pre-procedure predictions and 0.86 for post-procedure predictions. High specificity and negative predictive values were noted, indicating potential clinical utility.

Conclusion

This study presents a novel fusion model for predicting PEP in NSAID-treated patients, offering improved tools for risk stratification and clinical decision-making. Future research should focus on multi-center validation and the integration of additional predictive factors to enhance model robustness and applicability.

Graphical abstract