Prediction of the severity of acute pancreatitis using machine learning models

ABSTRACT

Background

Acute pancreatitis (AP) is the most common pancreatic disease. Predicting the severity of AP is critical for making preventive decisions. However, the performance of existing scoring systems in predicting AP severity was not satisfactory. The purpose of this study was to develop predictive models for the severity of AP using machine learning (ML) algorithms and explore the important predictors that affected the prediction results.

Methods

The data of 441 patients in the Department of Gastroenterology in our hospital were analyzed retrospectively. The demographic data, blood routine and blood biochemical indexes, and the CTSI score were collected to develop five different ML predictive models to predict the severity of AP. The performance of the models was evaluated by the area under the receiver operating characteristic curve (AUC). The important predictors were determined by ranking the feature importance of the predictive factors.

Results

Compared to other ML models, the extreme gradient boosting model (XGBoost) showed better performance in predicting severe AP, with an AUC of 0.906, an accuracy of 0.902, a sensitivity of 0.700, a specificity of 0.961, and a F1 score of 0.764. Further analysis showed that the CTSI score, ALB, LDH, and NEUT were the important predictors of the severity of AP.

Conclusion

The results showed that the XGBoost algorithm can accurately predict the severity of AP, which can provide an assistance for the clinicians to identify severe AP at an early stage.

KEYWORDS:

• Acute pancreatitis
• machine learning
• extreme gradient boosting
• severity
• prediction
• predictors

Acknowledgments

The author thanked the Affiliated Hospital of Yangzhou University and all the authors of the original studies.

Disclosure of financial/other conflicts of interest

The authors have no relevant conflicts of interest to disclose. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

YZ, GTL, LHH and GHY designed this study; FH, XLS, JXZ, and GYL collected the data; CCY, JJP, and JXZ conducted formal analysis; YZ, CCY, and GYL developed machine learning models; YZ, FH, and XLS wrote the original draft; GTL, LHH, JJP, WMX, and GHY provided guidance and amendments; all authors approved the final manuscript.

Data availability statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/00325481.2022.2099193

Additional information

Funding

The authors received no financial support to report.