Abstract
Purpose
To develop a model to identify risk factors and predictors of acute pancreatitis in children with pancreaticobiliary maljunction (PBM).
Methods
We screened consecutive PBM patients treated at two centers between January, 2015 and July, 2021. For machine learning, the cohort was divided randomly at a 6:4 ratio to a training dataset and a validation dataset. Three parallel models were developed using logistic regression (LR), a support vector machine (SVM), and extreme gradient boosting (XGBoost), respectively. Model performance was judged primarily based on the area under the receiver operating curves (AUC).
Results
A total of 99 patients were included in the analysis, 17 of whom suffered acute pancreatitis and 82 did not. The XGBoost (AUC = 0.814) and SVM (AUC = 0.813) models produced similar performance in the validation dataset; both outperformed the LR model (AUC = 0.805). Based on the SHapley Additive exPlanation values, the most important variable in both the XGBoost and SVM models were age, protein plugs, and white blood cell count.
Conclusions
Machine learning models, especially XGBoost and SVM, could be used to predict acute pancreatitis in children with PBM. The most important contributing factor to the models were age, protein plugs, and white blood cell count.
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This work was supported by the National Natural Science Foundation of China (#81971685).
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The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study protocol was approved by the Ethics Committee of the Children’s Hospital of Soochow University and Xuzhou Children’s Hospital.
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Han, X., Geng, J., Zhang, Xx. et al. Using machine learning models to predict acute pancreatitis in children with pancreaticobiliary maljunction. Surg Today 53, 316–321 (2023). https://doi.org/10.1007/s00595-022-02571-y
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DOI: https://doi.org/10.1007/s00595-022-02571-y