Abstract
Purpose
To develop a model to identify risk factors and predict recurrent cases of intussusception in children.
Methods
Consecutive cases and recurrent cases of intussusception in children from January 2016 to April 2022 were screened. The cohort was divided randomly at a 4:1 ratio to a training dataset and a validation dataset. Three parallel models were developed using extreme gradient boosting (XGBoost), logistic regression (LR), and support vector machine (SVM). Model performance was assessed by the area under the receiver operating characteristic curves (AUC).
Results
A total of 2469 cases of intussusception were included, where 225 were recurrent cases. The XGBoost (AUC = 0.718) models showed the best performance in the validation dataset, followed by the LR model (AUC = 0.652), while the SVM model (AUC = 0.613) performed worst among the three models. Based on the Shapley Additive exPlanation values, the most important variables in the XGBoost models were air enema pressure, mass size, age, duration of symptoms, and absence of vomiting.
Conclusions
Machine learning models, especially XGBoost, could be used to predict recurrent cases of intussusception in children. The most important contributing factors to the models are air enema pressure, mass size, age, duration of symptoms and absence of vomiting.
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Contributors Y-FQ designed the study; J-YG and Y-FQ collected data; J-YG analysed the data; J-YG and Y-FQ wrote and revised the manuscript; and all authors read and approved the final version of manuscript.
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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.
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Guo, Jy., Qian, Yf. Predicting recurrent cases of intussusception in children after air enema reduction with machine learning models. Pediatr Surg Int 39, 9 (2023). https://doi.org/10.1007/s00383-022-05309-6
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DOI: https://doi.org/10.1007/s00383-022-05309-6