Predicting surgical intervention in pediatric intussusception using machine learning model
Saloua Ammar1,2, Imen Sellami2, Emna Krichen1
1Department of pediatric surgery, Hédi Chaker Hospital, Sfax, Tunisia.
Insights
A new model accurately predicts surgical treatment for pediatric intussusception. Key factors include symptom duration, bloody stools, and mass length, aiding pre-operative decisions.
Area of Science:
- Pediatric Surgery
- Medical Informatics
- Clinical Decision Support
Background:
- Intussusception is a common surgical emergency in young children.
- Accurate prediction of surgical intervention is crucial for timely management.
Purpose of the Study:
- To develop and validate a predictive model for surgical treatment of intussusception in children.
- To identify key clinical factors associated with the need for surgery.
Main Methods:
- Retrospective chart review of children under 3 years with ileocolic intussusception.
- Development of a predictive model using logistic regression and machine learning (Knime platform).
- Model validation using a separate dataset.
Main Results:
- The final model identified symptom duration, bloody stools, and intussusception length as independent predictors of surgical treatment.
- The machine learning model achieved 95% sensitivity and specificity.
- No significant differences were observed between training and validation sets.
Conclusions:
- The developed model can assist in pre-operative decision-making for pediatric intussusception.
- Further validation through larger, prospective, multicenter studies is recommended.
Aim:
To develop and validate a model predicting surgical treatment of intussusception in children.
Methods:
Design: Retrospective study of charts and development of a model for predicting surgical treatment using logistic regression and machine learning using''Knime'' platform.
Setting:
Data collection occurred in the Department of Pediatric Surgery between January 2013 and December 2022.
Patients:
Children aged less than 3 years old with the diagnosis of ileocolic intussusception.
Results:
One hundred and nine children were assigned to the training set, and 47 were assigned to the validation set. There were no significant differences between the two sets in clinical characteristics and surgical reduction. Surgical reduction was performed in 64 patients in the training set and 23 patients in the validation set (p=0.259). The univariate analysis showed that the duration of symptoms, mental state, palpable abdominal mass, bloody stools, elevated white blood cells, intraperitoneal effusion on ultrasound, and mass length were significantly associated with surgical treatment. After Logistic regression, bloody stools (p=0.033; OR=2.61), the duration of symptoms (p=0.028; OR=1.02), and the length of the intussusception (p=0.014; OR=1.265) were identified as independent risk factors for surgical treatment. The clinic-pathologic risk factors incorporated in the machine learning model were bloody stools, the duration of symptoms, and the length of the intussusception. This model was highly predictive, with a sensitivity and specificity of 95% for the SVM-derived model.
Conclusions:
This model may be applied to facilitate pre-surgery decisions for children with intussusception. Larger prospective multicenter studies are needed to validate the model.
