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Prediction of post-operative acute pancreatitis in children with pancreaticobiliary maljunction using machine
Tian-Na Cai1, Shun-Gen Huang2, Yang Yang1
1Department of Radiology, Children's Hospital of Soochow University, Suzhou, 215025, China.
Insights
A new prediction model accurately identifies children at risk for post-operative acute pancreatitis (POAP) after pancreaticobiliary maljunction (PBM) surgery. Key risk factors include protein plugs, age, white blood cell count, and bile duct diameter.
Area of Science:
- Pediatric Surgery
- Gastroenterology
- Medical Informatics
Background:
- Pancreaticobiliary maljunction (PBM) is a congenital anomaly requiring surgical correction.
- Post-operative acute pancreatitis (POAP) is a significant complication following PBM surgery in children.
- Accurate risk stratification for POAP is crucial for patient management.
Purpose of the Study:
- To develop and validate a predictive model for identifying children at high risk of POAP.
- To analyze pre-operative patient variables associated with POAP development.
- To establish a tool for personalized risk assessment in pediatric PBM surgery.
Main Methods:
- Development of predictive models including Logistic Regression (LR), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost).
- Utilized prospectively collected data from pediatric PBM patients (August 2015 - August 2022).
- Model performance evaluated using Area Under the Curve (AUC), accuracy, sensitivity, and specificity; validated with nomogram and clinical impact curve.
Main Results:
- The study included 111 children with PBM, with 21 developing POAP.
- Logistic Regression (LR) model demonstrated superior performance in the validation dataset.
- The developed risk nomogram and clinical impact curve confirmed the LR model's high predictive accuracy.
Conclusions:
- A Logistic Regression (LR) based prediction model, integrated with a nomogram, effectively predicts POAP risk in pediatric PBM patients.
- Identified key predictive factors: protein plugs, patient age, white blood cell count, and common bile duct diameter.
- This model can aid in pre-operative risk stratification and clinical decision-making for PBM surgery.
Purpose:
This study aimed to develop a prediction model to identify risk factors for post-operative acute pancreatitis (POAP) in children with pancreaticobiliary maljunction (PBM) by pre-operative analysis of patient variables.
Methods:
Logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGBoost) models were established using the prospectively collected databases of patients with PBM undergoing surgery which was reviewed in the period comprised between August 2015 and August 2022, at the Children's Hospital of Soochow University. Primarily, the area beneath the receiver-operating curves (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. The model was finally validated using the nomogram and clinical impact curve.
Results:
In total, 111 children with PBM met the inclusion criteria, and 21 children suffered POAP. In the validation dataset, LR models showed the highest performance. The risk nomogram and clinical effect curve demonstrated that the LR model was highly predictive.
Conclusion:
The prediction model based on the LR with a nomogram could be used to predict the risk of POAP in patients with PBM. Protein plugs, age, white blood cell count, and common bile duct diameter were the most relevant contributing factors to the models.
