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A Predictive Model for Secondary Posttonsillectomy Hemorrhage in Pediatric Patients: An 8-Year Retrospective Study
Yuting Ge1, Wenchuan Chang1, Lixiao Xie1
1Department of Otolaryngology Children's Hospital of Soochow University Suzhou Jiangsu China.
Insights
This study developed a machine learning model to predict secondary posttonsillectomy hemorrhage (PTH) in children. The XGBoost model accurately assesses the risk of this common complication, aiding clinical decision-making.
Area of Science:
- Pediatric Otolaryngology
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Posttonsillectomy hemorrhage (PTH) is a significant complication following pediatric tonsillectomy.
- Accurate prediction of secondary PTH is crucial for patient management.
- Existing tools for assessing PTH risk are limited.
Purpose of the Study:
- To develop and validate a predictive model for secondary PTH in pediatric patients.
- To identify key predictors of secondary PTH using machine learning algorithms.
Main Methods:
- Retrospective analysis of 492 pediatric patients undergoing tonsillectomy.
- Development of predictive models using logistic regression and seven machine learning algorithms.
- Validation using discrimination, calibration, and clinical utility metrics; SHAP for model interpretation.
Main Results:
- The XGBoost model demonstrated the best performance in predicting secondary PTH.
- The SHAP method provided feature importance and explanations for the XGBoost model's predictions.
Conclusions:
- A machine learning-based model, specifically XGBoost, can accurately predict secondary PTH in children.
- This model offers a valuable tool for clinicians to assess and manage PTH risk.
Objectives:
Posttonsillectomy hemorrhage (PTH) is a common and potentially life-threatening complication in pediatric tonsillectomy. Early identification and prediction of PTH are of great significance. Currently, there are very few tools available for clinicians to accurately assess the risk of PTH. This study aimed to develop and validate a predictive model for secondary PTH.
Methods:
A retrospective analysis was conducted on 492 individuals who underwent tonsillectomy or tonsillotomy in Children's Hospital of Soochow University from July 1st, 2015 to December 31th, 2023. The study population was randomly divided into the training set and the validation set at a ratio of 7:3. Univariate logistic regression analysis was used to screen features. Multivariate logistic regression and seven machine learning algorithms were used to construct predictive models. Discrimination, calibration, and clinical utility were used to compare the predictive performance. The SHapley Additive exPlanation (SHAP) method was used to interpret the results of the best-performing model.
Results:
One multivariate logistic regression model and seven machine learning models were constructed. The XGBoost model yielded the best performance in the validation set. The SHAP method ranked the features of the XGBoost model based on their importance and provided both global and local explanations of the model.
Conclusion:
This study established a machine learning-based predictive model for secondary PTH, which may enable clinicians to accurately assess the risk of secondary PTH in children.
Level Of Evidence:
4.
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