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Machine Learning Model for Predicting Postoperative Complications in Pediatric Simple Congenital Heart Disease with
Chuli Shi1, Yuehang Yang1, Xinyi Liu2
1Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Insights
A machine learning model accurately predicts postoperative complications in pediatric simple congenital heart disease (CHD) patients after right vertical infra-axillary incision (RVIAI). This tool aids in targeted perioperative management for better patient outcomes.
Area of Science:
- Cardiology
- Medical Informatics
- Pediatric Surgery
Background:
- Pediatric simple congenital heart disease (CHD) patients undergoing right vertical infra-axillary incision (RVIAI) face risks of postoperative complications.
- Developing predictive tools is crucial for optimizing perioperative management in this population.
Purpose of the Study:
- To develop and validate a machine learning (ML) model for predicting postoperative complications in pediatric simple CHD patients.
- To identify independent risk factors associated with these complications.
Main Methods:
- A retrospective analysis of 638 pediatric patients undergoing RVIAI for simple CHD (ventricular septal defect and/or atrial septal defect).
- Construction and evaluation of 190 ML models using 35 preoperative and intraoperative variables.
- Selection of the optimal model based on the highest mean C-index and performance evaluation using ROC curve analysis.
Main Results:
- The optimal ML model, combining Elastic Net and Gradient Boosting Machine, identified 18 key predictors of postoperative complications.
- Significant predictors included defect size, globulin, activated partial thromboplastin time, red blood cell count, and blood urea nitrogen.
- The model demonstrated good predictive performance (AUC > 0.78) and identified high-risk patient groups with significantly higher complication rates.
Conclusions:
- The developed ML model serves as a valuable tool for predicting postoperative risk in pediatric simple CHD patients undergoing RVIAI.
- This predictive capability can facilitate more personalized and effective perioperative care strategies.
Background:
This study aimed to develop and validate a machine learning model to predict postoperative complications in pediatric simple congenital heart disease (CHD) patients undergoing right vertical infra-axillary incision (RVIAI).
Methods:
A retrospective dataset of 638 patients who underwent treatment for ventricular septal defect and/or atrial septal defect via RVIAI at our hospital between August 2020 and August 2023 was collected. A total of 35 preoperative and intraoperative variables were used to construct 190 machine learning models. The optimal model was selected based on the highest mean C-index. Independent risk factors identified by the optimal model were ranked according to their importance. Kaplan-Meier analysis was used to compare the incidence of postoperative complications between different risk groups. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC).
Results:
The optimal model, which combined Elastic Net (alpha = 0) and Gradient Boosting Machine, identified 18 baseline variables associated with postoperative complications. The top five predictors were defect size, globulin, activated partial thromboplastin time, red blood cell count, and blood urea nitrogen. Kaplan-Meier curves showed that postoperative complication rates were significantly higher in the high-risk group than in the low-risk group (p < 0.0001). The model demonstrated good discrimination, with area under the curve (AUC) values on postoperative days 5, 10, 15, and 20 remaining above 0.78 in both the training and test sets.
Conclusions:
This machine learning model provides a potential predictive tool for assessing postoperative risk in simple CHD patients undergoing RVIAI and may support more targeted perioperative management.
