Key result
A synthetic minority oversampling technique-based XGBoost model predicted postoperative complications after paediatric cardiac surgery with an AUC of 0.82 and a sensitivity of 0.74.
Why the study?
The study was conducted to identify risk factors for postoperative complications following paediatric cardiac surgery, develop a predictive tool, and compare it with traditional congenital heart disease risk adjustment tools.
Does a synthetic minority oversampling technique-based XGBoost model improve the prediction of postoperative complications compared to traditional risk assessment tools in paediatric patients undergoing cardiac surgery with cardiopulmonary bypass?
Observational (n=2,308)
No
Does a synthetic minority oversampling technique-based XGBoost model improve the prediction of postoperative complications compared to traditional risk assessment tools in paediatric patients undergoing cardiac surgery with cardiopulmonary bypass?
A machine learning approach using SMOTE and XGBoost significantly improves the sensitivity of predicting postoperative complications in pediatric cardiac surgery compared to traditional risk scores.
May support ML-based risk stratification after paediatric cardiac surgery; leaves open prospective validation before clinical adoption.
OBJECTIVES: Our objectives were to identify the risk factors for postoperative complications after paediatric cardiac surgery, develop a tool for predicting postoperative complications and compare it with other risk adjustment tools of congenital heart disease. METHODS: A total of 2308 paediatric patients who had undergone cardiac surgeries with cardiopulmonary bypass support in a single centre were included in this study. A univariate analysis was performed to determine the association between perioperative variables and postoperative complications. Statistically significant variables were integrated into a synthetic minority oversampling technique-based XGBoost model which is an implementation of gradient boosted decision trees designed for speed and performance. The 7 traditional risk assessment tools used to generate the logistic regression model as the benchmark in the evaluation included the Aristotle Basic score and category, Risk Adjustment for Congenital Heart Surgery (RACHS-1), Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STS-EACTS) mortality score and category and STS morbidity score and category. RESULTS: Our XGBoost prediction model showed the best prediction performance (area under the receiver operating characteristic curve = 0.82) when compared with these risk adjustment models. However, all of these models exhibited a relatively lower sensitivity due to imbalanced classes. The sensitivity of our optimization approach (synthetic minority oversampling technique-based XGBoost) was 0.74, which was significantly higher than the average sensitivity of the traditional models of 0.26. Furthermore, the postoperative length of hospital stay, length of cardiac intensive care unit stay and length of mechanical ventilation duration were significantly increased for patients who experienced postoperative complications. CONCLUSIONS: Postoperative complications of paediatric cardiac surgery can be predicted based on perioperative data using our synthetic minority oversampling technique-based XGBoost model before deleterious outcomes ensue.
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Zeng et al. (2019) conducted an observational in paediatric cardiac surgery (n=2,308). Synthetic minority oversampling technique-based XGBoost model vs. Traditional risk assessment tools (e.g., Aristotle Basic score, RACHS-1, STS-EACTS) was evaluated on Prediction of postoperative complications (AUC) (AUC 0.82). A synthetic minority oversampling technique-based XGBoost model predicted postoperative complications after paediatric cardiac surgery with an AUC of 0.82 and a sensitivity of 0.74.
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