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Pediatric cardiac surgery: machine learning models for postoperative complication prediction
Rémi Florquin1,2, Renaud Florquin3, Denis Schmartz4
1Department of Anesthesiology, CHU Charleroi, Chaussée de Bruxelles 140, 6042, Lodelinsart, Belgium. remi.florquin@gmail.com.
Insights
Machine learning models can predict complications in pediatric cardiac surgery patients. Logistic regression showed the highest accuracy, aiding anesthesiologists in risk assessment and decision-making.
Area of Science:
- Anesthesiology
- Machine Learning
- Pediatric Cardiac Surgery
Background:
- Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) is challenging.
- Machine learning (ML) tools offer potential for improved risk recognition and complication prediction.
Purpose of the Study:
- Develop effective ML prediction models for high-risk pediatric cardiac surgery patients.
- Create a user-friendly, comprehensive model for anesthesiologists.
Main Methods:
- Evaluated six ML models (logistic regression to support vector machine).
- Utilized a dataset of 1364 subjects and 33 variables.
- Primary metrics: Area Under the Curve (AUC) and F1 score.
Main Results:
- Logistic regression model achieved the highest AUC (83.65%) and F1 score (0.7296).
- This model demonstrated balanced sensitivity (77.94%) and specificity (76.47%).
- A three-layer decision tree model showed comparable sensitivity (79.41%) with a 72.84% AUC.
Conclusions:
- ML-assisted tools enhance predictive capabilities beyond traditional scoring methods.
- These tools support anesthesiologists in making informed decisions.
- Feasibility of a practical white-box model demonstrated; clinical validation is the next step.
Purpose:
Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a significant challenge for anesthesiologists. Machine Learning (ML)-assisted tools have the potential to enhance the recognition of patients at risk of complications and predict potential issues, ultimately improving outcomes.
Methods:
We evaluated the prediction capacity of six models, ranging from logistic regression to support vector machine, using a dataset comprising 33 variables and 1364 subjects. The Area Under the Curve (AUC) and the F1 score served as the primary evaluation metrics. Our primary objectives were twofold: first, to develop an effective prediction model, and second, to create a user-friendly comprehensive model for identifying high-risk patients.
Results:
The logistic regression model demonstrated the highest effectiveness, achieving an AUC of 83.65%, and an F1 score of 0.7296, with balanced sensitivity and specificity of 77.94% and 76.47%, respectively. In comparison, the comprehensive three-layer decision tree model achieved an AUC of 72.84%, with sensitivity (79.41%) comparable to more complex models.
Conclusion:
Our machine learning-assisted tools provide an additional perspective and enhance the predictive capabilities of traditional scoring methods. These tools can assist anesthesiologists in making well-informed decisions. Furthermore, we have successfully demonstrated the feasibility of creating a practical white-box model. The next steps involve conducting clinical validation and multicenter cross-validation.
Trial Registration:
NCT05537168.
