Machine learning-based risk profile classification of patients undergoing elective heart valve surgery
Ulrich Bodenhofer1,2, Bettina Haslinger-Eisterer3, Alexander Minichmayer3
1School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg, Austria.
Summary
Machine learning accurately predicts patient risk for heart valve surgery, outperforming traditional scores. This advance improves patient counseling and complication avoidance in cardiac procedures.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Accurate patient risk assessment is crucial for elective cardiac surgery.
- Personalized risk prediction can enhance patient counseling and prevent complications.
- Traditional risk scores may not fully capture individual patient complexities.
Purpose of the Study:
- To evaluate the efficacy of modern machine learning methods for personalized risk prediction in elective heart valve surgery.
- To compare the performance of machine learning models against established risk scores like EuroSCORE.
Main Methods:
- A retrospective study of 2229 patients undergoing elective heart valve surgery.
- Utilized random forests, artificial neural networks, and support vector machines to predict 30-day mortality.
- 129 demographic and preoperative parameters were analyzed.
Main Results:
- The random forest model achieved an AUC of 0.839.
- This significantly outperformed the EuroSCORE (AUC = 0.704) and a EuroSCORE-based model (AUC = 0.745).
- The study cohort had a 30-day mortality rate of 3.86%.
Conclusions:
- Advanced machine learning models offer superior accuracy in predicting outcomes for valve surgery compared to logistic regression-based scores.
- This approach is adaptable for other high-risk interventions and institution-specific cohorts.
- Machine learning enhances the precision of risk stratification in cardiac surgery.
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