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A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart
1Department of Cardiology, Peking University Third Hospital, National Health Commission Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing 100191, China.
Insights
Machine learning models, particularly XGBoost, can effectively predict cardiovascular events during exercise tests for coronary heart disease patients. This aids in managing patient safety and optimizing exercise evaluations.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Coronary heart disease (CHD) patients undergoing exercise evaluation require accurate risk assessment.
- Cardiovascular events during cardiopulmonary exercise testing (CPET) can occur, necessitating predictive models.
Purpose of the Study:
- To develop and optimize a machine learning (ML) model for predicting cardiovascular events during CPET in CHD patients.
- To identify key clinical and exercise parameters associated with these events.
Main Methods:
- Retrospective analysis of 16,645 CPETs in CHD patients (January 2016 - September 2019).
- Collected and analyzed pre-test clinical data and during-exercise data.
- Evaluated ML models including Support Vector Machine (SVM), logistic regression, Gradient Boosting Decision Tree (GBDT), and XGBoost.
Main Results:
- Cardiovascular events occurred in 3.0% (505) of CPETs; no deaths were reported.
- XGBoost demonstrated the highest predictive accuracy with an Area Under the Curve (AUC) of 0.794.
- Other models showed AUCs: SVM (0.686), logistic regression (0.778), and GBDT (0.784).
Conclusions:
- Machine learning, especially XGBoost, effectively predicts cardiovascular events during exercise evaluation in CHD patients.
- Associated factors include age, male sex, diabetes, myocardial infarction history, smoking, hyperlipidemia, hypertension, oxygen uptake, and ventilation efficiency.
Objective:
To develop and optimize a machine learning prediction model for cardiovascular events during exercise evaluation in patients with coronary heart disease (CHD).
Methods:
16,645 cases of cardiopulmonary exercise testing (CPET) conducted in patients with CHD from January 2016 to September 2019 were retrospectively included. Clinical data before testing and data during exercise were collected and analyzed.
Results:
Cardiovascular events occurred during 505 CPETs (3.0%). No death was reported. Predictive accuracy of the model was evaluated by area under the curve (AUC). AUCs for the SVM, logistic regression, GBDT and XGBoost were 0.686, 0.778, 0.784, and 0.794 respectively.
Conclusions:
Machine learning methods (especially XGBoost) can effectively predict cardiovascular events during exercise evaluation in CHD patients. Cardiovascular events were associated with age, male, diabetes and duration of diabetes, myocardial infarction history, smoking history, hyperlipidemia history, hypertension history, oxygen uptake, and ventilation efficiency indicators.
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