Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving
Lulu Lin1, Li Ding1, Zhongguo Fu2
1Department of Neurology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Insights
Machine learning models can predict stroke risk in coronary artery disease patients after revascularization. The Catboost model demonstrated superior performance in identifying high-risk individuals for better patient outcomes.
Area of Science:
- Cardiovascular Medicine
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Coronary artery disease (CAD) patients undergoing revascularization face a significant risk of stroke.
- Accurate prediction of this risk is crucial for timely intervention and improved patient management.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting stroke risk in CAD patients post-revascularization.
- To identify the most effective model for clinical application.
Main Methods:
- A cohort of 5757 CAD patients from the MIMIC-IV database undergoing revascularization was analyzed.
- Data were split into training (n=4029) and testing (n=1728) sets.
- Pearson correlation and LASSO regression were used for feature selection, with model performance assessed using AUC, sensitivity, and specificity.
Main Results:
- The Catboost model achieved the highest predictive performance with an AUC of 0.831 in the training set and 0.760 in the testing set.
- The logistic regression model showed an AUC of 0.789 (training) and 0.731 (testing).
- The Catboost model's predictive value was significantly higher than logistic regression (P<0.05), with Charlson Comorbidity Index identified as a key predictor.
Conclusions:
- The Catboost model is optimal for predicting stroke risk in CAD patients post-revascularization.
- This model can serve as a valuable tool for early identification of high-risk patients, potentially reducing postoperative stroke incidence.
Background:
To construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods.
Methods:
In total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care IV (MIMIC-IV) were included in this cohort study. All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficient<9 were included, and the regression coefficients were set to 0. Features more closely related to the outcome were selected from the 10-fold cross-validation, and features with non-0 Coefficent were retained and included in the final model. The predictive values of the models were evaluated by sensitivity, specificity, area under the curve (AUC), accuracy, and 95% confidence interval (CI).
Results:
The Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811-0.851) in the training set, and 0.760 (95%CI: 0.722-0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764-0.814) in the training set and 0.731 (95%CI: 0.686-0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (P<0.05). Charlson Comorbidity Index (CCI) was the most important variable associated with the risk of stroke in CAD patients receiving coronary revascularization.
Conclusion:
The Catboost model was the optimal model for predicting the risk of stroke in CAD patients receiving coronary revascularization, which might provide a tool to quickly identify CAD patients who were at high risk of postoperative stroke.
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