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Machine learning-based analysis of risk factors for chronic total occlusion in an Asian population
Yuchen Shi1, Zichao Cheng1, Wen Jian1
1Center for Coronary Artery Disease (CCAD), Beijing Anzhen Hospital, Capital Medical University, and Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China.
Insights
Machine learning models can predict chronic total occlusion (CTO), a form of coronary artery disease. Key predictors include sex, neutrophil percentage, creatinine, and brain natriuretic peptide (BNP).
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Chronic total occlusion (CTO) is a significant challenge in coronary artery disease (CAD) management.
- Percutaneous coronary intervention is often required for CTO, but research on predictive models is limited.
- Identifying patients with CTO before angiography can optimize treatment strategies.
Purpose of the Study:
- To develop and validate machine learning models for predicting CTO based on clinical characteristics.
- To identify key clinical risk factors associated with CTO development.
Main Methods:
- Retrospective analysis of data from 1473 patients with CAD (317 CTO, 1156 non-CTO).
- Development of predictive models using Partial Least Squares Discriminant Analysis (PLS-DA), Random Forest (RF), and Support Vector Machine (SVM).
- Model performance evaluated using Receiver Operating Characteristic (ROC) curve analysis.
Main Results:
- All three models demonstrated comparable predictive performance (ROC values around 0.70).
- The PLS-DA model identified 10 variables, RF identified 42, and SVM identified 20.
- Sex, neutrophil percentage, creatinine, and brain natriuretic peptide (BNP) were consistently identified across all models.
Conclusions:
- Machine learning models, particularly PLS-DA, show promise in predicting CTO before coronary angiography.
- Clinical factors such as sex, neutrophil percentage, creatinine, and BNP are potential important risk factors for CTO.
Objectives:
Chronic total occlusion (CTO) is a form of coronary artery disease (CAD) requiring percutaneous coronary intervention. There has been minimal research regarding CTO-specific risk factors and predictive models. We developed machine learning predictive models based on clinical characteristics to identify patients with CTO before coronary angiography.
Methods:
Data from 1473 patients with CAD, including 317 patients with and 1156 patients without CTO, were retrospectively analyzed. Partial least squares discriminant analysis (PLS-DA), random forest (RF), and support vector machine (SVM) models were used to identify CTO-specific risk factors and predict CTO development. Receiver operating characteristic (ROC) curve analysis was performed for model validation.
Results:
For CTO prediction, the PLS-DA model included 10 variables; the ROC value was 0.706. The RF model included 42 variables; the ROC value was 0.702. The SVM model included 20 variables; the ROC value was 0.696. DeLong's test showed no difference among the three models. Four variables were present in all models: sex, neutrophil percentage, creatinine, and brain natriuretic peptide (BNP).
Conclusions:
Validation of machine learning prediction models for CTO revealed that the PLS-DA model had the best prediction performance. Sex, neutrophil percentage, creatinine, and BNP may be important risk factors for CTO development.
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