Key result
Machine learning predicts CTO in CAD patients with ~0.71 ROC using sex and key biomarkers.
Why the study?
Chronic total occlusion requires percutaneous coronary intervention, but there has been minimal research regarding CTO-specific risk factors and predictive models.
Can machine learning predictive models based on clinical characteristics accurately identify patients with chronic total occlusion before coronary angiography?
Observational (n=1,473)
Can machine learning predictive models based on clinical characteristics accurately identify patients with chronic total occlusion before coronary angiography?
Machine learning models utilizing routine clinical variables, particularly sex, neutrophil percentage, creatinine, and BNP, can predict the presence of chronic total occlusion with moderate accuracy prior to angiography.
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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Shi et al. (2023) conducted an observational in Chronic total occlusion (CTO) (n=1,473). Machine learning predictive models (PLS-DA, RF, SVM) was evaluated on Prediction of CTO development (ROC value). Machine learning models, particularly PLS-DA (ROC 0.706), predicted chronic total occlusion in CAD patients, identifying sex, neutrophil percentage, creatinine, and BNP as key risk factors.
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