Why the study?
The study aimed to evaluate multiple machine learning methods for predicting the association between cardiovascular risk factors and CAD-RADS scores.
Can machine learning algorithms accurately predict high CAD-RADS scores based on cardiovascular risk factors in patients with CAD?
Can machine learning algorithms accurately predict high CAD-RADS scores based on cardiovascular risk factors in patients with CAD?
Machine learning algorithms, particularly random forest, can accurately predict high CAD-RADS scores using clinical risk factors such as plasma fibrinogen, age, and diabetes.
Answers are grounded in this paper and cite their sources.
ML models accurately predict CAD-RADS from risk factors retrospectively; leaves open prospective validation before clinical use.
Objective This study aimed to investigate a variety of machine learning (ML) methods to predict the association between cardiovascular risk factors and coronary artery disease-reporting and data system (CAD-RADS) scores. Methods This is a retrospective cohort study. Demographical, cardiovascular risk factors and coronary CT angiography (CCTA) characteristics of the patients were obtained. Coronary artery disease (CAD) was evaluated using CAD-RADS score. The stenosis severity component of the CAD-RADS was stratified into two groups: CAD-RADS score 0-2 group and CAD-RADS score 3–5 group. CAD-RADS scores were predicted with random forest (RF), k-nearest neighbors (KNN), support vector machines (SVM), neural network (NN), decision tree classification (DTC) and linear discriminant analysis (LDA). Prediction sensitivity, specificity, accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors. Results A total of 442 CAD patients with CCTA examinations were included in this study. 234 (52.9%) subjects were CAD-RADS score 0–2 group and 208 (47.1%) were CAD-RADS score 3–5 group. CAD-RADS score 3-5 group had a high prevalence of hypertension (66.8%), hyperlipidemia (50%) and diabetes mellitus (DM) (35.1%). Age, systolic blood pressure (SBP), mean arterial pressure, pulse pressure, pulse pressure index, plasma fibrinogen, uric acid and blood urea nitrogen were significantly higher ( p < 0.001), and high-density lipoprotein (HDL-C) lower ( p < 0.001) in CAD-RADS score 3–5 group compared to the CAD-RADS score 0–2 group. Nineteen features were chosen to train the models. RF (AUC = 0.832) and LDA (AUC = 0.81) outperformed SVM (AUC = 0.772), NN (AUC = 0.773), DTC (AUC = 0.682), KNN (AUC = 0.707). Feature importance analysis indicated that plasma fibrinogen, age and DM contributed most to CAD-RADS scores. Conclusion ML algorithms are capable of predicting the correlation between cardiovascular risk factors and CAD-RADS scores with high accuracy.
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Dai et al. (2023) studied this question.
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