Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study

Yueli Dai; Chenyu Ouyang; Guanghua Luo; Yi Cao; Jianchun Peng; Anbo Gao; Hong Zhou

doi:10.7717/peerj.15797

Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study

PeerJ. 2023 Aug 3:11:e15797. doi: 10.7717/peerj.15797. eCollection 2023.

Authors

Yueli Dai^#¹, Chenyu Ouyang^#², Guanghua Luo², Yi Cao¹, Jianchun Peng¹, Anbo Gao^{3

4

5}, Hong Zhou²

Affiliations

¹ Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
² Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
³ Clinical Research Institute, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
⁴ Department of Cardiovascular Medicine, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
⁵ Key Laboratory of Heart Failure Prevention & Treatment of Hengyang, Clinical Medicine Research Center of Arteriosclerotic Disease of Hunan Province, Hengyang, Hunan, China.

^# Contributed equally.

Abstract

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.

Keywords: CAD; CAD-RADS; ML; Plasma fibrinogen; Prediction; Risk factor.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Coronary Angiography / methods
Coronary Artery Disease* / diagnostic imaging
Diabetes Mellitus*
Humans
Machine Learning
Retrospective Studies
Risk Factors

Grants and funding

This research was supported by the Guidance Project of Hengyang City (No. 202222035663), the Scientific Research Project of Hunan Provincial Health Commission of China (No.202109011222; No.202203013975), the Natural Science Foundation of Hunan Province (2022JJ40386), and the Scientifc Research Project of Hunan Provincial Department of Education (No. 21B0407). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.