机器学习方法揭示的 CAD 患者高 CAD-RADS 评分的风险因素：一项回顾性研究。

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

机构信息

Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

出版信息

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

DOI:10.7717/peerj.15797

PMID:37551346

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10404399/

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 ( < 0.001), and high-density lipoprotein (HDL-C) lower ( < 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.

摘要

目的

本研究旨在探讨各种机器学习（ML）方法，以预测心血管危险因素与冠状动脉疾病报告和数据系统（CAD-RADS）评分之间的关联。

方法

这是一项回顾性队列研究。获取患者的人口统计学、心血管危险因素和冠状动脉 CT 血管造影（CCTA）特征。使用 CAD-RADS 评分评估冠状动脉疾病（CAD）。CAD-RADS 的狭窄严重程度组分分为两组：CAD-RADS 评分 0-2 组和 CAD-RADS 评分 3-5 组。使用随机森林（RF）、k-最近邻（KNN）、支持向量机（SVM）、神经网络（NN）、决策树分类（DTC）和线性判别分析（LDA）预测 CAD-RADS 评分。计算预测的灵敏度、特异性、准确性和曲线下面积（AUC）。利用特征重要性分析找出最重要的预测因子。

结果

本研究共纳入 442 例接受 CCTA 检查的 CAD 患者。234 例（52.9%）患者为 CAD-RADS 评分 0-2 组，208 例（47.1%）为 CAD-RADS 评分 3-5 组。CAD-RADS 评分 3-5 组高血压（66.8%）、高血脂（50%）和糖尿病（DM）（35.1%）患病率较高。年龄、收缩压（SBP）、平均动脉压、脉压、脉压指数、血浆纤维蛋白原、尿酸和血尿素氮显著升高（<0.001），高密度脂蛋白（HDL-C）显著降低（<0.001）（CAD-RADS 评分 3-5 组与 CAD-RADS 评分 0-2 组相比）。选择了 19 个特征来训练模型。RF（AUC=0.832）和 LDA（AUC=0.81）优于 SVM（AUC=0.772）、NN（AUC=0.773）、DTC（AUC=0.682）、KNN（AUC=0.707）。特征重要性分析表明，血浆纤维蛋白原、年龄和 DM 对 CAD-RADS 评分的贡献最大。