机器学习在预测 10 年冠心病和心血管疾病死亡方面增加了临床和 CAC 评估。
Machine Learning Adds to Clinical and CAC Assessments in Predicting 10-Year CHD and CVD Deaths.
机构信息
Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Tokyo, Japan; Los Angeles BioMedical Research Institute at Harbor UCLA Medical Center, Torrance, California, USA.
Department of Imaging (Division of Nuclear Medicine), Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; David Geffen School of Medicine at UCLA, Los Angeles, California, USA.
出版信息
JACC Cardiovasc Imaging. 2021 Mar;14(3):615-625. doi: 10.1016/j.jcmg.2020.08.024. Epub 2020 Oct 28.
OBJECTIVES
The aim of this study was to evaluate whether machine learning (ML) of noncontrast computed tomographic (CT) and clinical variables improves the prediction of atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) deaths compared with coronary artery calcium (CAC) Agatston scoring and clinical data.
BACKGROUND
The CAC score provides a measure of the global burden of coronary atherosclerosis, and its long-term prognostic utility has been consistently shown to have incremental value over clinical risk assessment. However, current approaches fail to integrate all available CT and clinical variables for comprehensive risk assessment.
METHODS
The study included data from 66,636 asymptomatic subjects (mean age 54 ± 11 years, 67% men) without established ASCVD undergoing CAC scanning and followed for cardiovascular disease (CVD) and CHD deaths at 10 years. Clinical risk assessment incorporated the ASCVD risk score. For ML, an ensemble boosting approach was used to fit a predictive classifier for outcomes, followed by automated feature selection using information gain ratio. The model-building process incorporated all available clinical and CT data, including the CAC score; the number, volume, and density of CAC plaques; and extracoronary scores; comprising a total of 77 variables. The overall proposed model (ML all) was evaluated using a 10-fold cross-validation framework on the population data and area under the curve (AUC) as metrics. The prediction performance was also compared with 2 traditional scores (ASCVD risk and CAC score) and 2 additional models that were trained using all the clinical data (ML clinical) and CT variables (ML CT).
RESULTS
The AUC by ML all (0.845) for predicting CVD death was superior compared with those obtained by ASCVD risk alone (0.821), CAC score alone (0.781), and ML CT alone (0.804) (p < 0.001 for all). Similarly, for predicting CHD death, AUC by ML all (0.860) was superior to the other analyses (0.835 for ASCVD risk, 0.816 for CAC, and 0.827 for ML CT; p < 0.001).
CONCLUSIONS
The comprehensive ML model was superior to ASCVD risk, CAC score, and an ML model fitted using CT variables alone in the prediction of both CVD and CHD death.
目的
本研究旨在评估与冠状动脉钙（CAC）Agatston 评分和临床数据相比，基于非对比计算机断层扫描（CT）和临床变量的机器学习（ML）是否能改善对动脉粥样硬化性心血管疾病（ASCVD）和冠心病（CHD）死亡的预测。
背景
CAC 评分可提供冠状动脉粥样硬化整体负担的衡量标准，其长期预后实用性已被一致证明具有优于临床风险评估的增量价值。然而，目前的方法未能整合所有可用的 CT 和临床变量以进行综合风险评估。
方法
本研究纳入了 66636 名无症状受试者（平均年龄 54±11 岁，67%为男性）的数据，这些受试者在接受 CAC 扫描时无 ASCVD，随访 10 年以评估心血管疾病（CVD）和 CHD 死亡情况。临床风险评估纳入了 ASCVD 风险评分。对于 ML，采用集成增强方法拟合预测分类器，然后使用信息增益比进行自动特征选择。模型构建过程纳入了所有可用的临床和 CT 数据，包括 CAC 评分、CAC 斑块的数量、体积和密度以及冠状动脉外评分，共计 77 个变量。总体建议模型（ML all）在人群数据上采用 10 折交叉验证框架进行评估，并以曲线下面积（AUC）作为指标。还比较了该预测性能与 2 个传统评分（ASCVD 风险和 CAC 评分）和 2 个基于所有临床数据（ML clinical）和 CT 变量（ML CT）训练的额外模型。
结果
ML all 预测 CVD 死亡的 AUC（0.845）优于 ASCVD 风险单独（0.821）、CAC 评分单独（0.781）和 ML CT 单独（0.804）的 AUC（p均<0.001）。同样，对于预测 CHD 死亡，ML all 的 AUC（0.860）也优于其他分析（ASCVD 风险为 0.835，CAC 为 0.816，ML CT 为 0.827；p均<0.001）。
结论
在预测 CVD 和 CHD 死亡方面，综合 ML 模型优于 ASCVD 风险、CAC 评分和仅基于 CT 变量拟合的 ML 模型。
Zotero 插件