Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.
Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.
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DOI：
10.1161/circresaha.117.311312
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发表时间：
2017-10-13
影响因子：
20.1
通讯作者：
Lima JAC
中科院分区：
文献类型：
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作者：
Ambale-Venkatesh B;Yang X;Wu CO;Liu K;Hundley WG;McClelland R;Gomes AS;Folsom AR;Shea S;Guallar E;Bluemke DA;Lima JAC
机器学习可能有助于表征心血管风险，预测结果并在人群研究中识别生物标志物。测试随机生存森林（RF）（一种机器学习技术）与标准心血管风险评分相比预测六种心血管结局的能力。我们纳入了多种族动脉粥样硬化研究（梅萨）的参与者。基线测量用于预测12年随访期间的心血管结局。梅萨旨在研究亚临床疾病进展为心血管事件，其中参与者最初无CV疾病。来自梅萨的所有6814名参与者，年龄在45至84岁之间，来自4个种族，美国6个中心。从成像和非侵入性测试、问卷调查和生物标志物面板中获得735个变量。我们使用RF技术来确定每个结果的前20个预测因子。与传统的CV风险因素相比，影像学、心电图和血清生物标志物在前20名中占有重要地位。年龄是全因死亡率最重要的预测因素。空腹血糖水平和颈动脉超声检查是卒中的重要预测因素。冠状动脉钙化评分是冠心病和所有动脉粥样硬化性心血管疾病综合结局的最重要预测因子。左心室结构和功能以及心肌肌钙蛋白-T是心力衰竭事件的主要预测因素。肌张力、年龄和踝臂指数是房颤的最佳预测因素。组织坏死因子-α和白细胞介素-2可溶性受体以及N末端脑钠肽前体水平在所有结局中均很重要。RF技术的表现优于已建立的风险评分，预测准确性增加（Brier评分降低10-25%）。机器学习结合深度表型分析提高了最初无症状人群心血管事件预测的准确性。这些方法可能会导致更大的见解亚临床疾病标志物没有先验假设的因果关系。多种族动脉粥样硬化研究（梅萨）http://mesa-nhlbi.org/。NCT00005487
Machine learning may be useful to characterize cardiovascular risk, predict outcomes and identify biomarkers in population studies. To test the ability of random survival forests (RF), a machine learning technique, to predict six cardiovascular outcomes in comparison to standard cardiovascular risk scores. We included participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Baseline measurements were used to predict cardiovascular outcomes over 12 years of follow-up. MESA was designed to study progression of subclinical disease to cardiovascular events where participants were initially free of CV disease. All 6814 participants from MESA, aged 45 to 84 years, from 4 ethnicities, and 6 centers across USA were included. 735 variables from imaging and non-invasive tests, questionnaires and biomarker panels were obtained. We used the RF technique to identify the top 20 predictors of each outcome. Imaging, electrocardiography and serum biomarkers featured heavily on the top-20 lists as opposed to traditional CV risk factors. Age was the most important predictor for all-cause mortality. Fasting glucose levels and carotid ultrasonography measures were important predictors of stroke. Coronary artery calcium score was the most important predictor of coronary heart disease and all atherosclerotic cardiovascular disease combined outcomes. Left ventricular structure and function, and cardiac troponin-T were among the top predictors for incident heart failure. Creatinine, age and ankle brachial index were among the top predictors of atrial fibrillation. Tissue necrosis factor-α and interleukin-2 soluble receptors, and N-terminal pro-Brain Natriuretic Peptide levels were important across all outcomes. The RF technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 10–25%). Machine learning in conjunction with deep phenotyping improve prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. These methods may lead to greater insights regarding subclinical disease markers without apriori assumptions of causality. Multi-Ethnic Study of Atherosclerosis (MESA) http://mesa-nhlbi.org/. NCT00005487