Rationale: Machine learning may be useful to characterize cardiovascular risk, predict outcomes, and identify biomarkers in population studies.

Objective: To test the ability of random survival forests, a machine learning technique, to predict 6 cardiovascular outcomes in comparison to standard cardiovascular risk scores.

Methods and Results: We included participants from the MESA (Multi-Ethnic Study of Atherosclerosis). 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 cardiovascular disease. All 6814 participants from MESA, aged 45 to 84 years, from 4 ethnicities, and 6 centers across the United States were included. Seven-hundred thirty-five variables from imaging and noninvasive tests, questionnaires, and biomarker panels were obtained. We used the random survival forests 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 cardiovascular 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. TNF-α (tissue necrosis factor-α) and IL (interleukin)-2 soluble receptors and NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) levels were important across all outcomes. The random survival forests technique performed better than established risk scores with increased prediction accuracy (decreased Brier score by 10%–25%).

Conclusions: Machine learning in conjunction with deep phenotyping improves prediction accuracy in cardiovascular event prediction in an initially asymptomatic population. These methods may lead to greater insights on subclinical disease markers without apriori assumptions of causality.

Clinical Trial Registration: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00005487.

理由：机器学习可能有助于表征心血管疾病风险，预测结果并在人群研究中识别生物标志物。

目的：测试随机生存森林（一种机器学习技术）与标准心血管风险评分相比预测6种心血管结果的能力。

方法和结果：我们纳入了来自MESA（动脉粥样硬化多民族研究）的参与者。基线测量用于预测12年随访中的心血管结局。MESA旨在研究亚临床疾病向心血管事件的进展，在这些事件中，参与者最初没有出现心血管疾病。来自美国4个种族和6个中心的来自MESA的所有6814名参与者（年龄在45至84岁之间）被包括在内。从成像和非侵入性测试，问卷调查表和生物标志物面板中获得了735个变量。我们使用随机生存森林技术来确定每种结果的前20个预测因子。影像，心电图和血清生物标志物在前20名列表中占据重要位置，与传统的心血管危险因素相反。年龄是全因死亡率的最重要预测因子。空腹血糖水平和颈动脉超声检查是卒中的重要预测指标。冠状动脉钙分数是冠心病和所有动脉粥样硬化性心血管疾病综合结果的最重要预测指标。左心室结构和功能以及心肌肌钙蛋白-T是发生心力衰竭的主要指标。肌酐，年龄和踝臂指数是房颤的主要预测指标。在所有结局中，TNF-α（组织坏死因子-α）和IL（白介素）-2可溶性受体和NT-proBNP（N末端Pro-B型利尿钠肽）水平都很重要。

结论：机器学习与深度表型结合可提高最初无症状人群心血管事件预测的预测准确性。这些方法可能会导致对亚临床疾病标记物有更深入的了解，而无需先验地假设因果关系。

临床试验注册： URL： http : //www.clinicaltrials.gov。唯一标识符：NCT00005487。