Cardiovascular risk from comprehensive evaluation of the CT calcium score exam

CT钙评分检查综合评价的心血管风险 总结 使用全面的机器学习分析冠状动脉钙化和胸部脂肪库， CT钙评分图像，我们将预测未来的主要不良心血管事件。改进的特性 心血管风险的确定将促进对心脏代谢疾病表型的了解，并支持临床 治疗决策和患者咨询以提高依从性。通过改进风险预测， 识别高风险表型，将有机会指导精确的预防性治疗， 鉴于其中一些疗法的成本和副作用，需要指导。阿加特- 钙石评分是未来主要不良心血管事件的主要预测因子，优于其他任何指标。 呃一个评价。心外膜脂肪体积和HU值是独立的危险因素。我们将联合收割机 以前所未有的方式在CT钙评分检查中分析脂肪和钙化。因为冠状动脉钙化- 结果，病理学观察和初步结果表明，检查其他功能（钙组学） 可以提高预测相比，全心脏阿加斯顿。我们的评估将以小，不稳定， 低密度钙化，比Agatston提供了更好的疾病脆弱性替代品，Agatston是数字， 主要是大的、可能稳定的钙化。除了脂肪量，我们还将研究定量 纹理和形状特征（脂肪组学）。HU值和组织纹理升高表明脂肪炎症- 是的。所有这些观察结果表明，组合脂肪组学和钙组学分析具有重要价值。我们 将使用来自不同地点的CT钙评分检查的大型档案，包括克里夫大学医院- land，这是一家拥有最大的免费CT钙评分项目（每年超过13，000次扫描）的机构。 这些大数据存储库提供了一个独特的机器学习机会。众多技术创新 包括新功能、数据表示和机器学习方法。除了 临床风险预测，我们的CT钙评分分析将在未来与许多研究兴趣相吻合， 包括基因、代谢组学、共病（例如，糖尿病和银屑病），社会经济地位， 和心血管肿瘤学对心血管风险的影响。

Cardiovascular risk from comprehensive evaluation of the CT calcium score exam Summary Using a comprehensive machine learning analysis of coronary artery calcifications and thoracic fat depots in CT calcium score images, we will predict future major adverse cardiovascular events. Improved characteriza- tion of cardiovascular risk will advance knowledge of cardiometabolic disease phenotypes and support clinical therapeutic decision-making and patient counseling for improved adherence. With improved risk prediction and identification of high-risk phenotypes, there will be an opportunity to guide precision preventive therapies, where guidance is needed given the cost and side effects associated with some of these therapies. The Agat- ston calcium score is the leading predictor of a future major adverse cardiovascular event, better than any oth- er single assessment. Epicardial fat volume and HU values are independent risk factors. We will combine analyses of fat and calcifications in CT calcium score exams in an unprecedented way. For coronary calcifica- tions, pathological observations and preliminary results suggest that examining other features (calcium-omics) can improve prediction as compared to whole-heart Agatston. Our assessments will characterize small, spotty, low-density calcifications, providing a better surrogate of disease vulnerability than Agatston, which is numeri- cally dominated by large, likely stable, calcifications. In addition to fat volumes, we will examine quantitative texture and shape features (fat-omics). Elevated HU values and tissue textures are indicative of fat inflamma- tion. All these observations suggest significant value in a combined fat-omics and calcium-omics analysis. We will use large archives of CT calcium score exams from different sites, including University Hospitals of Cleve- land, which is an institution with the largest no-charge CT calcium scoring program (>13,000 scans per year). These big data repositories provide a unique machine-learning opportunity. Numerous technical innovations are planned, including novel features, data representations, and machine learning approaches. In addition to clinical risk prediction, our CT calcium score analyses will dovetail in the future with many research interests, including the role of genes, metabolomics, co-morbidities (e.g., diabetes and psoriasis), socio-economic status, and cardio-oncology on cardiovascular risk.