Cardiovascular risk from comprehensive evaluation of the CT calcium score exam

应用免费或低成本CT钙化评分预测主动脉瓣病变及其在MACE中的作用 AMS 美国国立卫生研究院R01HL165218家长补充，“CT计算综合评估的心血管风险-- 嗯，分数考试“ 摘要 利用对主动脉瓣钙化的综合机器学习分析，我们将预测主动脉瓣应力。 早期和未来的主要不良心血管事件(MACE)。主动脉高危患者的早期识别 瓣膜狭窄将改善先发制人的患者管理(例如，监测、生活方式改变，以及可能的 新兴药物)治疗这种疾病，这种疾病往往不被注意到。此外，检测方法可以 促进在疾病过程中早期实施经导管主动脉瓣置换术(TAVR)的趋势 并将使医疗治疗试验成为可能。除了它们在主动脉瓣疾病中的作用外，主动脉瓣钙化- 已有研究表明，血管紧张素转换酶与Mace有关。对Mace风险特征的改进也将改善 个性化的患者管理。改进的MACE风险预测和高危现象识别 类型，将有机会指导精确的预防性治疗，在需要指导的地方，给出 与其中一些疗法相关的成本和副作用。我们将使用大量的CT钙质档案 分数考试，包括克利夫兰大学医院的考试，这是一所拥有最大 免费CT钙化评分计划(&gt；100,000名扫描患者，&gt；每年13,000次扫描)，提供独特的 为医疗保健创造新的个性化方法的机会。众多的技术创新是 计划，包括主动脉瓣钙识别的新方法，钙特征，和机器- 学习方法。附录中的研究与家长资助中的研究非常协同，其中 我们使用冠状动脉钙组学和脂肪组学特征来预测MACE。除了临床风险预案 我们的CT钙分分析将在未来与许多研究兴趣相衔接，包括 基因的作用、代谢组学、共病(如慢性肾脏疾病和高脂血症)、社会经济 状态，以及心血管风险的心脏肿瘤学。因为有有趣的性别和种族暗示- 对于患有主动脉瓣狭窄的ED患者，我们将仔细确定这些变量在早期诊断风险中的作用。 模特们。

AI prediction of aortic valve disease and its role in MACE using free, or low-cost, CT calcium score ex- ams Supplement to NIH R01HL165218 parent, “Cardiovascular risk from comprehensive evaluation of the CT calci- um score exam” Summary Using a comprehensive machine learning analysis of aortic valve calcifications, we will predict aortic valve ste- nosis and future major adverse cardiovascular events (MACE). Earlier identification of patients at risk of aortic valve stenosis will improve preemptive patient management (e.g., monitoring, lifestyle changes, and possibly emerging medications) for this disease, which can often go unnoticed. In addition, detection methods could enable the trend of performing transcatheter aortic valve replacement (TAVR) earlier in the disease process and will enable trials of medical treatments. Aside from their role in aortic valve disease, aortic valve calcifica- tions have been shown to be associated with MACE. Improved characterization of MACE risk will also improve personalized patient management. With improved MACE risk prediction and identification of high-risk pheno- types, 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. We will use large archives of CT calcium score exams, including those from the University Hospitals of Cleveland, which is an institution with the largest no-cost CT calcium scoring program (>100,000 scanned patients, >13,000 scans per year), providing a unique opportunity to create new personalized approaches for healthcare. Numerous technical innovations are planned, including novel methods for aortic valve calcium identification, calcium features, and machine- learning approaches. Research in the supplement is very synergistic with research in the parent grant, where we are predicting MACE using coronary calcium-omics and fat-omics features. In addition to clinical risk pre- diction, our CT calcium score analyses will dovetail in the future with many research interests, including the role of genes, metabolomics, co-morbidities (e.g., chronic kidney disease and hyperlipidemia), socio-economic status, and cardio-oncology on cardiovascular risk. As there are interesting sex and race implications associat- ed with aortic valve stenosis, we will carefully determine the role of these variables in our early diagnosis risk models.