Cardiovascular risk from comprehensive evaluation of the CT calcium score exam

CT钙评分检查综合评估心血管风险

• 批准号: 10853742
• 负责人: Sanjay Rajagopalan
• 金额: $ 76.76万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10853742
• 关键词: Adipose tissue; Agatston Score; Age Years; Aortic Valve Stenosis; Archives; Black Populations; Calcium; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Chronic Kidney Failure; Classification; Clinical Trials; Communities; Computer software; Coronary Arteriosclerosis; Detection; Developed Countries; Disease; Disease Management; Dose; Early Diagnosis; Early identification; Elderly; Evaluation; Event; Fatty acid glycerol esters; Female; Future; Genes; Goals; Grant; Health; Healthcare; Heart Valve Diseases; Heart failure; Hyperlipidemia; Hypertension; Image; Inflammatory; Institution; Intervention; Label; Life Style; Link; Machine Learning; Measurement; Medical; Methodology; Methods; Modeling; Monitor; Myocardium; Oncology; Paper; Parents; Patients; Persons; Pharmaceutical Preparations; Phenotype; Population; Preventive therapy; Process; Race; Reporting; Reproducibility; Research; Resolution; Risk; Risk Factors; Role; Scanning; Shapes; Socioeconomic Status; Spatial Distribution; Technology Transfer; Time; United States National Institutes of Health; University Hospitals; Work; X-Ray Computed Tomography; aging population; aortic valve; aortic valve disorder; aortic valve replacement; artificial intelligence method; calcification; cardiovascular risk factor; clinical risk; cohort; commercialization; comorbidity; coronary artery calcification; coronary artery calcium; cost; deep learning; density; detection method; equity, diversity, and inclusion; high resolution imaging; high risk; imaging modality; improved; innovation; interest; machine learning prediction; male; meetings; metabolomics; negative affect; novel; parent grant; personalized approach; personalized medicine; programs; research study; risk prediction; risk prediction model; screening; sex; side effect; socioeconomics; success; trend

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.

使用免费或低成本的 CT 钙评分前​​的 AI 预测主动脉瓣疾病及其在 MACE 中的作用 艾姆斯 NIH R01HL165218 母版的补充，“CT 钙化综合评估的心血管风险” 嗯分数考试” 概括 通过对主动脉瓣钙化的全面机器学习分析，我们将预测主动脉瓣钙化 早期识别有主动脉风险的患者。 瓣膜狭窄将改善患者的预防性管理（例如，监测、生活方式的改变，以及可能的治疗） 针对这种疾病的新兴药物），这通常会被忽视。此外，检测方法也可能会被忽视。 推动在疾病过程早期进行经导管主动脉瓣置换术 (TAVR) 的趋势 除了在主动脉瓣疾病、主动脉瓣钙化中的作用之外，还将能够进行医学治疗试验。 已被证明与 MACE 相关，改善 MACE 风险特征也将改善。 个性化患者管理。改进 MACE 风险预测和高风险现象识别。 类型，将有机会在需要指导的情况下指导精准的预防性治疗，因为 我们将使用大量 CT 钙档案来了解与其中一些疗法相关的成本和副​​作用。 考试成绩，包括来自克利夫兰大学医院的考试成绩，该机构是拥有最大的机构 免费 CT 钙评分计划（>100,000 名扫描患者，每年 >13,000 次扫描），提供独特的 创造新的个性化医疗保健方法的机会。 计划，包括主动脉瓣钙识别、钙特征和机器- 补充材料中的研究与家长补助金中的研究具有很强的协同作用。 除了临床风险预测外，我们还使用冠状动脉钙组学和脂肪组学特征来预测 MACE。 换句话说，我们的 CT 钙评分分析未来将与许多研究兴趣相吻合，包括 基因的作用、代谢组学、合并症（例如慢性肾病和高脂血症）、社会经济 由于存在与心血管风险相关的有趣的性别和种族影响。 当患有主动脉瓣狭窄时，我们将仔细确定这些变量在我们早期诊断风险中的作用 模型。