Pericoronary fat: MACE risk from non-contrast CT and the role of iodine perfusion in contrast CT

冠状动脉周围脂肪：非造影 CT 的 MACE 风险以及造影 CT 中碘灌注的作用

• 批准号: 10577558
• 负责人: Sanjay Rajagopalan
• 金额: $ 78.61万
• 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: 2023-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10577558
• 关键词: Adipose tissue; Advocate; Agatston Score; Angiography; Appearance; Archives; Arterial Fatty Streak; Arteries; Artificial Intelligence; Atherosclerosis; Big Data; Biological; Biology; Biomedical Engineering; Blood flow; Calcium; Cardiac; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Cells; Clinical; Coronary; Coronary Arteriosclerosis; Coronary Artery Risk Development in Young Adults Study; Coronary artery; Detection; Diabetes Mellitus; Disease; Early Diagnosis; Engineering; Ensure; Event; Fatty acid glycerol esters; Future; Gender; Genes; Goals; Health; Image; Individual; Inflammation; Inflammatory; Infusion procedures; Institution; Iodine; Knowledge; Location; Machine Learning; Measures; Medicine; Methods; Modeling; Myocardial Infarction; Oncology; Pathway interactions; Patients; Perfusion; Phenotype; Physicians; Psoriasis; Race; Reproducibility; Research; Risk; Risk Factors; Role; Scanning; Scientist; Services; Severities; Site; Texture; Time; Tissues; University Hospitals; Validation; Vascular Diseases; X-Ray Computed Tomography; attenuation; calcification; cardiometabolic risk; cardiovascular disorder risk; cardiovascular health; cohort; comorbidity; contrast enhanced computed tomography; cost; deep learning; diverse data; evidence base; experimental study; health disparity; high risk; image processing; improved; indexing; meetings; mortality; multiple datasets; personalized medicine; programs; radiomics; research study; risk prediction; screening; success; theories; tool; vasa vasorum

Pericoronary fat: MACE risk from non-contrast CT and the role of iodine perfusion in contrast CT Summary Pericoronary adipose tissue (PCAT) inflammation is an important, emerging concept in coronary artery dis- ease, giving rise to the “outside-in” theory where inflammatory cells within PCAT, delivered by the vasa- vasorum, influence atherosclerosis plaque progression. Using cardiovascular CT images, we will use ad- vanced image processing and AI to better understand pericoronary fat appearance and to predict major ad- verse cardiovascular events (MACE). As cardiovascular disease remains the most common cause of death in the US, improved early detection, disease prediction, and patient management will positively impact health for many individuals. Using cardiac CT imaging (angiography, CCTA; perfusion, CCTP; and calcium score, CTCS) in elegant experiments and analyses, we will elucidate pericoronary fat assessments and create a new, inex- pensive CTCS assessment of pericoronary fat suitable for screening. As the principal pericoronary fat inflam- mation feature in CCTA is elevated HU, we will use CCTP to assess pericoronary fat perfusion and clarify the role of iodine on existing CCTA signatures, including confounds due to varying filling rates with obstructive dis- ease. Using paired images, we will associate CTCS pericoronary fat features to established ones from CCTA. Using appropriate pericoronary fat features from CTCS exams, we will predict major adverse cardiac events (MACE) without the iodine confound and combine with Agatston to get an even better prediction. Large CTCS cohorts enable interesting research studies. For example, using the serial Coronary Artery Risk Development in Young Adults (CARDIA) study, we will determine if pericoronary fat features precede the appearance of cal- cifications, giving credence to the “outside-in” theory. The CTCS exam is inexpensive (≤$99) at many institu- tions. At University Hospitals (UH), our nationally acknowledged free CTCS program currently servicing >13,000 patients/year with an archive of >65,000 cases, will provide an opportunity for big data, machine/deep learning analysis of PCAT. In addition, improved MACE prediction from PCAT plus Agatston will enable multi- ple future studies on health disparities, genes, cardiometabolic risk, co-morbidities (e.g., diabetes and psoria- sis), and cardio-oncology. To accomplish goals, we have assembled a world class team of biomedical engi- neers and physician scientists at University Hospitals/CWRU with deep knowledge of cardiovascular CT imag- ing and the biology of atherosclerosis. This proposal will ultimately advance the field of predictive medicine and propel new ways to pre-emptively detect at-risk patients so that they can be placed on evidence-based thera- pies.

冠状动脉周围脂肪：非造影 CT 的 MACE 风险以及造影 CT 中碘灌注的作用 概括 冠状动脉周围脂肪组织（PCAT）炎症是冠状动脉疾病中一个重要的新兴概念。 轻松，产生了“由外向内”的理论，其中 PCAT 内的炎症细胞由血管输送 使用心血管 CT 图像，我们将使用 ad- 先进的图像处理和人工智能可以更好地了解冠状动脉周围脂肪的外观并预测主要广告 与心血管事件（MACE）相反，心血管疾病仍然是最常见的死亡原因。 在美国，改进的早期检测、疾病预测和患者管理将对人类健康产生积极影响 使用心脏 CT 成像（血管造影，CCTA；灌注，CCTP；和钙评分，CTCS） 在优雅的实验和分析中，我们将阐明冠状动脉周围脂肪评估并创建一个新的、独立的 适合筛查冠状动脉周围脂肪的 CTCS 评估 作为主要的冠状动脉周围脂肪炎症。 CCTA 的特征是 HU 升高，我们将使用 CCTP 来评估冠状动脉周围脂肪灌注并阐明 碘对现有 CCTA 特征的作用，包括由于阻塞性障碍的填充率不同而造成的混淆 使用配对图像，我们将 CTCS 冠状动脉周围脂肪特征与 CCTA 中已建立的特征相关联。 利用 CTCS 检查中适当的冠状动脉周围脂肪特征，我们将预测主要的不良心脏事件 (MACE) 没有碘混淆并与 Agatston 结合以获得更好的大型 CTCS 预测。 队列可以进行有趣的研究，例如，使用系列冠状动脉风险开发。 在年轻人（CARDIA）研究中，我们将确定冠状动脉周围脂肪特征是否先于钙的出现 CTCS 考试在许多机构都很便宜（≤ 99 美元）。 在大学医院 (UH)，我们全国认可的免费 CTCS 计划目前正在提供服务。 每年超过 13,000 名患者，档案超过 65,000 例，将为大数据、机器/深度学习提供机会 PCAT 的学习分析此外，PCAT 加上 Agatston 的改进 MACE 预测将实现多重分析。 未来关于健康差异、基因、心脏代谢风险、合并症（例如糖尿病和牛皮癣）的研究 为了实现目标，我们组建了世界一流的生物医学工程团队。 大学医院/CWRU 的研究人员和医师科学家对心血管 CT 成像有深入的了解 该提案最终将推动预测医学和动脉粥样硬化领域的发展。 推动新方法先发制人地检测高危患者，以便他们能够接受基于证据的治疗 馅饼。