Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT Angiography

通过 CT 血管造影自动定量冠脉斑块和冠周脂肪组织来综合预测心血管事件

• 批准号: 10595673
• 负责人: Damini Dey
• 金额: $ 69.52万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10595673
• 关键词: Adipose tissue; Algorithms; American; Angiography; Arterial Fatty Streak; Arteries; Artificial Intelligence; Atherosclerosis; Cardiac Death; Cardiovascular system; Cause of Death; Cessation of life; Characteristics; Clinical; Clinical Data; Clinical Trials; Clinical assessments; Complex; Consumption; Coronary; Coronary Arteriosclerosis; Coronary Stenosis; Coronary artery; Data; Deposition; Event; Future; Hospitals; Hour; Imaging Techniques; Individual; Language; Longterm Follow-up; Machine Learning; Manuals; Measurement; Measures; Medical center; Myocardial Infarction; Patient risk; Patient-Focused Outcomes; Patients; Physicians; Prognosis; Registries; Reproducibility; Research; Research Personnel; Risk; Rupture; Scanning; Severities; Site; Standardization; Stenosis; Symptoms; Testing; Time; Trees; Validation; Visual; Work; acute coronary syndrome; arm; cardiovascular risk factor; clinically significant; computerized; coronary calcium scoring; coronary computed tomography angiography; coronary event; coronary plaque; density; experience; follow-up; heart imaging; high risk; high risk population; image processing; improved; indexing; individual patient; machine learning method; machine learning prediction; mortality; noninvasive diagnosis; novel; patient registry; prognostic significance; prognostic value; prospective; risk prediction

PROJECT SUMMARY Coronary artery disease remains the leading cause of death worldwide, and more than half of the individuals suffering myocardial infarction (heart attacks) have no premonitory symptoms. Studies of patients with coronary artery disease have traditionally focused only on the severity of narrowing (stenosis) of the coronary arteries by atherosclerotic plaques, rather than the adverse features of coronary plaques which are predisposed to rupture and precipitate myocardial infarction. Coronary CT Angiography (CTA) is a noninvasive test that allows assessment of both coronary stenosis and plaque characteristics. Currently, however, CTA is interpreted visually for stenosis. Quantitative measurements of CTA stenosis severity and plaque features are not part of current clinical routine. We propose to develop novel image processing algorithms for fully automated, robust quantification of coronary plaque features from CTA. We also propose to automatically quantify the characteristics of adipose tissue around the coronary arteries (pericoronary adipose tissue, PCAT), which have been shown to differentiate rupture-prone, high-risk coronary plaques from stable ones. We propose to apply machine learning methods to efficiently combine stenosis, plaque and PCAT features, along with patient clinical data, into a new integrated risk score for the prediction of future adverse cardiovascular events. We will evaluate this risk score in the real-world, prospective, landmark SCOT-HEART trial (including all 2073 patients in the CTA arm of the trial), with added external validation in large multicenter patient registries, with available CTA scans, clinical data, and followup for cardiovascular events (fatal and non-fatal myocardial infarction and cardiovascular death in a grand total of 7844 patients). We propose three specific aims: 1) To refine, expand and automate measurements of coronary plaque and lumen for the entire coronary artery tree, and to standardize measurement of plaque changes in serial CTA; 2) To evaluate the prognostic value of automatically-quantified plaque features and PCAT characteristics for the prediction of future MACE in the prospective SCOT-HEART trial and multicenter CTA registries; 3) To develop and evaluate with full external validation a new automated patient risk score—combining patient clinical data, CTA-measured quantitative plaque features and PCAT characteristics, using machine learning—for the prediction of future MACE events in the prospective SCOT-HEART trial and multicenter CTA registries. The proposed work will enable automated, multi-faceted and reproducible analysis of plaque, stenosis and PCAT from CTA, combined with objective risk scores reflecting likelihood of adverse cardiovascular events. This work will provide a novel, personalized, real-world paradigm that objectively and accurately identifies individual patients at risk of future cardiovascular events, from routine CTA imaging.

项目摘要 冠状动脉疾病仍然是全球死亡的主要原因，超过一半的人 遭受心肌梗塞（心脏病发作）没有前症状。患者的研究 传统上，冠状动脉疾病仅集中在冠状动脉狭窄（狭窄）的严重程度上 动脉粥样硬化斑块的动脉，而不是冠状动脉斑块的不利特征 冠状动脉血管造影（CTA）是无创的 允许评估冠状动脉狭窄和斑块特征的测试。但是，目前CTA是 在视觉上解释为狭窄。 CTA狭窄严重程度和斑块特征的定量测量是 不是当前临床常规的一部分。 我们建议开发新的图像处理算法，以完全自动化，可靠的数量 CTA的冠状斑块特征。我们还建议自动量化脂肪的特征 周围的组织周围的冠状动脉（周围脂肪组织，PCAT），已显示为 区分易于破裂的高风险冠状斑与稳定的斑块。我们建议使用机器 有效地结合狭窄，斑块和PCAT特征的学习方法以及患者的临床数据 作为预测未来不良心血管事件的新综合风险评分。我们将评估这个 现实世界中的潜在，具有里程碑意义的苏格兰心脏试验中的风险评分（包括所有2073名患者 试验的CTA组），并在大型多中心患者注册表中增加了外部验证，可用CTA 扫描，临床数据和心血管事件的随访（致命和非致命心肌梗塞以及 总共7844名患者的心血管死亡）。我们提出了三个具体目标： 1）整个冠状动脉的冠状牙菌斑和管腔的完善，扩展和自动测量 树，并标准化串行CTA斑块变化的测量； 2）评估自动量化牌匾特征和PCAT特征的预后价值 预期的Scot-Heart试验和多中心CTA登记处的未来狼牙棒的预测； 3）通过全面的外部验证开发和评估新的自动化患者风险评分 - 结合 使用机器的患者临床数据，CTA测量的定量牌匾特征和PCAT特征 学习 - 预测未来的梅斯事件在潜在的Scot-Heart试验和多中心CTA中 登记处。 拟议的工作将使牙菌斑，狭窄和 来自CTA的PCAT，结合了反映不良心血管事件的可能性的客观风险评分。 这项工作将提供一个新颖，个性化的现实世界范式，客观，准确地识别 常规CTA成像的个体患者有未来心血管事件的风险。