无阻塞性冠状动脉疾病患者的冠状动脉炎症与心血管事件
Coronary Inflammation and Cardiovascular Events in Patients Without Obstructive Coronary Artery Disease.
作者信息
Diau Jia Ling, Lange Richard A
机构信息
Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA.
Department of Internal Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA.
出版信息
Curr Cardiol Rep. 2025 Mar 7;27(1):68. doi: 10.1007/s11886-025-02221-y.
PURPOSE OF REVIEW
This review evaluates the role of vascular inflammation in patients who develop myocardial infarction with non-obstructive coronary arteries (MINOCA). It also introduces pericoronary adipose tissue (PCAT) and epicardial adipose tissue (EAT) as possible biomarkers for risk prediction in patients with non-obstructive coronary artery disease (CAD).
RECENT FINDINGS
PCAT and EAT contribute to the development and progression of coronary artery inflammation and plaque vulnerability. Coronary computed tomography angiography (CCTA) can detect localized areas of inflammation through changes in the attenuation values of PCAT and EAT. Attenuation values can be further integrated with traditional risk factors using artificial intelligence to generate risk scores that significantly enhance prognostic accuracy in patients with and without obstructive coronary artery disease. Assessing PCAT and EAT inflammation via CCTA and AI-driven risk algorithms enable precise risk prediction of MINOCA and major adverse coronary events (MACE) in patients with non-obstructive CAD.
综述目的
本综述评估血管炎症在发生非阻塞性冠状动脉心肌梗死（MINOCA）患者中的作用。它还介绍了冠状动脉周围脂肪组织（PCAT）和心外膜脂肪组织（EAT）作为非阻塞性冠状动脉疾病（CAD）患者风险预测的可能生物标志物。
最新发现
PCAT和EAT促成冠状动脉炎症和斑块易损性的发生与发展。冠状动脉计算机断层血管造影（CCTA）可通过PCAT和EAT衰减值的变化检测局部炎症区域。衰减值可使用人工智能与传统危险因素进一步整合，以生成风险评分，显著提高有或无阻塞性冠状动脉疾病患者的预后准确性。通过CCTA和人工智能驱动的风险算法评估PCAT和EAT炎症，能够对非阻塞性CAD患者的MINOCA和主要不良冠状动脉事件（MACE）进行精确的风险预测。
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