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根据冠状动脉计算机断层扫描血管图谱的斑块负担,重血管化与医疗治疗的结果

Sarah Bär^1,2, Juhani Knuuti^1,3, Antti Saraste^1,4

¹Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.

European heart journal. Cardiovascular Imaging

|December 29, 2025

概括

冠状动脉计算机断层扫描血管学 (CCTA) 斑块负担可以识别受益于再血管化的患者. 较高的每血管斑块负担 (≥22%) 表明,重血管化可以降低死亡,心肌梗塞或不稳定性胸痛的风险.

科学领域:

心血管成像 - 心血管成像
干预心脏病学干预心脏病学
人工智能在医学中的应用

背景情况:

冠状动脉疾病 (CAD) 的管理依赖于评估斑块负担和缺血.
冠状动脉计算机断层扫描血管学 (CCTA) 提供了详细的斑块可视化.
识别受益于再血管化的患者仍然是一个临床挑战.

研究的目的:

为了确定CCTA衍生的斑块负担是否可以识别受益于再血管化的患者.
为了研究斑块负担,再血管化和不良心血管事件之间的关系.

主要方法:

一项队列研究,包括接受CCTA和15O-水PET的患者.
人工智能引导的定量CT (AI-QCT) 量化了每位患者和每位血管的百分比动脉瘤体积 (PAV).
考克斯回归分析评估了PAV,再血管化和结果 (死亡,心脏病发作,UAP) 之间的相互作用.

主要成果:

在患者层面 (p=0.042) 和血管层面 (p=0.026) 之间发现了重血管化和PAV之间的显著相互作用.
在每血管PAV≥22% (HR0.50,p=0.024) 的患者中,重血管化与较低的事件率有关.
患者层面的PAV分析显示了非显著的趋势.

结论:

来自CCTA的斑块负担,特别是每血管的PAV≥22%,可以识别受益于再血管化的患者.
除了医疗疗法外,再血管化还可以减少长期不良事件,在某些具有显著斑块负担的患者中减少长期不良事件.
对PAV的AI-QCT量化可能会改善CAD中的风险分层.

关键词:

人工智能是一种人工智能.冠状动脉计算机断层扫描血管图谱斑块的负担斑块负担重新血管化的过程.

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