一种基于人工智能的冠状动脉计算机断层扫描血管学衍生性缺血症算法的预后价值,用于疑似冠状动脉疾病的患者
Sarah Bär1,2, Takeru Nabeta3, Teemu Maaniitty1,4
1Turku PET Centre, Turku University Hospital, University of Turku, Kiinamyllynkatu 4-8, 20520 Turku, Finland.
European heart journal. Cardiovascular Imaging
|December 12, 2023
概括
人工智能算法 (AI-QCTischaemia) 可以从冠状动脉CTA扫描中识别心肌缺血. 异常结果预测死亡,心肌梗塞或不稳定心痛风险增加两倍,特别是在非阻塞性冠状动脉疾病患者中.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 冠状动脉计算机断层扫描血管造影 (CTA) 对于诊断疑似冠状动脉疾病 (CAD) 至关重要.
- 准确的风险分层对于管理疑似CAD的患者至关重要.
- 一个新的AI引导的定量计算断层扫描缺血算法 (AI-QCTischaemia) 旨在直接从CTA图像中检测心肌缺血.
研究的目的:
- 为了评估AI-QCT血的预后价值,在症状患者中进行冠状动脉CTA,因为怀疑CAD.
- 在非阻塞性 (≤50%) 和阻塞性 (>50%) CAD患者中单独评估AI-QCT血的预后价值.
主要方法:
- 一项队列研究,涉及涉嫌CAD的患者接受冠状动脉CTA.
- AI-QCT血是由盲目的分析师计算的.
- 主要终点是死亡,心肌梗塞 (MI) 或不稳定的胸痛 (uAP) 的组合,随访时间中位数为6.9年.
主要成果:
- 在1880 (27%) 的患者中,有509人发现了异常的AI-QCT血结果.
- 患有异常AI-QCT血的患者具有显著更高的初级终点调整风险 (aHR 1.96;95% CI 1.46-2.63;P < 0.001).
- 这种关联在非阻塞性CAD患者 (aHR1.81;P = 0.022) 中显著,但在阻塞性CAD患者 (aHR 1.26;P = 0.386) 中没有显著的相互作用 (P = 0.032).
结论:
- 异常的AI-QCT血结果与怀疑CAD的患者长期不良心血管事件的调整后风险翻倍相关.
- AI-QCT血显示出改善风险分层的潜力,特别是在冠状动脉CTA上确定的非阻塞性CAD患者中.
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