在疑似CAD患者中,CCTA衍生的冠状动脉斑块负担提供了比CAC评分更好的预后价值
Jorge Dahdal1,2, Ruurt A Jukema1,3, Teemu Maaniitty4,5
1Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
European heart journal. Cardiovascular Imaging
|March 25, 2025
概括
人工智能 (AI) 斑块分析,包括百分比动脉瘤体积 (PAV),与冠状动脉 (CAC) 评分相比,可以更好地预测心血管事件. 人工智能驱动的斑块表征提升了对心血管不良结果的风险分层.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 心脏病学中的预测分析.
背景情况:
- 冠状动脉 (CAC) 评分和冠状动脉计算机断层扫描血管造影 (CCTA) 是评估冠状动脉疾病 (CAD) 的确立方法.
- 来自CCTA的定量斑块指标可能会提供超越传统风险因素和CAC评分的增量预后价值.
- 人工智能 (AI) 工具正在出现,用于对心血管成像数据的自动化和全面分析.
研究的目的:
- 评估来自CCTA的AI衍生的定量斑块指标的预后效用,与预测不良心血管结果的CAC评分相比.
- 在风险分层中评估瘤体积百分比 (PAV) 和非化斑块体积百分比 (NCPV%) 的增量值.
- 确定基于人工智能的斑块表征是否可以改善对主要不良心血管事件 (MACE) 的预测.
主要方法:
- 一个潜在的队列,包括2404名疑似患有CAD的患者,接受了CAC评分和CCTA.
- 人工智能软件 (Cleerly, Inc) 量化了斑块指标,包括PAV和NCPV%.
- 考克斯比例危险模型被用来评估斑块指标,CAC和所有原因死亡率和非致命性心肌梗塞 (MI) 的复合终点之间的关联,经过调整以考虑临床因素.
主要成果:
- 在平均7.0年的随访期间,208名患者 (8.7%) 经历了主要终点.
- 与CAC评分 (AUC=0.706,P=0.016) 相比,AI衍生的PAV在复合终点 (AUC=0.729) 中表现出更高的区分能力.
- 对于MI预测,PAV (AUC=0.791) 和NCPV% (AUC=0.814) 显著超过了CAC (AUC=0.699),其中NCPV%显示了最高的准确性.
结论:
- 人工智能驱动的冠状动脉斑块负担评估显著提高了未来不良心血管事件的预后准确性.
- 使用基于人工智能的工具进行全面的斑块表征,特别是NCPV%,对于完善心血管风险分层策略至关重要.
- 这些发现支持将人工智能衍生的定量斑块分析纳入常规临床实践,以改善患者管理.
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