通过人工智能支持的心电图识别和冠状动脉疾病风险分层
Samir Awasthi1,2, Nikhil Sachdeva1,2, Yash Gupta1,2
1Anumana, Inc, One Main Street, Cambridge, MA, USA.
EClinicalMedicine
|December 18, 2023
概括
一个人工智能工具 (ECG-AI) 增强了超越传统方法的动脉样硬化心血管疾病 (ASCVD) 风险预测. 这种人工智能工具改善了冠状动脉疾病 (CAD) 和心肌梗塞 (MI) 的风险分层,提供了更准确的评估.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 公共卫生 公共卫生
背景情况:
- 动脉样性心血管疾病 (ASCVD) 是全球主要的死亡原因,主要是由冠状动脉疾病 (CAD) 驱动的.
- 目前的ASCVD风险估计器,如聚合队列方程 (PCE),在风险分层和初级预防方面准确度低于最佳.
- 需要改进的方法来准确评估ASCVD风险,特别是在PCE可能低估或高估风险的个体中.
研究的目的:
- 开发和验证基于人工智能的心电图分析工具 (ECG-AI) 来检测CAD.
- 评估ECG-AI在ASCVD风险分层中的附加值,与PCE相比.
- 评估ECG-AI用于识别CAD的特定指标的实用性,例如冠状动脉 (CAC) 和先前心肌梗塞 (MI).
主要方法:
- 利用来自70多个医疗机构的700多万名患者的深度电子健康记录数据.
- 开发了使用神经网络的独立ECG-AI模型,以识别CAC分数≥300,阻塞性CAD和区域左心室秋,表明先前的MI.
- 评估了ECG-AI在以前没有ASCVD的患者的回顾性观察研究中的表现,将其风险分层与PCE进行了比较.
主要成果:
- 在确定CAC≥300 (AUROC 0.88),阻塞性CAD (AUROC 0.85) 和区域性秋 (AUROC 0.94) 方面,ECG-AI表现出高准确度.
- 一组ECG-AI模型独立和附加地预测了急性冠状动脉事件和死亡的3,5年和10年风险.
- 与零阳性模型相比,对1,2,或3种特定疾病的ECG-AI模型呈阳性患者的3年急性冠状动脉事件风险显著增加 (HR分别为2.41,4.23,11.75).
结论:
- 电脑心电图-人工智能显示出增强ASCVD风险分层的巨大潜力,解决当前方法的局限性.
- 该工具为低于最佳PCE估计值的患者提供了可访问的风险评估,并用于在较短的时间框架 (<10年) 上进行风险评估.
- ECG-AI可以为现有的风险计算器提供有价值的附加信息,改善对心血管事件高风险个体的识别.
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