人工智能ASCVD风险分层使用心电图的开发和多国验证
medRxiv : the preprint server for health sciences
|January 16, 2026
概括
一个新的ECG-ASCVD工具使用心电图 (ECGs) 预测动脉样硬化心血管疾病 (ASCVD) 风险,改善了缺乏传统数据的患者的风险评估. 这使得针对高风险个体进行有针对性的查.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 预防医学 预防医学
背景情况:
- 动脉样硬化心血管疾病 (ASCVD) 的临床风险得分受到缺少预测数据的限制.
- 电心电图 (ECG) 是广泛可用的,但在风险预测方面未得到充分利用.
- 需要可扩展的ASCVD风险评估工具.
研究的目的:
- 开发和验证ECG-ASCVD,一种使用ECG的风险预测范式,以识别患ASCVD风险的个体.
- 评估基于ECG的不同模型 (ECG-ASCVD-12,ECG-ASCVD-IMAGE,ECG-ASCVD-1) 的性能.
- 模拟ECG-ASCVD的临床实用性,用于有针对性的风险因素评估.
主要方法:
- 使用来自耶鲁纽黑文卫生系统 (YNNHS),ELSA-巴西 (ELSA) 和英国生物银行 (UKB) 的数据开发和验证ECG-ASCVD模型.
- 从12导电图谱信号,ECG图像和1导电信号预测到ASCVD的时间.
- 使用C指数和危险比率评估模型性能,并对10万名成年人进行部署模拟.
主要成果:
- 通过ECG-ASCVD-12,在验证队列中表现出可概括的歧视 (C指数:0.684-0.746).
- 在对传统分数进行调整后,模型仍然与ASCVD风险独立相关.
- 模拟部署表明ECG-ASCVD可以识别缺乏现有得分数据的高风险患者.
结论:
- 一个ECG-ASCVD工具包被开发和验证在不同的跨国队列.
- 静止心电图信息具有预测ASCVD风险的巨大潜力.
- 通过ECG-ASCVD,可以对心血管疾病进行更有针对性的查和风险因素评估.
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