在鼻腔节律期间使用心电图对心房动进行人工智能引导的查:前性非随机干预试验
Peter A Noseworthy1, Zachi I Attia2, Emma M Behnken3
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
一个人工智能 (AI) 算法有效地指导了对心房的查,与通常护理相比,在高风险患者中显著提高了检测率. 这种人工智能方法有望提高心房动查效率.
科学领域:
- 心脏病学
- 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 之前的心房动查试验表明需要更有针对性的策略.
- 没有检测到的AF带来了严重的中风风险,需要改进诊断方法.
研究的目的:
- 评估人工智能 (AI) 算法引导的向查方法的有效性,以确定以前未被识别的心房动.
- 评估人工智能驱动的风险分层在AF查中的收益.
主要方法:
- 这是一项实用,非随机的干预试验,涉及患有中风风险因素且没有已知AF的患者.
- 使用AI算法对心电图 (ECG) 进行持续的门诊心律监测长达30天,以进行风险分层 (高风险与低风险).
- 倾向性得分与现实世界对照进行二次分析.
主要成果:
- 在高风险患者中有7. 6%和低风险患者中有1. 6%的患者检测到心房动.
- 与常规治疗相比,AI指导的查显著增加了AF检测率 (10. 6%对高风险组的3. 6%,p<0.0001).
- 人工智能算法与更高的AF检测率 (OR 4. 98, p=0.0002) 有显著的关联.
结论:
- 利用现有的临床数据进行人工智能引导的查方法可以提高心房的检测率.
- 这种人工智能驱动的策略有可能提高心房动查计划的整体有效性.
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