人工智能增强型心电图识别了正常射出小部分的患者,他们面临着更糟糕的结果的风险
Jwan A Naser1, Eunjung Lee1, Francisco Lopez-Jimenez1
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
人工智能 (AI) 的心电图 (ECG) 模型可以预测低射出分数 (EF). 尽管EF正常,但AI-ECG标记为虚假阳性患者的死亡率较高,特别是在心声学异常的情况下.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 基于人工智能 (AI) 的心电图 (ECG) 模型有助于识别患有低射出分数 (EF) 风险较高的患者.
- 由AI-ECG识别为虚假阳性 (FP) 的患者,表现出正常的EF但异常的AI-ECG得分,显示出更有可能在未来发展低EF.
研究的目的:
- 为了研究FP患者的心声回声学特征.
- 评估FP状态与全因死亡风险之间的关联.
主要方法:
- 根据AI-ECG得分和EF测量,回顾性地将患者分为真阴性 (TN),FP,假阴性 (FN) 和真阳性 (TP) 的分类.
- 详细分析心声学参数,包括左心室功能,门疾病,肺压力和右心功能.
- 考克斯回归模型的应用,以确定与全因死亡率相关的因素.
主要成果:
- 在100,586名患者中,有7%被归类为FP. FP患者表现出比TN患者更多的心声学异常 (97%的FP患者有异常).
- 与TN患者相比,FP患者面临的全因死亡风险增加 (HR:1.64),平均随访时间为2.7年.
- 异常心声图的FP患者的死亡风险明显高于正常心声图和TN患者的患者.
结论:
- 人工智能-心电图模型可以识别正常EF的患者,这些患者有潜在的心电图异常.
- 肌痛性肺炎患者,特别是那些同时出现心声学异常的患者,死亡风险较高.
- 结合AI-ECG和回声心电图,可增强左心室喷射分数 (LVEF) 正常的患者的风险分层.
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