深度学习了休息12导电心电图的表示,以预测在运动高峰时的运动
Shaan Khurshid1,2,3, Timothy W Churchill1,4, Nathaniel Diamant5
1Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA.
European journal of preventive cardiology
|October 5, 2023
概括
使用静止心电图的深度学习模型可以在没有心肺运动测试的情况下估计运动能力 (V ̇O2峰值). 这种方法使可扩展的心血管风险分层成为可能,并识别出具有较高不良健康结果风险的个体.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 运动生理学 运动生理学
背景情况:
- 心肺运动测试 (CPET) 是评估心肺健康 (V ̇O2峰) 的黄金标准.
- CPET是资源密集型的,限制其广泛的临床应用风险分层.
- 为了更广泛的风险评估,需要使用非侵入性方法来估计V ̇O2峰值.
研究的目的:
- 开发和验证深度学习模型,使用休息12导电心电图 (ECG) 来估计V ̇O2峰值.
- 评估来自心电图的V·O2峰值估计与发生心血管疾病和死亡风险之间的关联.
主要方法:
- 深度学习模型使用ECG数据训练了1891名接受CPET的个人.
- 模型的性能使用惩罚性线性回归来评估,比较基本的临床因素,标准的心电图参数和深度学习衍生的心电图变量.
- 对独立数据集进行了外部验证 (MGH测试,n=448;BWH测试,n=1076).
- 在一个大型初级保健队列 (n=84,718) 中分析了与发生疾病的关联.
主要成果:
- "深度心电图-V ̇O2"模型在估计V ̇O2峰值方面表现出很高的准确性,在验证队列中表现优于传统方法 (MGH测试:r=0.845,MAE=5.84;BWH测试:r=0.552,MAE=6.49).
- 当排除心力衰竭患者时,表现有所改善.
- 较低的估计V ̇O2峰值 (<14毫升/千克/分钟) 与心房动,心肌梗塞,心力衰竭和全因死亡率的风险增加显著相关.
结论:
- 静止12导电图的深度学习分析提供了一个可扩展和准确的方法来估计V ̇O2峰值.
- 这种人工智能驱动的方法可以促进大群体中有效的心血管风险分层.
- 基于心电图的V ̇O2峰值估计有望用于识别患有心血管不良事件和死亡风险较高的个体.
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