从使用深度学习的ECG在社区预测心房动:一项跨国研究
Luisa C C Brant1,2, Antônio H Ribeiro3, Oseiwe B Eromosele4
1Faculty of Medicine & Hospital das Clínicas/EBSERH, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil (L.C.C.B., S.M.B., A.L.P.R.).
使用心电图数据的深度神经网络模型有效预测心房动 (AF) 风险和心血管结果. 将这种ECG-AF模型与CHARGE-AF得分相结合,可以提高不同人群的预测准确性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 心房动 (AF) 构成严重的健康风险.
- 现有的AF风险评分有其局限性.
- 多种数据集对于验证预测模型至关重要.
研究的目的:
- 改进和验证深度神经网络模型 (ECG-AF) 以使用心电图数据预测AF风险.
- 将ECG-AF模型的性能与已建立的CHARGE-AF风险评分进行比较.
- 评估ECG-AF模型与其他心血管结果的关联.
主要方法:
- 该ECG-AF模型是使用60%的Framingham心脏研究 (FHS) 样本开发的.
- 用FHS,英国生物库和ELSA-巴西队列的曲线下面面积 (AUC) 来评估模型性能.
- 考克斯的比例危险模型被用来评估与心血管结果的关联.
主要成果:
- 在FHS中,ECG-AF模型显示了发生性AF (AUC,0.82) 的中度歧视,与CHARGE-AF (AUC,0.83) 相比.
- 结合ECG-AF和CHARGE-AF在FHS和其他队列中改善了AF预测 (AUC,0.85).
- 较高的ECG-AF得分与心力衰竭,心肌梗塞,中风和死亡率的风险增加有关.
结论:
- 单输入ECG-AF深度神经网络模型在预测AF和心血管结果方面表现强.
- 该模型可与多变量临床风险得分进行比较,并且在组合时提供更好的预测.
- 这种人工智能驱动的方法有望提高不同人群的心血管风险评估.
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