在使用基于人工智能的心电图的儿童中预测心血管不良事件
Yoshitsugu Nogimori1, Kaname Sato1, Koichi Takamizawa1
1Department of Pediatrics, The University of Tokyo Hospital, Japan.
International journal of cardiology
|April 5, 2024
概括
一种新的心电图 (ECG) 衍生标记,电心力衰竭指标 (EHFI),显示出确定儿科心力衰竭 (HF) 风险的前景. 这种基于CNN的新方法可能比传统方法更好地预测未来的心血管事件.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 卷积神经网络 (CNN) 用于通过ECG检测成人心力衰竭 (HF).
- 儿科HF评估因先天性心脏缺陷而复杂.
- 电图衍生的神经激素激活 (NHA) 可能表明儿科HF风险.
研究的目的:
- 开发和验证一个来自心电图的标志物,用于儿科HF.
- 评估标记物预测未来心血管事件的能力.
- 将新型标志物与已确定的生物标志物进行比较.
主要方法:
- 在21,378个儿科心电图上训练了CNN,其中包括B型尿素 (BNP) 和主要不良心血管事件 (MACE).
- 该模型的输出,即电心力衰竭指标 (EHFI),得到了验证.
- 在295名儿童的813个心电图中,比较了EHFI的MACE预测与BNP.
主要成果:
- 与BNP相比,EHFI表现出优异的MACE预测能力 (AUC为0.826对0.691,p=0.03).
- 欧洲高等金融机构显示,MACE的危险比率明显高于BNP.
- 对于MACE,EHFI的预测精度明显高于BNP,尤其是在早期.
结论:
- 与BNP相比,来自心电图的CNN提供了一种新的HF标记,与BNP相比具有明显的预后价值.
- 基于CNN的心电图分析为评估儿科HF提供了一种新的方法.
- 电心力衰竭指标 (EHFI) 显示了改善儿科患者风险分层的潜力.
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