来自ECG的基于深度学习的NT-proBNP预测,用于社区风险评估
Meraj Neyazi1,2,3, Jan P Bremer1,2, Marius S Knorr1,2
1Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Clinical chemistry and laboratory medicine
|November 20, 2023
概括
深度学习模型可以从心电图中预测N-终端亲B型性尿素 (NT-proBNP) 水平,帮助识别心血管疾病风险. 这种方法对社区查和临床实践充满希望.
科学领域:
- 心脏病学 心脏病学
- 生物标志物 生物标志物
- 人工智能在医学中的应用
背景情况:
- N-终端亲B型尿素 (NT-proBNP) 是一种有价值的心血管疾病 (CVD) 风险生物标志物.
- 目前NT-proBNP查的局限性包括财务和运营障碍.
- 从心电图 (ECG) 预测NT-proBNP为更广泛的风险评估提供了潜在的解决方案.
研究的目的:
- 开发和验证一种深度学习模型,使用心电图数据预测血清NT-proBNP水平.
- 评估模型在识别患心血管疾病风险的个体方面的效率和可靠性.
- 评估ECG衍生的NT-proBNP在基于人群的查中的潜力.
主要方法:
- 在汉堡城市健康研究 (HCHS) 的心电图数据上训练了一个深度卷积神经网络.
- 模型性能在两个独立的队列中得到验证:SHIP-START和SHIP-TREND.
- 使用皮尔森相关性 (R) 和接收器操作特征曲线 (AUROC) 下的面积来评估模型准确性.
主要成果:
- 深度学习模型成功地预测了来自ECG的所有队伍的NT-proBNP水平 (R值在0.566到0.655之间).
- 预测NT-proBNP在流行和发作心力衰竭 (HF) (AUROCs 0.7830.816) 中表现出强大的歧视能力.
- 该模型还显示了其他心血管疾病事件的可比预测值,如心房动,中风,心肌梗塞和心血管死亡.
结论:
- 对ECG应用的深度学习算法可以准确预测NT-proBNP度.
- 这些算法对心力衰竭和其他主要心血管疾病具有重要的诊断和预测价值.
- 基于心电图的NT-proBNP预测可以促进基于社区的CVD风险识别,并增加AI在临床实践中的接受度.
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