基于深度学习的心电图风险评分对长期心血管死亡和疾病的风险评分
J Weston Hughes1, James Tooley2, Jessica Torres Soto3
1Department of Computer Science, Stanford University, Palo Alto, CA, USA. jwhughes@stanford.edu.
NPJ digital medicine
|September 12, 2023
概括
一种深度学习模型,SEER,使用静止心电图 (ECG) 准确预测长期心血管风险. 该工具增强了心血管风险分层,并有助于临床决策,以获得更好的患者结果.
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:
- 静止心电图 (ECG) 是一种常见的心血管诊断工具.
- 从静止心电图得出的长期心血管风险信息的程度仍然不清楚.
研究的目的:
- 开发和验证一个深层卷积神经网络 (CNN),能够预测静止心电图的长期心血管风险.
- 评估开发的模型SEER (斯坦福心电图风险估计器) 在预测心血管死亡率和动脉样硬化心血管疾病 (ASCVD) 的性能.
主要方法:
- 利用来自斯坦福大学医学中心的休息12导心电图的大数据集来训练深度CNN.
- 通过多个机构的独立测试集验证了SEER对5年心血管死亡率和5年ASCVD的预测准确性.
- 评估了SEER在与已有的风险评估工具 (如聚合队列方程) 一起使用时对患者风险进行重新分类的能力.
主要成果:
- 在一组测试中,SEER在预测5年心血管死亡率方面获得了0.83的AUC,在外部验证队列中获得了可比的AUC.
- SEER预测了5年的ASCVD,AUC为0.67,表明与聚合队列方程的相关性很小.
- SEER将16%的患者从低风险重新分类为中等风险,确定了具有明显更高实际10年ASCVD风险的个体.
- 该模型还显示了对心力衰竭和心房等疾病的预测能力,即使使用单个心电图线索.
结论:
- 深度学习模型,如SEER,只使用静止心电图数据,可以准确预测长期心血管风险.
- SEER为改善心血管风险分层提供了一种有价值的工具,有可能增强临床决策,并指导诸如他类药物使用之类的预防疗法.
- 将SEER与现有的风险评估工具相结合,可以更精确地识别高风险个体.
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