检查动脉脉,以识别和风险分层心力衰竭受试者与深度神经网络的神经网络
Chieh-Chun Huang1,2,3, Shih-Hsien Sung4,2, Wei-Ting Wang4
1Institute of Biomedical Informatics, National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan.
Physical and engineering sciences in medicine
|February 16, 2024
概括
一个新的深度学习算法使用动脉压力波形来准确识别和预测心力衰竭 (HF) 患者的结果. 这种脉冲波分析为改善HF临床护理提供了宝贵的见解.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 来自脉冲波分析的血液动力学参数是心力衰竭 (HF) 长期结果的既定预测因素.
- 在临床实践中,准确识别和对高烧结患者的风险分层仍然是临床实践中的关键挑战.
研究的目的:
- 开发一种集压波形的深度学习算法,用于HF识别和风险分层.
- 评估这种新算法在HF患者中的诊断和预后效用.
主要方法:
- 一项病例对照研究涉及431名高压患者和1545名对照患者,通过平面化度测量获得了状动脉压力波形.
- 一个单维深度神经网络 (DNN) 模型被训练,从正常化的大脑动脉压力波形中推导出HF得分.
- 考克斯回归模型整合了HF得分和83个临床变量,以预测HF患者的死亡率和再住院.
主要成果:
- 该DNN模型实现了高诊断性能 (灵敏度0.867,特异性0.851,准确度0.874,F1得分0.878,AUC0.93),优于其他机器学习模型.
- 与现有的风险预测模型相比,结合高频谱得分的多变量考克斯模型显示出更高的预测能力 (一致性指数0.71).
- 肝炎得分,以及年龄,PCI史,水平,NT-proBNP和血红蛋白,是肝炎患者不良结果的独立预测因素.
结论:
- 对动脉波形的深度学习分析,特别是动脉压,在心力衰竭中提供了重要的诊断和预后价值.
- 由脉冲波分析得出的HF得分可以提高心力衰竭患者的临床护理和风险分层.
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