预测心力衰竭的结果,通过将心肺运动测试和临床数据的呼吸量测量与深度学习生存神经网络的临床数据相结合
Heather J Ross1, Mohammad Peikari1, Julie K K Vishram-Nielsen1
1The Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Department of Medicine, University of Toronto, 200 Elizabeth Street, Toronto, ON M5G 2C4, Canada.
European heart journal. Digital health
|May 22, 2024
概括
一种使用心肺运动测试 (CPET) 呼吸数据的新型深度学习模型显著改善了对心力衰竭 (HF) 结果的预测. 这种先进的模型提高了HF患者长期预后的准确性.
科学领域:
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 现有的心力衰竭 (HF) 预测模型缺乏性能,并且未能纳入复杂的心肺运动测试 (CPET) 数据.
- 来自CPET的呼吸数据为患者预后提供了丰富的,未充分利用的信息.
研究的目的:
- 开发和验证HF结果的新型时间到事件预测模型.
- 将CPET复杂的呼吸数据集成到深度学习框架中.
- 通过使用DeepSurv算法来提高HF患者结果的预测准确性.
主要方法:
- 一组2490名患有高风险心脏病或高风险心脏病的成人患者接受了CPET.
- 功能包括临床指标,标准CPET统计数据,以及来自呼吸数据的数学特征.
- 使用深度学习框架 (DeepSurv) 预测死亡,心脏移植或机械循环支持的复合结果.
主要成果:
- 从每次呼吸数据中获得的预测特征非常重要,与传统的风险因素一起.
- 该模型表现出极好的性能,AUC为0.93 (训练) 和0.87 (验证).
- 该模型显示了优秀的校准和稳定的表现跨患者子组.
结论:
- 将呼吸对呼吸CPET数据与深度学习和生存分析相结合,可显著改善HF的长期结果预测.
- 该DeepSurv模型提供了一个高度准确和数据丰富的方法来预测HF患者的轨迹.
- 这种方法为未来使用复杂患者数据的先进预测模型铺平了道路.
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