通过可解释的先进心电图估计的心脏年龄差距与心血管风险因素和生存率有关
Thomas Lindow1,2,3, Maren Maanja4, Erik B Schelbert5
1Kolling Institute, Royal North Shore Hospital, University of Sydney, Sydney, Australia.
European heart journal. Digital health
|October 5, 2023
概括
可解释的高级心电图 (A-ECG) 心脏年龄准确预测心血管结果和风险因素,优于深度神经网络人工智能方法. 这种方法为心脏年龄估计提供了更好的临床采用和预后价值.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 预测分析是一种预测分析.
背景情况:
- 深度神经网络人工智能 (DNN-AI) 已被用于心脏年龄估计,将心脏年龄-时间年龄差异与预后联系起来.
- 可解释的先进心电图 (A-ECG) 方法使得没有DNN的确切心脏年龄计算成为可能.
研究的目的:
- 为了评估可解释的A-ECG心脏年龄的预后价值.
- 为了比较A-ECG心脏年龄与DNN-AI心脏年龄的性能.
主要方法:
- 对接受心血管磁共振成像 (n=731) 的患者进行了A-ECG和DNN-AI心脏年龄测试 (n=731).
- 后勤回归分析了心脏年龄差距和心血管风险因素之间的关联.
- 考克斯回归评估了心脏年龄差距与死亡或心力衰竭 (HF) 住院治疗之间的关联,并调整了共变量.
主要成果:
- A-ECG心脏年龄差与风险因素和结果相关 (调整后HR为1.11每5年增量).
- 在调整后 (HR 1.11),DNN-AI心脏年龄差异显示了类似的关联,但在未调整的分析中 (HR 1.00) 没有.
- 由于其一致的性能,A-ECG心脏年龄差在临床实践中具有更好的适用性.
结论:
- 可解释的A-ECG心脏年龄差距与心血管风险因素和诸如HF住院或死亡等不良结果有关.
- 可解释的A-ECG心脏年龄为DNN-AI方法提供了增强临床采用和预后准确性的潜力.
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