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深度行为表现学习揭示了恶性心室节律失常的风险概况

Maarten Z H Kolk^1,2, Diana My Frodi³, Joss Langford^4,5

¹Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands.

NPJ digital medicine

|September 16, 2024

概括

深度学习从可穿戴数据中确定了五个患者行为概况. 低活动和睡眠不足的患者患心室节律失常的风险明显更高,为个性化预防策略提供了信息.

科学领域:

心脏病学心脏病学
数字健康数字健康
机器学习机器学习

背景情况:

突然心脏死亡 (SCD) 仍然是高风险患者的一个重大问题.
目前的风险分层严重依赖临床因素,可能缺少行为洞察力.
可穿戴技术提供日常活动和睡眠模式的持续监控.

研究的目的:

通过使用深度表示学习,在高风险的SCD患者中识别和描述不同的行为概况.
为了将这些行为特征与恶性心室节律失常的风险相关联.
探索基于已识别的行为进行个性化预防策略的潜力.

主要方法:

一项前性观察性研究 (SafeHeart) 涉及272名接受植入式心脏转换器-除器 (ICD) 的患者.
无监督的集群被应用到来自可穿戴加速度计的180天行为时间序列数据的低维表示,这些数据是通过卷积剩余变化神经网络 (ResNet-VAE) 学习的.
分析了37478天的行为数据,以确定不同的患者群.

主要成果:

确定了五种不同的行为特征:活动低/睡眠不佳 (A组),中度活动 (B组),高活动 (C组),睡眠良好 (D组) 和睡眠不佳 (E组).
恶性心室节律失常的年度风险有显著的变化,A组显示的风险最高 (30.4%).
与低风险个人资料 (集群D-E) 相比,A集群的恶性心室节律失常风险增加了3.63倍,即使经过对临床共变量进行调整 (aHR3.63,p < 0.001).

结论:

深度表示学习可以有效地从可穿戴数据中描述高风险SCD患者的行为概况.
特定的行为模式,特别是低体力活动和睡眠障碍,与恶性心室节律失常的风险大幅增加有关.
这些数据支持开发个性化的方法来预防心室节律失常和SCD,将行为洞察纳入风险评估.

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