Kolk Maarten Z H, Frodi Diana My, Langford Joss, Andersen Tariq O, Jacobsen Peter Karl, Risum Niels, Tan Hanno L, Svendsen Jesper Hastrup, Knops Reinoud E, Diederichsen Søren Zöga, Tjong Fleur V Y
Department of Clinical and Experimental Cardiology, Amsterdam UMC Location University of Amsterdam, Heart Center, Meibergdreef 9, Amsterdam, the Netherlands.
Amsterdam Cardiovascular Sciences, Heart Failure & Arrhythmias, Amsterdam UMC location AMC Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands.
NPJ Digit Med. 2024 Sep 16;7(1):250. doi: 10.1038/s41746-024-01247-w.
We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54-8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.
我们旨在通过对日常行为记录进行深度表征学习，识别并描述猝死高危患者的行为特征。我们提出了一种流程，该流程对卷积残差变分神经网络（ResNet-VAE）学习到的行为时间序列数据的低维表征进行无监督聚类。使用了来自荷兰和丹麦两个大型三级大学中心进行的前瞻性观察性SafeHeart研究的数据。患者在2021年5月至2022年9月期间接受了植入式心律转复除颤器（ICD），并在连续180天内佩戴使用加速度计技术的可穿戴设备。共有272名患者（平均年龄63.1±10.2岁，81%为男性）符合条件，共采集了37478天的行为数据（每位患者138±47天）。深度表征学习识别出五种不同的行为特征：A组（n = 46）身体活动水平极低且睡眠模式紊乱。B组（n = 70）活动水平高，主要为轻度至中度强度。C组（n = 63）表现出高强度活动特征。D组（n = 51）睡眠效率高于平均水平。E组（n = 42）有频繁的觉醒发作且睡眠质量差。恶性室性心律失常的年度风险在A组为30.4%，在D组和E组分别为9.8%和9.5%。与低风险特征组（D - E）相比，A组经临床协变量调整后，恶性室性心律失常风险增加了三到四倍（调整后HR 3.63，95% CI 1.54 - 8.53，p < 0.001）。这些行为特征可能会为室性心律失常和猝死的预防指导更个性化的方法。
