Background Remote monitoring of patients with heart failure (HF) using wearable devices can allow patient-specific adjustments to treatments and thereby potentially reduce hospitalizations. We aimed to assess HF state using wearable measurements of electrical and mechanical aspects of cardiac function in the context of exercise.

Methods and Results Patients with compensated (outpatient) and decompensated (hospitalized) HF were fitted with a wearable ECG and seismocardiogram sensing patch. Patients stood at rest for an initial recording, performed a 6-minute walk test, and then stood at rest for 5 minutes of recovery. The protocol was performed at the time of outpatient visit or at 2 time points (admission and discharge) during an HF hospitalization. To assess patient state, we devised a method based on comparing the similarity of the structure of seismocardiogram signals after exercise compared with rest using graph mining (graph similarity score). We found that graph similarity score can assess HF patient state and correlates to clinical improvement in 45 patients (13 decompensated, 32 compensated). A significant difference was found between the groups in the graph similarity score metric (44.4±4.9 [decompensated HF] versus 35.2±10.5 [compensated HF]; P<0.001). In the 6 decompensated patients with longitudinal data, we found a significant change in graph similarity score from admission (decompensated) to discharge (compensated; 44±4.1 [admitted] versus 35±3.9 [discharged]; P<0.05).

Conclusions Wearable technologies recording cardiac function and machine learning algorithms can assess compensated and decompensated HF states by analyzing cardiac response to submaximal exercise. These techniques can be tested in the future to track the clinical status of outpatients with HF and their response to pharmacological interventions.

背景技术使用可穿戴设备对心力衰竭（HF）病人进行远程监控，可以根据具体情况调整治疗方案，从而有可能减少住院治疗。我们旨在通过运动中心脏功能的电气和机械方面的可穿戴测量来评估HF状态。

方法与结果患有补偿性（门诊）和失代偿性（住院）心力衰竭的患者配备了可穿戴的心电图​​和心电图感应贴片。患者静置进行初始记录，进行6分钟的步行测试，然后静置5分钟以恢复健康。该方案是在门诊就诊时或在HF住院期间的两个时间点（入院和出院）进行的。为了评估患者的状态，我们设计了一种方法，该方法基于运动后的运动心动图信号与休息后的运动心动图信号结构的相似性，使用图挖掘（图相似性得分）进行比较。我们发现，图相似度评分可以评估HF患者的状态，并与45例患者的临床改善相关（13例失代偿，32例失代偿）。P <0.001）。在6例具有纵向数据的失代偿患者中，我们发现从入院（失代偿）到出院（补偿； 44±4.1 [入院]与35±3.9 [出院]；P <0.05）的图相似度得分有显着变化。

结论记录心脏功能的可穿戴技术和机器学习算法可以通过分析对次最大运动的心脏反应来评估已补偿和未补偿的HF状态。这些技术可以在将来进行测试，以追踪患有HF的门诊患者的临床状况及其对药理干预措施的反应。