基于可穿戴设备的心率变化的日间变化在慢性功能不全队列研究中
Carsten Skarke1,2, Wei Yang3, Daohang Sha3
1Institute for Translational Medicine and Therapeutics (ITMAT), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. cskarke@pennmedicine.upenn.edu.
在慢性病 (CKD) 中,持续的可穿戴心电图监测揭示了影响心率变化 (HRV) 的关键因素. 糖尿病和高蛋白尿与较低的NN间隔标准偏差 (SDNN) 相关,这是一个重要的HRV指标.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 生物医学工程 生物医学工程
背景情况:
- 在慢性病 (CKD) 中,持续的非临床心血管监测的预后价值尚未得到充分研究.
- 可穿戴技术为评估CKD患者心血管功能提供了新的方法.
研究的目的:
- 评估持续的诊所外生物识别监测心血管功能在CKD的预后价值.
- 确定与慢性病患者心率变化 (HRV) 度量相关的临床和人口因素.
主要方法:
- 收集了来自458名参与者使用穿戴式BioPatch设备的约50小时的心电图记录,遍及七个慢性功能不良队列中心.
- 利用多变量线性回归来分析临床因素和HRV指标之间的关联,特别是NN间隔的标准偏差 (SDNN).
主要成果:
- 与非糖尿病参与者相比,糖尿病与7.4毫秒较低的SDNN显著相关 (p=0.001).
- 较高的蛋白尿 (uPCR ≥0.2) 与较低的蛋白尿 (p=0.027) 相比,与5.73毫秒较低的SDNN相关.
- 这项研究代表了在CKD中使用可穿戴心电图技术评估SDNN的最大数据集.
结论:
- 特定的临床和人口因素,包括糖尿病和蛋白尿,在CKD患者群体中显著影响HRV.
- 这些发现为研究特定时间的HRV指标作为潜在的心血管风险和CKD临床结果的预测生物标志物奠定了基础.
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