Skarke Carsten, Yang Wei, Sha Daohang, Lahens Nicholas F, Isakova Tamara, Unruh Mark, Deo Rajat, Carmona-Powell Eunice, Holmes John H, Ficarra Elaine, Chen Jing, Rincon-Choles Hernan, Shah Vallabh, Hsu Chi-Yuan, Anderson Amanda H, Lash James P, Rahman Mahboob
Institute for Translational Medicine and Therapeutics (ITMAT), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104.
medRxiv. 2025 May 5:2025.04.30.25326177. doi: 10.1101/2025.04.30.25326177.
Little is known about the prognostic value of out-of-clinic biometric monitoring of cardiovascular function in chronic kidney disease (CKD). Using real-world sampling strategies, a mean (±SD) of 50.3±9.3 hours of ECG recordings from wearable BioPatch ECG devices was collected in a cohort consisting of 458 participants from seven Chronic Renal Insufficiency Cohort (CRIC) centers. The presence of diabetes was associated with a 7.4 ms lower Standard Deviation of NN Intervals (SDNN) compared to non-diabetic participants (=0.001). Multivariable linear regression revealed that participants without proteinuria (uPCR<0.2) had a 5.15 ms higher SDNN compared to participants with proteinuria (uPCR≥0.2, =0.027). Cosinor modeling suggested differences in SDNN acrophase quartiles for diabetes (=0.02), history of cardiovascular disease (=0.003), eGFR (=0.04), systolic blood pressure (=0.04), and beta blocker use (=0.0002). In the spline analysis, the SDNN curve differed between participants with and without cardiovascular disease (=0.0005). This study assembled the largest dataset to date of SDNN as an index for heart rate variability from wearable digital health technology in the CRIC. The study demonstrates that several clinical and demographic factors are associated with SDNN in participants with CKD. This sets the stage to determine the predictiveness of time-specific HRV metrics for future clinical outcomes.
关于慢性肾脏病（CKD）中心血管功能的门诊外生物特征监测的预后价值，目前所知甚少。采用现实世界抽样策略，在一个由来自七个慢性肾功能不全队列（CRIC）中心的458名参与者组成的队列中，收集了可穿戴式BioPatch心电图设备平均（±标准差）50.3±9.3小时的心电图记录。与非糖尿病参与者相比，糖尿病的存在与NN间期标准差（SDNN）降低7.4毫秒相关（P=0.001）。多变量线性回归显示，与有蛋白尿（尿蛋白肌酐比值[uPCR]≥0.2）的参与者相比，无蛋白尿（uPCR<0.2）的参与者SDNN高5.15毫秒（P=0.027）。余弦分析表明，糖尿病（P=0.02）、心血管疾病史（P=犯犯犯）、估算肾小球滤过率（eGFR，P=0.04）、收缩压（P=0.04）和使用β受体阻滞剂（P=0.0002）在SDNN峰相位四分位数上存在差异。在样条分析中，有心血管疾病和无心血管疾病的参与者之间SDNN曲线不同（P=0.0005）。本研究收集了CRIC中迄今为止最大的数据集，将SDNN作为可穿戴数字健康技术中心率变异性的指标。该研究表明，在CKD参与者中，几个临床和人口统计学因素与SDNN相关。这为确定特定时间心率变异性指标对未来临床结局的预测性奠定了基础。
