Diurnal variation of wearable device-based heart rate variability in the Chronic Renal Insufficiency Cohort study

Area of Science:

• Cardiology
• Nephrology
• Biomedical Engineering

Background:

• Prognostic value of continuous, out-of-clinic cardiovascular monitoring in chronic kidney disease (CKD) is under-explored.
• Wearable technology offers novel methods for assessing cardiovascular function in CKD patients.

Purpose of the Study:

• To evaluate the prognostic value of continuous, out-of-clinic biometric monitoring of cardiovascular function in CKD.
• To identify clinical and demographic factors associated with heart rate variability (HRV) metrics in CKD patients.

Main Methods:

• Collected approximately 50 hours of EKG recordings from 458 participants using wearable BioPatch devices across seven Chronic Renal Insufficiency Cohort centers.
• Utilized multivariable linear regression to analyze associations between clinical factors and HRV metrics, specifically Standard Deviation of NN Intervals (SDNN).

Main Results:

• Diabetes was significantly associated with a 7.4 ms lower SDNN compared to non-diabetic participants (p=0.001).
• Higher proteinuria (uPCR ≥ 0.2) was associated with a 5.73 ms lower SDNN compared to lower proteinuria (p=0.027).
• This study represents the largest dataset evaluating SDNN using wearable EKG technology in CKD.

Conclusions:

• Specific clinical and demographic factors, including diabetes and proteinuria, significantly influence HRV in the CKD population.
• These findings establish a foundation for investigating time-specific HRV metrics as potential predictive biomarkers for cardiovascular risk and clinical outcomes in CKD.