Key result
Wearable device-based heart rate variability analysis revealed that α2 long-term fluctuations at night significantly differentiated diabetic from nondiabetic patients (0.58 vs. 0.45, p=0.004).
Why the study?
Patients with chronic kidney disease are at risk of developing cardiovascular disease, prompting pilot testing of wearable device-based evaluations outside the clinic.
Does wearable device-based analysis of heart rate variability and behavioral readouts differentiate disease phenotypes in patients with chronic kidney disease?
Cohort (n=49)
Does wearable device-based analysis of heart rate variability and behavioral readouts differentiate disease phenotypes in patients with chronic kidney disease?
Absolute Event Rate: 0.58% vs 0.45%
p-value: p=0.004
Wearable sensor-based analysis of heart rate variability can differentiate between diabetic and nondiabetic phenotypes in patients with chronic kidney disease in an outpatient setting.
May support outpatient phenotyping of diabetes in CKD; leaves open clinical utility and outcome impact.
Patients with chronic kidney disease are at risk of developing cardiovascular disease. To facilitate out-of-clinic evaluation, we piloted wearable device-based analysis of heart rate variability and behavioral readouts in patients with chronic kidney disease from the Chronic Renal Insufficiency Cohort and controls (n = 49). Time-specific partitioning of heart rate variability readouts confirm higher parasympathetic nervous activity during the night (mean RR at night 14.4 ± 1.9 ms vs. 12.8 ± 2.1 ms during active hours; n = 47, analysis of variance (ANOVA) q = 0.001). The α2 long-term fluctuations in the detrended fluctuation analysis, a parameter predictive of cardiovascular mortality, significantly differentiated between diabetic and nondiabetic patients (prominent at night with 0.58 ± 0.2 vs. 0.45 ± 0.12, respectively, adj. p = 0.004). Both diabetic and nondiabetic chronic kidney disease patients showed loss of rhythmic organization compared to controls, with diabetic chronic kidney disease patients exhibiting deconsolidation of peak phases between their activity and standard deviation of interbeat intervals rhythms (mean phase difference chronic kidney disease 8.3 h, chronic kidney disease/type 2 diabetes mellitus 4 h, controls 6.8 h). This work provides a roadmap toward deriving actionable clinical insights from the data collected by wearable devices outside of highly controlled clinical environments.
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Lahens et al. (2022) conducted a cohort in Chronic kidney disease (n=49). Wearable device-based analysis of heart rate variability and behavioral readouts vs. Nondiabetic patients / Controls was evaluated on α2 long-term fluctuations in the detrended fluctuation analysis at night (p=0.004). Wearable device-based heart rate variability analysis revealed that α2 long-term fluctuations at night significantly differentiated diabetic from nondiabetic patients (0.58 vs. 0.45, p=0.004).
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