Evaluation of pulse wave velocity and central systolic blood pressure in children and adolescents with chronic kidney disease

Abstract

Objective: Investigate pulse wave velocity and central systolic blood pressure among pediatric population with chronic kidney disease. Methods: In this cross-sectional study, 57 patients (61.4% male) aged 6.2 to 17.5 years old, 44 with nondialysis chronic kidney disease and 13 on chronic dialysis, were included in the analysis. The pulse wave velocity and the central systolic blood pressure were measured with an oscillometric device with an inbuilt ARC Solver  algorithm and were compared with previously established percentiles. Results: The prevalence of elevated pulse wave velocity was 21.1% (95%Cl: 11.4-33.9) and elevated central systolic blood pressure was 28.1% (95%CI: 17.0-41.5). According to the generalized linear model, there was a higher risk of elevated pulse wave velocity in patients undergoing chronic dialysis treatment than nondialysis chronic kidney disease patients (adjPR=4.24, 95%CI: 1.97-9.13, p=<0.001). Hypertensive patients (stage 2) had a higher risk of elevated pulse wave velocity than normotensive ones (adjPR=2.70, 95%CI: 1.05-6.95, p=0.040), as did patients younger than 12 years than the older patients (adjPR=2.95, 95%CI: 1.05-8.40, p=0.041). Hypertensive patients had a higher risk of elevated central systolic blood pressure than normotensives (adjPR=3.29,, as did patients undergoing chronic dialysis treatment when comparing to nondialysis chronic kidney disease patients (adjPR=2.08, 95%Cl: 1.07-4.02). Conclusion: Younger age, dialysis, and hypertension in children are independently associated with higher pulse wave velocity. Hypertension and dialysis are independently associated with higher central systolic blood pressure.

Endothelial Dysfunction in Children With Nephrotic Syndrome: A Cross-Sectional Study

“…We observed a positive correlation between PWV and BMI. This finding suggests that though PWV is used as a marker for arterial stiffness, largely influenced by factors like age, height, and body size, which is also observed by Brecheret et al [ 26 ]. The correlation among physiological parameters and AI is difficult to interpret because of the small sample size.…”