Cardiovascular Entropy and Mortality Prediction in Hemodialysis Patients

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Background/Objectives: The main cause of death in patients with chronic kidney disease (CKD) is of cardiovascular origin. Entropy-based analysis of physiological signals reflects system irregularity, complexity, and adaptive capacity. Amplitude-aware permutation entropy (AAPE) is a signal analysis method suitable for assessing complex cardiovascular dynamics, and growing evidence suggests that measures of physiological signal variability and complexity may have prognostic value. This study aimed to evaluate whether AAPE can predict mortality in CKD patients undergoing hemodialysis (HD), with and without diabetes. The aim of this study was to assess whether AAPE analysis of cardiovascular signals following the administration of a glucose bolus directly into the extracorporeal circuit during hemodialysis (HD)—a method originally used to treat intradialytic hypotension and to study the kinetics of glucose, insulin, and C-peptide in patients with and without type 2 diabetes—can predict mortality in patients with chronic kidney disease (CKD) undergoing hemodialysis (HD), both with and without diabetes. Methods: After seven years of follow-up, mortality outcomes were analyzed in relation to AAPE-derived parameters. Results: Higher mortality was associated with smaller differences in AAPE of mean arterial pressure (MAP) and diastolic arterial pressure (DIA) before and after intravenous glucose administration (p = 0.009 and p = 0.016, respectively). Higher tonicity was associated with higher survival (p = 0.01). Additionally, greater reductions in AAPE of systolic arterial pressure (SYS) and larger differences in AAPE of ejection time (EJT) and total peripheral resistance (TPR) were associated with increased mortality. Conclusions: These findings suggest that entropy analysis reflects cardiovascular adaptability and may serve as a prognostic biomarker in HD patients.
Funding Information
  • Military Institute of Medicine—National Research Institute (403)

This publication has 26 references indexed in Scilit: