Heart rate variability, mortality, and exercise in patients with end-stage renal disease

2000 ◽  
Vol 10 (1) ◽  
pp. 10-16 ◽  
Author(s):  
Ann Cashion ◽  
Patricia Cowan ◽  
E. Milstead ◽  
A. Gaber ◽  
Donna Hathaway
2000 ◽  
Vol 10 (1) ◽  
pp. 10-16 ◽  
Author(s):  
Ann K. Cashion ◽  
Patricia A. Cowan ◽  
E. Jean Milstead ◽  
A. Osama Gaber ◽  
Donna K. Hathaway

Context Cardiac autonomic function has been associated with mortality in patients with end-stage renal disease. It is unknown whether end-stage renal disease patients who have succumbed to sudden cardiac death can be better identified by a newer test of heart rate variability that uses spectral analysis, rather than laboratory evoked measures. Objective This series of studies sought to characterize cardiac autonomic function in patients awaiting kidney transplantation, identify factors associated with heart rate variability, identify tests which distinguish patients at-risk for death, and compare evoked measures with 24-hour heart rate variability measures. Patients Data were collected on 184 nondiabetics, 60 type 1 diabetics, and 34 type 2 diabetics with end-stage renal disease, all of whom had been referred for kidney transplantation. Main Outcome Measures The 278 patients and 67 healthy control subjects underwent evoked tests (changes in heart rate with deep breathing and Valsalva maneuver) and 24-hour heart rate variability Holter monitoring (time and frequency domains). Five patients had sudden cardiac deaths during the study. Results Data showed that end-stage renal disease patients, particularly diabetics, had compromised autonomic function. The standard deviation of all R-to-R intervals for the electrocardiogram recording (<50 minutes in 60% of the deceased patients), a 24-hour heart rate variability time domain measure, holds the promise of identifying patients at increased risk for death. Exercise was identified as a factor associated with better autonomic function. Examining relationships between 24-hour heart rate variability and characteristics of patients who succumb to death could make quantification of the mortality risk for individual pretransplant end-stage renal disease patients possible, much as it has in other populations. The data from this study may also make it possible to design interventions, such as exercise, aimed at reducing mortality risk.


2013 ◽  
Vol 32 (3) ◽  
pp. 127-133 ◽  
Author(s):  
Kyung Won Park ◽  
Sang Kyun Bae ◽  
Buhyun Lee ◽  
Jeong Hun Baek ◽  
Jin Woo Park ◽  
...  

2020 ◽  
Author(s):  
Hongyun Liu ◽  
Ping Zhan ◽  
Jinlong Shi ◽  
Minlu Hu ◽  
Guojing Wang ◽  
...  

Abstract Objective Heart rhythm complexity, a measure of heart rate dynamics and a risk predictor in various clinical diseases, has not been systematically studied in patients with end-stage renal disease. The aim of this study is to investigate the heart rhythm complexity and its prognostic value for mortality in end-stage renal disease patients undergoing hemodialysis.Methods To assess heart rhythm complexity and conventional heart rate variability measures, 4-hour continuous electrocardiography for a retrospective cohort of 202 ostensibly healthy control subjects and 51 hemodialysis patients with end-stage renal disease were analyzed. Heart rhythm complexity was quantified by the complexity index from the measurement of the multiscale entropy profile.Results During a median follow-up of 13 months, 8 people died in the patient group. Values of either traditional heart rate variability measurements or complexity indices were found significantly lower in patients than those in healthy controls. In addition, the complexity indices (Area 1-5, Area 6-15 and Area 6-20) in the mortality group were significantly lower than those in the survival group, while there were no significant differences in traditional heart rate variability parameters between the two groups. In receiver operating characteristic curve analysis, Area 6-20 (AUC = 0.895, p<0.001) showed the strongest predictive power between mortality and survival groups.Conclusion The results suggest that heart rhythm complexity is impaired for patients with end-stage renal disease. Furthermore, the complexity index of heart rate variability quantified by multiscale entropy may be a powerful independent predictor of mortality in end-stage renal disease patients undergoing hemodialysis.


2016 ◽  
Vol 16 (C) ◽  
pp. 75
Author(s):  
Stefan Hagmair ◽  
Matthias Christoph Braunisch ◽  
Martin Bachler ◽  
Anna-Lena Hasenau ◽  
Axel Bauer ◽  
...  

1998 ◽  
Vol 82 (9) ◽  
pp. 1156-1158 ◽  
Author(s):  
Alon A Steinberg ◽  
Ronald L Mars ◽  
Daniel S Goldman ◽  
Robert F Percy

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 114 ◽  
Author(s):  
Martín Calderón-Juárez ◽  
Gertrudis Hortensia González-Gómez ◽  
Juan C. Echeverría ◽  
Héctor Pérez-Grovas ◽  
Claudia Lerma

Linear heart rate variability (HRV) indices are dependent on the mean heart rate, which has been demonstrated in different models (from sinoatrial cells to humans). The association between nonlinear HRV indices, including those provided by recurrence plot quantitative analysis (RQA), and the mean heart rate (or the mean cardiac period, also called meanNN) has been scarcely studied. For this purpose, we analyzed RQA indices of five minute-long HRV time series obtained in the supine position and during active standing from 30 healthy subjects and 29 end-stage renal disease (ESRD) patients (before and after hemodialysis). In the supine position, ESRD patients showed shorter meanNN (i.e., faster heart rate) and decreased variability compared to healthy subjects. The healthy subjects responded to active standing by shortening the meanNN and decreasing HRV indices to reach similar values of ESRD patients. Bivariate correlations between all RQA indices and meanNN were significant in healthy subjects and ESRD after hemodialysis and for most RQA indices in ESRD patients before hemodialysis. Multiple linear regression analyses showed that RQA indices were also dependent on the position and the ESRD condition. Then, future studies should consider the association among RQA indices, meanNN, and these other factors for a correct interpretation of HRV.


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