Heart rate variability in diabetic and non-diabetic renal transplant patients

Influence of Dexmedetomidine on Myocardial Injury in Patients with Simultaneous Pancreas-Kidney Transplantation

Background

Diabetes is one of the most common chronic diseases in the world. End-stage renal disease (ESRD) caused by diabetes is the most serious long-term complication. The main cause of death in patients with simultaneous pancreas-kidney transplantation (SPKT) is cardiovascular disease. Although dexmedetomidine (Dex) has unique advantages in heart protection against ischaemic/reperfusion injury, few clinical studies have been conducted on its cardioprotective effect in SPKT. This study aimed to explore the influence of Dex on myocardial injury in patients undergoing SPKT and to analyze its possible mechanism.

Methods

A randomized controlled trial (RCT) was performed from July 1, 2018 to December 1, 2020. Eighty patients, regardless of gender, scheduled for SPKT were randomly allocated into a Dex group (D group) receiving Dex at a rate of 1 µg/kg for 10 minutes before anaesthesia induction and then continuous infusion at 0.5 µg/kg/hour until the end of surgery and control group (C group) receiving equivalent capacity of saline. Serum cardiac troponin I (cTnI), creatine kinase isoenzyme (CK-MB), tumour necrosis factor-α (TNF-α), and interleukin-6 (IL-6) were recorded at 5 minutes after anaesthesia induction (baseline,T0), 5 minutes before renal arteriovenous opening (T1), 30 minutes after renal arteriovenous opening (T2), 30 minutes after pancreatic related arteriovenous opening (T3), immediately after surgery (T4), 4 hours after surgery (T5), and 24 hours after surgery (T6). Adverse cardiovascular events were recorded during the perioperative period. Changes in ECG S-T segments and T waves were monitored at T0-T6. Myocardial infarction and percutaneous coronary intervention were recorded with an average follow-up of one year.

Results

Compared with T0, TNF-α and IL-6 concentrations significantly increased at T1-T6 in the C and D groups (P < 0.05). IL-6 concentration increased significantly after renal artery opening and reached the peak after the opening of pancreatic blood vessels. Compared with the C group, TNF-α, and IL-6 concentrations were significantly reduced in group D at T2-T6 (P < 0.05). Compared with T0, cTnI and CK-MB concentrations were significantly increased at T3-T6 in the C and D groups (P < 0.05). cTnI and CK-MB concentrations increased significantly after the opening of renal artery, and reached the peak after the opening of pancreatic blood vessels. Compared with the C group, cTnI and CK-MB concentrations were significantly reduced in the D group at T3-T6 (P < 0.05). There was no significant difference in patient characteristics amongst groups, including the proportion of intraoperative vasoactive drug use and adverse cardiovascular events during the follow-up period. Heart rate, mean blood pressure, central venous pressure, and cardiac output were not remarkably different between the two groups at any time point.

Conclusions

Perioperative reperfusion could aggravate myocardial injury in SPKT. Dex may be considered a way to reduce myocardial injury caused by inflammatory action by decreasing the release of inflammatory factors. Trial Registration Number: Chinese Clinical Trial Registry ID: ChiCTR2200060084.

THE EFFECT OF DIABETES ON CARDIOVASCULAR SYSTEM

Diabetes mellitus (DM) is a critical and long-term disorder due to the insufficient production of insulin by the pancreas or ineffective use of insulin by the body. Importantly, cardiovascular disease (CVD) has long been thought to be linked with diabetes. Despite more diabetic individuals surviving from better medications and treatments, there has been significant rise in the morbidity and mortality from CVD. Indeed, the classification of DM based on the electrocardiogram signals of the heart will be an advantageous system. Further, computer-aided classification of DM with integrated algorithms may enhance the execution of the system. In this paper, we have reviewed various studies using heart rate variability signals for automated classification of diabetes. Furthermore, the different techniques used to extract the features and the efficiency of the classification systems are discussed.

A NEW METHOD FOR AUTOMATED DETECTION OF DIABETES FROM HEART RATE SIGNAL

Diabetes Mellitus (DM) is a chronic disease and it is characterized based on the increase in the sugar level in the blood. The other diseases such as the cardiomyopathy, neuropathy and retinopathy may occur due to the DM pathology. The RR-time series or heart rate (HR) signal quantifies the beat-to-beat variations in the electrocardiogram (ECG) and it has been widely used for the detection of various cardiac diseases. Detection of DM based on the features of HR signal is a challenging problem. This paper copes with a new method for the detection of Diabetes Mellitus (DM) based on the features extracted from the HR signal. The Singular Spectrum Analysis (SSA) of HR signal and the Kernel Sparse Representation Classifier (KSRC) are the mathematical foundations used to achieve the detection. SSA is used to decompose the HR signal into sub-signals, and diagnostic features such as the maximum value of each sub-signal and eigenvalues are evaluated. Then, the KSRC uses the proposed diagnostic features as inputs for detecting diabetes. The experimental results reveal that the proposal attains the accuracy, sensitivity, and specificity values of 92.18%, 93.75% and 90.62%, respectively, employing the KSRC and the hold-out cross-validation approach. The method is compared with existing approaches for detecting diabetes from HR signal.

Heart Rate Variability, Mortality, and Exercise in Patients with End-Stage Renal Disease

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.

AUTOMATED DIAGNOSIS OF DIABETES USING ENTROPIES AND DIABETIC INDEX

Diabetes Mellitus (DM) is a chronic metabolic disorder that hampers the body’s energy absorption capacity from the food. It is either caused by pancreatic malfunctioning or the body cells being inactive to insulin production. Prolonged diabetes results in severe complications, such as retinopathy, neuropathy, cardiomyopathy and cardiovascular diseases. DM is an incurable disorder. Thus, diagnosis and monitoring of diabetes is essential to prevent the body organs from severe damage. Heart Rate Variability (HRV) signal processing can be used as one of the methods for the diagnosis of DM. Our paper introduces a noninvasive technique of automated diabetic diagnosis using HRV signals. The R-R interval signals are decomposed using Shearlet transforms integrated with Continuous Wavelet Transform (CWT), and their characteristic features are extracted by using Shannon’s, Renyi’s, Kapur entropies, energy and Higher Order Spectra (HOS). Then, Locality Sensitive Discriminant Analysis (LSDA) is employed to remove insignificant features and reduce the number of employed features. These redundant features are eliminated by using six feature selection algorithms: Student’s t-test, Receiver Operating Characteristic Curve (ROC), Wilcoxon signed-rank test, Bhattacharyya distance, Information entropy and Fuzzy Max-Relevance and Min-Redundancy (MRMR). This step is followed by classification of normal and diabetic signals using different classifiers, such as discriminant classifiers, Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Naïve Bayes (NB), Fuzzy Sugeno (FSC), Gaussian Mixture Model (GMM), AdaBoost and k-Nearest Neighbor (k-NN) classifier. In these classifiers, the selected features are employed to distinguish diabetic signals from normal signals. These classifiers are trained and then tested to validate their accuracy to make accurate diagnosis. The FSC classifier is shown to have the highest (100%) accuracy. Nevertheless, we go one step further in formulating another novel classifier in the form of the diabetic index, and have shown how distinctly it is able to separate diabetic signals from normal signals.

AN IMPROVED ONLINE PARADIGM FOR SCREENING OF DIABETIC PATIENTS USING RR-INTERVAL SIGNALS

Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not treated in a timely manner, it may cause serious complications. For timely treatment, an early detection of the disease is of great interest. Diabetes can be detected by analyzing the RR-interval signals. This work presents a methodology for classification of diabetic and normal RR-interval signals. Firstly, empirical mode decomposition (EMD) method is applied to decompose the RR-interval signals in to intrinsic mode functions (IMFs). Then five parameters namely, area of analytic signal representation (AASR), mean frequency computed using Fourier-Bessel series expansion (MFFB), area of ellipse evaluated from second-order difference plot (ASODP), bandwidth due to frequency modulation (BFM) and bandwidth due to amplitude modulation (BAM) are extracted from IMFs obtained from RR-interval signals. Statistically significant features are fed to least square-support vector machine (LS-SVM) classifier. The three kernels namely, Radial Basis Function (RBF), Morlet wavelet, and Mexican hat wavelet kernels have been studied to obtain the suitable kernel function for the classification of diabetic and normal RR-interval signals. In this work, we have obtained the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation. The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals.

Automated identification of normal and diabetes heart rate signals using nonlinear measures

Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.

COMPREHENSIVE ANALYSIS OF NORMAL AND DIABETIC HEART RATE SIGNALS: A REVIEW

A large section of the world's population is affected by diabetes mellitus (DM), commonly referred to as "diabetes." Every year, the number of cases of DM is increasing. Diabetes has a strong genetic basis, hence it is very difficult to cure, but can be controlled with medications to prevent subsequent organ damage. Therefore, early diagnosis of diabetes is very important. In this paper, we examine how diabetes affects cardiac health, which is reflected through heart rate variability (HRV), as observed in electrocardiography (ECG) signals. Such signals provide clues for both the presence and severity of diabetes as well as diabetes-induced cardiac impairments. Heart rate (HR) is a non-linear and non-stationary signal. Thus, extracting useful information from HRV signals is a difficult task. We review several sophisticated signal processing and information extraction methods in order to establish measurable relationships between the presence and the extent of diabetes as well as the changes in the HRV signals. Furthermore, we discuss a typical range of values for several statistical, geometric, time domain, frequency domain, time–frequency, and non-linear features for HR signals from 15 normal and 15 diabetic subjects. We found that non-linear analysis is the most suitable approach to capture and analyze the subtle changes in HRV signals caused by diabetes.

Abnormal nocturnal heart rate variability response among chronic kidney disease and dialysis patients during wakefulness and sleep

BACKGROUND

Dialysis patients and patients with chronic kidney disease (CKD) experience a substantial risk for abnormal autonomic function and abnormal heart rate variability (HRV). It remains unknown whether HRV changes across sleep stages in patients with different severity of CKD or dialysis dependency. We hypothesized that high-frequency (HF) HRV (vagal tone) will be attenuated from wakefulness to non-rapid eye movement (NREM) and then to rapid eye movement (REM) sleep in dialysis patients as compared to patients with CKD.

METHODS

In-home polysomnography was performed in 95 patients with stages 4-5 CKD or end-stage renal disease (ESRD) on haemodialysis (HD) or peritoneal dialysis (PD). HRV was measured using fast Fourier transform of interbeat intervals during wakefulness and sleep. Low-frequency (LF) and HF intervals were generated. Natural logarithm HF (LNHF) and the logarithm LF/HF ratio (sympathovagal tone) were analysed by multivariable quantile regression and generalized estimating equations.

RESULTS

Of the 95 patients, 63.2% (n = 60) was male, 35.8% (n = 34) was African American and 20.4% (n = 19) was diabetic. Average age was 51.6 ± 15.1 (range 19-82). HRV variables were significantly associated with diabetic status, higher periodic limb movement indices and lower bicarbonate levels. Patients with advanced CKD did not differ from dialysis patients in their inability to increase vagal tone during sleep. During wakefulness, female gender (P = 0.05) was associated with the increases in the vagal tone.

CONCLUSIONS

Patients with CKD/ESRD exhibit dysregulation of the autonomic nervous system tone manifesting as a failure to increase HRV during wakefulness and sleep. Different patient characteristics are associated with changes in HRV at different sleep stages.

Strategies for managing the diabetic patient

Diabetes mellitus is now classified as either 'type 1' (failure of endogenous insulin production) or 'type 2' ('insulin resistance') and can be diagnosed if fasting blood glucose is >6.1 mmol/l (110mg/dl) on two separate occasions or there is unequivocal hyperglycaemia with acute metabolic decompensation or obvious symptoms. The prevalence of the disease is rising and may be as great as 12-14% in western populations aged over 40 years. Diabetes is complicated by micro- and macrovascular consequences of chronically elevated blood glucose concentrations, and diabetic patients are over-represented in hospital populations, particularly among patients requiring surgical interventions. It is associated with increased perioperative mortality and morbidity. Evidence is now accumulating that intensive glycaemic monitoring and the administration of insulin infusions to achieve tight glycaemic control are associated with an improvement of both perioperative mortality and morbidity.

Is pruritus in chronic uremic patients related to peripheral somatic and autonomic neuropathy? Study by R-R interval variation test (RRIV) and by sympathetic skin response (SSR)

The problem of pruritus in uremic dialysed patients remains unsolved. The etiology of pruritus has not been precisely explained, and sometimes no efficient treatment is available. The aim of this study was to analyse the relationship between somatic neuropathy and pruritus as well as the relationship between pruritus and dysautonomia. Fifty-one patients with end-stage renal failure underwent basic neurological examination, nerve conduction velocity studies, and pruritus assessment by means of a questionnaire. Two tests were used to assess the autonomic nervous system, namely the R-R interval variation test in basal and profound breath conditions (RRIV) and the sympathetic skin response (SSR). Pruritus was found in 63% patients of the sample. Most of them had clinical symptoms and signs of peripheral sensorimotor neuropathy and dysautonomia. About 59% of uremic patients revealed abnormally reduced RRIV. About 45% of patients had abnormal (delayed or absent) SSR. The pruritus in uremic patients occurred significantly more frequently (P < 0.01) in patients with paresthesia. A nonsignificant but sizeable trend towards a relationship of pruritus with hypohidrosis and pathological SSR results was also observed. There was no relationship between the pruritus presence and RRIV results. According to our results the activity of the nervous system might play an important role in the mechanism of uremic pruritus, but paradoxically this latter appeared more tightly related to somatic neuropathy than to autonomic dysfunction. Our results also suggest that SSR may become a useful technique for the assessment of autonomic dysfunction in uremic patients.

Differences in heart rate variability parameters during the post-dialytic period in type II diabetic and non-diabetic ESRD patients

BACKGROUND

Heart rate variability parameters were evaluated in 10 healthy subjects, 10 type II diabetic patients and 20 end-stage renal disease (ESRD) patients (11 non-diabetic and nine type II diabetic) undergoing chronic haemodialysis. The study was divided in two phases.

METHODS

In the first phase all subjects underwent electrocardiograph (ECG) recording under baseline conditions. In the second phase only ESRD patients underwent haemodialysis and ECG recording. On the day of dialysis and ECG recording the ECG recording was started 1 h before the haemodialysis session (pre-dialytic period), and continued throughout the dialysis (dialytic period), until the morning after (post-dialytic period).

RESULTS

Compared with ESRD patients, non-ESRD patients showed the lowest cardiac sympathetic activity. Diabetic patients compared to non-diabetic patients showed a prevalence of cardiac sympathetic activity in the pre-dialytic period (P < 0.01). During the dialytic period in comparison with the pre-dialytic one, a further increase in cardiac sympathetic activity was observed in both diabetic and non-diabetic ESRD patients (P < 0.001). However, in the post-dialysis period the cardiac autonomic nervous system activity remained at the pre-dialytic condition in the diabetic group. In contrast, in the non-diabetic group the cardiac autonomic balance shifted towards a parasympathetic prevalence in the post-dialytic period (P < 0.01). In addition, a significant correlation was found between changes in heart rate variability and changes in plasma urea concentration in the non-diabetic group only (r = 0.65; P < 0.03).

CONCLUSIONS

Non-insulin-dependent diabetic uraemic patients undergoing a chronic haemodialysis programme have a severe impairment of heart rate variability. This is probably due to autonomic neuropathy related to the effects of both diabetes and chronic uraemic conditions. In non-diabetic haemodialysis patients uraemia causes similar but reversible changes in heart rate variability compared with the changes caused by diabetes.

Posttransplant improvement in heart rate variability correlates with improved quality of life

A prospective evaluation of 37 kidney and 20 kidney-pancreas transplant recipients was conducted to assess the relationship between pre- to posttransplant changes in heart rate variability (HRV) and quality of life (QoL). Assessments of 24-hour interbeat variability (pNN50 and rMSSD, SDNN, SDANN) and power spectral analysis of total, low (sympathetic), and high (parasympathetic) frequency components of HRV were performed. The Sickness Impact Profile was used to assess three dimensions of QoL (physical, psychosocial, and total functioning) prior to and at 6 months following transplantation. Changes in vagally mediated time domain measures of HRV were related to changes in physical and total functioning. Stronger correlations occurred between biobehavioral measures in kidney-pancreas recipients, with the strongest relationships occurring between changes in HRV frequency domain measures and changes in physical functioning. Findings indicate that changes in HRV and QoL are related, suggesting that interventions that enhance transplant recipients' HRV may also enhance their QoL.

Anaesthesia for patients with a transplanted organ

Increasing numbers of individuals leading normal lives have transplanted organs. They may appear in any hospital for treatment of trauma or general diseases. Common anaesthesia methods can be used for these patients, but safe conduct of anaesthesia requires knowledge of the immunosuppression, risk factors, and altered physiology or drug actions. This article reviews the anaesthesia-related literature on patients with transplanted organs.

Does dialysis therapy improve autonomic and peripheral nervous system abnormalities in chronic uraemia?

OBJECTIVES

Autonomic nervous system (ANS) dysfunction and peripheral neuropathy occur in patients with chronic renal insufficiency. Adequate renal replacement therapy should prevent development or correct these abnormalities.

DESIGN AND SUBJECTS

We studied retrospectively ANS and peripheral neuropathy in 32 patients with chronic uraemia who received either haemodialysis (16) or peritoneal dialysis (16) therapy, and compared the observed dialysis efficiency with changes in neurological function.

METHODS

Heart rate variability (HRV) time domain indices and peripheral sensory nerve conduction studies were followed for a mean of 2.9 years. The adequacy of haemodialysis (HD) efficiency was estimated by Kt/V, an index of fractional urea clearance. Adequacy of continuous ambulatory peritoneal dialysis (CAPD) was estimated on the basis of the patient's wellbeing and nutritional status as excellent, satisfactory or poor. Based on observed changes in HRV time domain measures, the observations were divided in three subgroups: improved, unchanged or deteriorated.

RESULTS

The peripheral sensory nerve conduction studies were abnormal in 38% of the patients and did not change significantly during the study. Improvement in HRV time domain measures occurred in HD patients with mean Kt/V > 1.20 or in CAPD patients with satisfactory or excellent response to dialysis treatment. Values of Kt/V < 0.85 in HD patients were associated with progressive deterioration of autonomic neuropathy. Diabetic patients (n = 4) differed from others as their HRV was grossly abnormal and did not improve.

CONCLUSIONS

The adequacy of haemodialysis is a predictor of improvement of cardiac autonomic nervous function in chronic uraemia. The same trend of improvement was seen also in CAPD patients.

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

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.

Changes in patterns of 24-hr heart rate variability after kidney and kidney-pancreas transplant

BACKGROUND

Transplantation has been shown to improve cardiorespiratory reflex measures of autonomic function. However, there are limited data on how kidney or kidney-pancreas transplantation influence continuous autonomic modulation of heart rate and the clinical utility of 24-hr heart rate variability (HRV) monitoring.

METHODS

Ninety nondiabetic kidney and 30 diabetic kidney-pancreas transplant recipients underwent 24-hr Holter monitoring before and again at 6 and 12 months posttransplantation. Tapes were submitted for determination of HRV including interbeat variability (the proportion of adjacent R-R intervals having a difference <50 msec, the SD of all R-R intervals for the entire recording, and the SD of the averages of R-R intervals calculated over 5-min blocks for the entire recording) which is associated with vagal function, sudden death, and circadian function, respectively. Power spectral analysis quantified total neural, sympathetic, and parasympathetic modulation of the heart in ln(msec2).

RESULTS

Nondiabetic kidney recipients showed improvement (P< or =0.05) in the SD of the averages of R-R intervals calculated over 5-min blocks (83.2 vs. 95.7 msec) and the SD of all R-R intervals (94.5 vs. 104.4 msec) by 6 months and all groups showed improvement by 12 months. Kidney-pancreas recipients also showed improved total neural (4.35 vs. 4.64) and sympathetic modulation (2.70 vs. 3.13). Kidney-pancreas recipients had significantly poorer values for each measure (P< or =0.05) at all time points.

CONCLUSIONS

Cardiac autonomic neuropathy arises in the presence of uremia and diabetes, with severe dysfunction seen when these conditions occur concomitantly. Improvement in cardiac autonomic function follows both kidney and kidney-pancreas transplantation with more pronounced improvement in the circadian measures. Therefore, circadian measures of 24-hr HRV could be used to monitor the restoration of cardiac autonomic function.

Heart rate variability during chronic hemodialysis and after renal transplantation: studies in patients without and with systemic amyloidosis

The present study was undertaken to compare heart rate variability (HRV) values in patients on maintenance hemodialysis with no evidence of ischemic or hypertensive heart diseases to those of age- and gender-matched healthy individuals and those of patients after renal transplantation. To assess the effects of a common confounding factor, HRV values were also determined in patients with systemic amyloidosis, in chronic hemodialysis, and after successful renal transplantation. Spectral analyses of RR intervals from continuous electrocardiogram recordings were performed to quantify ultra low frequency, very low frequency, low frequency, and high frequency powers. HRV determinations were all significantly reduced in uremic patients undergoing hemodialysis compared with the healthy control subjects, especially in those with systemic amyloidosis. Renal transplantation normalized HRV in most patients; HRV, however, remained reduced in isolated amyloidosis patients with cardiac or adrenal involvement. HRV circadian day/night differences were preserved in hemodialysis patients and after renal transplantation in those without amyloidosis but not in those with amyloidosis. These data suggest that reduced HRV in chronic hemodialysis patients may precede other manifestations of cardiovascular disease. In uremic patients with amyloidosis, a more severe form of autonomic failure may occur. Successful transplantation corrects HRV abnormalities in most patients, suggesting that the autonomic dysfunction of uremia is caused by humoral factors reversed by the normalization of the renal function.