1 Nephrology and Hypertension Services and 2 Cardiology Department, Hadassah University Hospital, Jerusalem, Israel
Correspondence and offprint requests to: D. Rubinger, MD, Nephrology and Hypertension Services, Hadassah University Hospital, POB 12000, Jerusalem 91120, Israel. Email: rdvora{at}hadassah.org.il
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Abstract |
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Methods. HRV was evaluated in 56 chronic haemodialysis patients without (stable; n = 27) and with symptomatic hypotension episodes (unstable; n = 29) during daytime, haemodialysis and night-time periods. Logistic regression analysis was performed in a model that included clinical and biochemical data and HRV measurements.
Results. HRV was significantly reduced in haemodynamically unstable as compared with the stable patients. LF/HF ratio, an index representative of sympathovagal balance, was significantly lower in unstable patients, especially in those with ischaemic heart disease and diabetes mellitus. In a logistic regression model including clinical data and HRV measurements, ischaemic heart disease and left ventricular systolic dysfunction were found to be the main predictors of haemodynamic instability.
Conclusions. These data suggest that haemodynamic instability is strongly associated with a decreased HRV and an impaired sympathovagal balance, suggesting disturbed autonomic control in uraemic patients with cardiac damage. Patients with ischaemic heart disease, reduced left ventricular systolic function and decreased HRV may be at the highest risk to be haemodynamically unstable during haemodialysis. The role of early detection and treatment of ischaemic heart disease in preventing symptomatic hypotensive episodes in these patients remains to be determined.
Keywords: haemodialysis; heart rate variability; hypotension; predictors of haemodynamic instability; sympathovagal balance
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Introduction |
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Spectral analysis of heart rate variability (HRV) emerged in the last decade as a powerful non-invasive clinical tool for the assessment of sympathetic and parasympathetic functions of the autonomic nervous system. Previous studies have shown a significantly reduced HRV in patients undergoing chronic haemodialysis, even in the absence of cardiovascular disease [4]. In haemodynamically unstable patients (hypotension-prone subjects), the power pattern was mainly recorded in the parasympathetic nervous system-related high frequency (HF) band, whereas in stable patients it was principally in the sympathetic nervous system-related low frequency band (LF). Furthermore, in haemodynamically unstable patients, the LF/HF ratio during dialysis was lower than the ratio found in stable subjects [5,6]. These data were interpreted as evidence for altered autonomic control of cardiovascular function, resulting in haemodynamic instability and dialysis-induced hypotension [7].
Most HRV studies of haemodynamically unstable haemodialysis patients, however, were performed in small, heterogeneous groups of subjects. HRV was studied over short periods (34 h), usually not exceeding the dialysis time. Thus, the first objective of the present study was to determine HRV parameters in a larger group of haemodialysis patients, with (hypotension-prone) and without haemodynamic instability and to compare daytime and night-time values with those obtained during haemodialysis.
Gender, age and the presence of comorbid conditions, such as diabetes mellitus, ischaemic heart disease, hypertension and heart failure, are known to affect HRV. In the general population, patients with multiple comorbidities and, presumably, a decreased HRV are at high risk for cardiac and sudden death [8]. Similarly, high-risk patients undergoing chronic haemodialysis would be more likely to develop symptomatic hypotension during ultrafiltration. The second objective of the present study was to identify patients at elevated risk for haemodynamic instability during haemodialysis and to define the role of HRV and of patients clinical and laboratory characteristics as predictors of ultrafiltration-induced hypotension.
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Subjects and methods |
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From an initial group of 100 patients, 44 were excluded because of young age (25 years), chronic atrial fibrillation or frequent ventricular premature beats, acute illness, permanent pacemakers or lack of cooperation. HRV was evaluated in the remaining 56 chronic haemodialysis patients, with (unstable; n = 27) and without (stable; n = 29) symptomatic hypotension during ultrafiltration. In haemodynamically unstable (hypotension-prone) patients, at least three monthly episodes of hypotension and at least one episode of collapse were recorded in the selection period, while in the stable patients, no more than one hypotensive episode during the same period without signs of cardiovascular collapse was noted. The patients underwent haemodialysis three times weekly, 3.55.0 h each time. The percentage of patients dialysed in the morning and of those dialysed in the afternoon hours was similar in the two (stable and hypotensive-prone) subgroups. The mean urea reduction rate in all patients was 6575%. The dialysis was performed using a polysulphone high-flux dialyser (1.3 m2). The ultrafiltration rate (% body weight reduction) during dialysis sessions was adjusted according to the presumed dry weight (assessed as the post-dialysis patient's weight when normotensive and free of oedema). The patients were afebrile (pre-dialysis temperature 36.336.9°C) and the temperature of the dialysate was kept constant at 37°C. The dialysate flow rate was kept at 500 ml/min. The dialysis sodium concentration was kept constant at 140 mmol/l. The dialysate chloride, bicarbonate and potassium concentrations were 108, 35 and 2 mmol/l, respectively. High (1.5 mmol/l) or low (1.25 mmol/l) calcium dialysate was used as indicated.
The patients clinical characteristics are listed in Table 1. The routine medication in all patients included calcium, vitamin D supplements, sevelamer hydrochloride (Renagel) in selected cases and recombinant erythropoietin. All patients underwent routine echocardiography for the determination of the left ventricular function. Four stable patients and three unstable patients were treated with angiotensin-converting enzyme (ACE) inhibitors. Six stable and two unstable patients were treated with calcium-channel blockers (P = NS). Seven stable patients and 10 unstable had left ventricular hypertrophy on echocardiography (P = NS).
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Definitions of clinical variables
The criteria for ischaemic heart disease were as follows: a documented history of prior myocardial infarction or of coronary interventions, the presence of significantly abnormal Q waves on a 12 lead electrocardiogram or symptomatic angina pectoris. Left ventricular systolic dysfunction was defined as a decrease of the left ventricular ejection fraction (LVEF) to <40% by echocardiography measurements. Diastolic function was evaluated in 54 patients. In two patients, one stable and one unstable, diastolic function could not be determined because of severe rheumatic valvula disease. Doppler interrogation of the mitral valve, pulmonary vein and mitral annulus was used to assess diastolic function. Severe diastolic dysfunction was defined as (a) pseudonormalization of E (early filling wave) to A (late filling wave) ratio, suggesting an elevated left ventricular diastolic pressure, or as (b) a restrictive pattern, suggesting reduced compliance, i.e. grade 24 diastolic dysfunction by echocardiographic criteria [9].
The mean pre-dialysis systolic blood pressure was determined by the average of three measurements obtained the week of the Holter monitoring.
Determination of HRV
Marquette Series 8500 Holter recorders were used to record a modified V4 or V5 lead and modified V1 lead. The 24 h Holter cassette tapes were analysed on a Marquette MARS analysis system. Files of RR intervals with their annotations were then transferred to a Newron Pentium II PC. Four time domain variables were calculated for each patient: mean RR, the mean of mean 5-min RR intervals between normal beats; SD, the mean of 5-min RR SDs; SDANN, the SD of mean 5-min RR intervals; and RMSSD, the mean of 5-min root-mean square of differences of successive RR intervals. Five minute epochs of RR intervals as a function of time were used for power spectrum analysis. Power spectrum was calculated using autoregressive (AR) model analysis. AR power spectrum methods are superior to classical fast Fourier transform (FFT) methods, especially for short data segments [10, p. 164]. The disadvantage of FTT methods is the leakage of the main lobe in the power spectrum into sidelobes due to the finite segments of data. FFT data also offer smaller resolution compared with AR methods.
AR power spectrum analysis is based on time series parametric modelling [10, chapters 68]. Parametric modelling for power spectrum estimation consists of an appropriate choice of a model, estimation of its parameters and substituting these estimated parameters into the theoretical power spectrum density expressions. The model output, which is the estimated RR interval at a certain point in time, is calculated as a linear combination of previous values of RR interval values. The order of the model is the number of terms in this time series. The input driving process is assumed to be white noise. The autocorrelation function of the time series is first calculated and the YuleWalker equations, which describe the relationship between the autocorrelation function and the AR parameters, are then derived. The LevinsonDurbin algorithm is implemented to solve these equations and provides the coefficients of the AR model. These coefficients are entered into the power spectrum equation. The model order selection is generally chosen by several criteria, such as Akaike information criterion. We have chosen a 16th-order AR model as it was found to be the most suitable and serves as a good compromise between a power spectrum that is too smooth and one with too many peaks [10, p. 229].
A moving polynomial was subtracted from the RR interval signal for detrending the slow non-periodic HRV fluctuations. Power spectrum analysis was performed using a 16th-order AR model and solving the YuleWalker equations by the Levinson algorithm [10, pp. 194 and 209]. Three frequency bands were used: very low frequency (VLF) 00.05 Hz, LF 0.050.2 Hz and HF 0.20.4 Hz. The average spectral amplitudes for each frequency band and for every epoch of 5 min were determined. Averages of time and frequency domain variables were calculated for the daytime and night-time and for the dialysis period.
Sympathovagal balance, i.e. the autonomic state resulting from the interaction of sympathetic and parasympathetic influences, is thought to be cardinal in the modulation of dynamic physiological processes, such as volume regulation during haemodialysis. RR interval and LF/HF ratio, considered to be the most representative indexes of sympathovagal balance [11,12], were determined in the whole subgroups of haemodynamically stable and unstable patients and in specific subsets according to gender and comorbidities.
Statistical analysis
Statistical analysis was performed using the SPSS 10.0 for Windows® statistical package. The MannWhitney U-test was used to compare the stable patients with the non-stable patients. The Wilcoxon signed ranks test (paired) was used to compare daytime, night-time and dialysis periods in the two subgroups. Student's t-tests and 2 tests were used when appropriate. P-values of <0.05 were considered significant.
Logistic regression analysis (stepwise forward conditional procedure) was performed to select determinants of haemodynamic instability. Age, gender, duration of haemodialysis (years) and the presence of diabetes mellitus, hypertension, ischaemic heart disease, left ventricular systolic and severe diastolic dysfunction were included in the model. HRV time and frequency domain variables for logistic regression analysis were selected in relation to the dependence between them. Variables with correlation coefficients greater than 0.75 with other variables were excluded with preference given to time domain variables. The final choice of HRV variables included the daytime RR interval, SD, RMSSD and LF/HF ratio, the night-time LF power and SDANN and the SDANN during haemodialysis. The other variables with physiological significance which were included in the model were the pre-dialysis plasma levels of sodium, phosphate, calcium, albumin and haematocrit, the pre-dialysis systolic blood pressure, the mean blood flow and the percentage (%) decrease in body weight during dialysis.
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Results |
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In the haemodynamically unstable group, however, there was a higher proportion of patients with ischaemic heart disease (P = 0.003) and with left ventricular dysfunction (P = 0.006). Left ventricular systolic dysfunction (LVEF <40%) was diagnosed only in patients with ischaemic heart disease. Conversely, 68% of all patients with ischaemic heart disease had left ventricular systolic dysfunction. Five (18%) and nine (35%) patients from the stable and unstable groups, respectively, had severe diastolic dysfunction (P = NS).
The blood flow during haemodialysis and the percentage (%) of body weight reduction during one treatment session were similar in both groups of patients. There were no significant differences in pre-dialysis plasma levels of albumin, calcium, phosphate and haematocrit between groups. Pre-dialysis plasma sodium was lower in the unstable patients (P = 0.057, NS).
HRV in unstable and stable haemodialysis patients
Marked differences were observed between the haemodynamically unstable and stable patients (Tables 2 and 3). HRV time-domain determinations were lower in the unstable group; the differences in SD (at daytime, at night-time and during dialysis) and SDANN (at daytime) were statistically significant. There were no differences in RMSSD between stable and unstable patients. The frequency domain measurements were lower in the unstable patients. Most differences in frequency domain measurements were statistically significant (Table 3).
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Effects of haemodialysis on HRV
During the haemodialysis session, RR significantly increased in both subgroups of patients as compared with the daytime period and decreased during the night-time in the stable subjects (Table 2). Significant decreases during dialysis in SD as compared with the night period and in SDANN as compared with both daytime and night-time periods were noted in all patients (Table 2). The frequency domain variables (Table 3) were not significantly different during haemodialysis, with the exception of VLF (in both subgroups) and the LF/HF ratio (in the unstable group), which were significantly lower than during the night-time. HRV measurements, time and frequency domains, were also compared in the stable and unstable patients over the whole 24 h period. Again, 24 h HRV was significantly lower in unstable as compared with stable patients (data not shown).
Sympathovagal balance
Figure 1 depicts the RR interval (Figure 1A) and the LF/HF ratio (Figure 1B) at 30 min intervals during the haemodialysis session in haemodynamically unstable and stable patients. At any time interval during dialysis, mean RR was of the same magnitude in both subgroups. In contrast, LF/HF ratio (Figure 1B) was significantly lower in the unstable patients. The difference between LF/HF ratio in stable as compared with unstable patients remained unchanged during the entire dialysis procedure (P = 0.005). A lower LF/HF ratio in unstable patients was also found in the off-dialysis periods. The difference during daytime was statistically significant (P = 0.012; Table 3).
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Discussion |
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In most studies, however, HRV has been determined during the dialysis procedure only, without monitoring of the pre- or the post-dialytic periods and without consideration of the impact of comorbidity or left ventricular function. Using continuous (24 h) monitoring, we found a significantly decreased HRV in haemodynamically unstable as compared with stable patients in both time and frequency domains, not only during dialysis but also during the day and at night (Tables 2 and 3). We also found during haemodialysis an increase in RR vs daytime and a decrease in SDANN as compared with both daytime and night-time (Table 2). A reduced SDANN during haemodialysis was reported recently by Galetta et al. [15] and was interpreted as resulting from abnormal autonomic modulation during high-rate ultrafiltration. The above study, however, did not discriminate between haemodynamically stable and unstable subjects. In our study, SDANN, considered to be representative of sympathetic activity [16], decreased during dialysis in the same proportion in patients with and without haemodynamic instability and returned after the session to values similar to those before treatment. RMSSD, representative of parasympathetic activity [16], a parameter found by several investigators to decrease during ultrafiltration [5], did not significantly change during dialysis in our study.
To further characterize the role of autonomic nervous system function, we compared the intradialytic variations over short time intervals in RR and the LF/HF ratio, two parameters considered to be most representative of sympathovagal balance, in haemodialysis unstable and stable subjects (Figure 1). RR intervals recorded over 30 min periods were of the same magnitude in unstable and stable patients and did not change during dialysis. The LF/HF ratio, also recorded at 30 min intervals, was significantly lower in unstable as compared with that recorded in stable patients. This difference, however, remained constant during the dialysis procedure, as reported previously [5]. Thus, both groups of patients demonstrated a fall-off in sympathetic activity during dialysis, as reflected by SDANN, with little changes in sympathovagal balance. The persistently lower LF/HF ratio in unstable patients, however, may be interpreted as evidence for a constant state of impaired sympathovagal equilibrium. Recent studies point to significant gender-related differences in autonomic cardiovascular regulation [17]. Although we did find a significantly lower LF/HF ratio in haemodynamically unstable women, these results are of limited value because of the relatively small sample size.
Ischaemic heart disease and diabetes mellitus, two common comorbid conditions in middle-aged and elderly haemodialysis patients [18], are both associated with HRV alterations [19]. Our data show that the lowest LF/HF ratio was in haemodynamically unstable patients with ischaemic heart disease, diabetes and poor left ventricular systolic function (Figure 2).
This type of pattern may reflect a permanent neural damage after extensive myocardial injury, comparable with that seen in severe heart failure and after heart transplantation, indicating cardiac denervation [20].
Predictors of haemodynamic instability in haemodialysis patients
To define the most important predictors of haemodynamic instability during dialysis, logistic regression analysis was performed using a model that included clinical and laboratory data and HRV measurements (Table 4). In this model, ischaemic heart disease was found to be the most significant predictor of haemodynamic instability. The other two predictors of statistical significance were LF during the night and a low pre-dialysis plasma sodium concentration.
The majority of patients with ischaemic heart disease also had left ventricular systolic dysfunction (Table 1). Furthermore, when ischaemic heart disease was excluded, left ventricular systolic dysfunction, i.e. a diminished LVEF, became the strongest predictor of haemodynamic instability in the logistic model. These data suggest that in patients with ischaemic heart disease, hypotension during dialysis may occur because of poor left ventricular performance or acute ischaemia. While a higher proportion of patients with severe diastolic impairment was found in the unstable group, diastolic dysfunction did not predict haemodynamic instability in the above model. Diabetes mellitus, unless associated with ischaemic heart disease and/or left ventricular systolic dysfunction, was not a strong predictor of haemodynamic instability.
The role of LF at night, the second predictor of haemodynamic instability, is probably minor, since the odds ratio, although significant, is low (1.001).
Despite the closeness of the mean sodium concentrations of the unstable and stable patients and the lack of significant difference between them (P = 0.057), a lower pre-dialytic plasma sodium concentration was found to be the third predictor of haemodynamic instability in our model. The prognostic value of this marker of chronic illness [21] remains to be determined.
Clinical implications
Our data suggest that patients with ischaemic heart disease and with left ventricular dysfunction are most likely to be haemodynamically unstable during haemodialysis. Haemodynamic instability is strongly associated with a decreased HRV and an impaired sympathovagal balance, suggesting a disturbed autonomic control in patients with uraemia worsened by superimposed cardiac damage. Therefore, early detection and treatment of ischaemic heart disease may be effective ways to prevent hypotensive episodes in these patients. This hypothesis, however, remains to be confirmed by further prospective clinical studies.
Conflict of interest statement. None declared.
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References |
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