Renal Failure
ISSN: 0886-022X (Print) 1525-6049 (Online) Journal homepage: http://www.tandfonline.com/loi/irnf20
Heart rate variability predicts mortality in peritoneal dialysis patients Juan Pei, Wen Tang, Li-Xian Li, Chun-Yan Su & Tao Wang To cite this article: Juan Pei, Wen Tang, Li-Xian Li, Chun-Yan Su & Tao Wang (2015) Heart rate variability predicts mortality in peritoneal dialysis patients, Renal Failure, 37:7, 1132-1137, DOI: 10.3109/0886022X.2015.1061729 To link to this article: http://dx.doi.org/10.3109/0886022X.2015.1061729
Published online: 30 Jun 2015.
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Date: 05 November 2015, At: 17:14
http://informahealthcare.com/rnf ISSN: 0886-022X (print), 1525-6049 (electronic) Ren Fail, 2015; 37(7): 1132–1137 ! 2015 Informa Healthcare USA, Inc. DOI: 10.3109/0886022X.2015.1061729
CLINICAL STUDY
Heart rate variability predicts mortality in peritoneal dialysis patients Juan Pei1,2, Wen Tang2, Li-Xian Li3, Chun-Yan Su2, and Tao Wang2 Division of Nephrology, Xiamen University First Hospital, Xiamen, China, 2Division of Nephrology, Peking University Third Hospital, Beijing, China, and 3Division of Nephrology, Guangdong Provincial Hospital of Traditional Chinese Medicine, China
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Abstract
Keywords
Background: The predictive value of heart rate variability (HRV) in peritoneal dialysis (PD) has never been tested. Methods: In this study, the associations between HRV measures and the mortality in 81 PD patients were analyzed. HRV was measured by using 5-min recordings of a stationary system by a standardized method. Both time domain and frequency domain parameters were analyzed. Results: During a follow-up period of 43.78 ± 14.77 months, 25 patients died, four patients were transferred to hemodialysis. Of the 81 patients, the time domain parameters, such as the standard deviation of differences between adjacent normal sinus to normal sinus (NN) intervals (SDSD) and the square root of the mean of the squared differences between adjacent normal NN intervals (RMSSD), were higher; the frequency domain parameters, such as the ratio of low-frequency power to high-frequency power (LF/HF) and the normalized LF, were lower, and the normalized HF was higher in the non-survived group as compared with the survived group. A Cox proportional hazards model analysis revealed that, of the HRV measures, decrease of the normalized LF, LF/HF and increase of rMSSD, SDSD, normalized HF had significant predictive value for mortality. After adjustment for other univariate predictors including age, urine volume, renal Kt/V, high-sensitivity C-reactive protein (hs-CRP), the predictive value of decreased LF/HF remained significant. Kaplan–Meier survival analysis showed mortality rate was much higher in patients with a low LF/HF (median value of 1.56). Conclusion: The decreases of LF/HF which reflects impaired sympathetic nerve regulation is an independent predictor of mortality in PD patients.
Autonomic nervous system, chronic kidney disease, heart rate variability, mortality, peritoneal dialysis
Introduction The mortality of patients with end-stage renal disease (ESRD) on chronic peritoneal dialysis remains high despite significant advances in technologies in the past decades.1–3 Factors related to mortality include age, serum albumin level, protein–energy wasting (PEW), high-sensitivity C-reactive protein (hs-CRP) level, cardiovascular disease, peritonitis, comorbidity, depression, volume status, residual kidney function (RRF), etc.,4–8 in which cardiovascular death accounts for more than half of the death. Heart rate variability (HRV), which is defined as ‘‘the amount of fluctuations from the mean heart rate’’, is primarily controlled by the continuous interplay of the autonomic nervous system, and it is used as a useful noninvasive tool to assess the autonomic function. Reduced HRV has prognostic value in various populations,9–12 and it is reported that reduced HRV predicted an increased risk of mortality in chronic hemodialysis (HD) patients and non-dialysis chronic kidney disease (CKD) patients.13,14 However, the prognostic effect of HRV on peritoneal dialysis patients remains unclear. Address correspondence to Tao Wang, M.D., Ph.D., Division of Nephrology, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing 100191, China. Tel: +86 1062080399; Fax: +86 1062080399; E-mail: [email protected]
History Received 29 January 2015 Revised 10 May 2015 Accepted 31 May 2015 Published online 30 June 2015
Therefore, in the present study, we tried to investigate the predictive effect of HRV analysis parameters on mortality of continuous ambulatory peritoneal dialysis (CAPD) patients.
Subjects and methods Study populations This is a prospective observational study. In December 2007, CAPD patients from our peritoneal dialysis (PD) unit were enrolled. The exclusion criteria were: (a) dialysis duration of less than 3 months, (b) unwillingness to participate in the study, (c) a cardiac event that occurred less than 3 months before the study, (d) a history of hemodynamically significant valvular or congestive heart failure, (e) atrial fibrillation or flutter and (f) high grade heart block or a permanent pacemaker. Analysis of HRV A dedicated doctor was in charge of all HRV measurements with standardized method. Briefly, all patients were studied in a quiet, comfortable room from 8 am to 10 am. Before recording, we asked all patients to breathe at normal frequency and the respiratory rate was recorded. The patients were then lying in supine position for at least 10 min before
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DOI: 10.3109/0886022X.2015.1061729
starting a baseline. The patients underwent ambulatory ECG recording for 5 min. All recordings were analyzed using ECG Explorer 500 A pc electrocardiograph (Multiparameter Analysis and Review System). An automatic analysis was made to classify QRS morphology, to distinguish normal and non-normal QRS complexes and to identify normal sinus rhythm, to exclude ectopic beats and arrhythmias that would interfere with HRV analysis. The following time domain analyses of normal (NN) variability were calculated: the standard deviation of all normal NN intervals (SDNN, ms); the standard deviation of differences between adjacent NN intervals (SDSD, ms); the square root of the mean of the squared differences between adjacent normal NN intervals (RMSSD, ms); the percentage of differences between adjacent normal NN intervals that were 450 ms computed (pNN50, %). Frequency domain measures of R–R variability were assessed with power spectral analysis of ECG recordings by a nonparametric (fast Fourier transformation) method for the following frequency bands: the low-frequency energy in the power spectrum 0.0400–0.1500 Hz (LF, ms2); the highfrequency energy in the power spectrum 0.1500–0.400 Hz (HF, ms2); the total power spectrum of 0.0033–1.7070 Hz (TP, ms2) and the ratio of low- to high-frequency power (LF/HF). The LF and HF bands were expressed in normalized units (LF normalized units ¼ LF/[TP VLF]; HF normalized units ¼ HF/[TP VLF]).15 Bioimpedance analysis Multiple-frequency bioelectrical impedance analysis (BIA) was performed with the Hydra analyzer (Xitron Technologies, San Diego, CA) after patients emptied their dialysis solutions. The procedure to perform this measurement was described in detail elsewhere. From this measurement, the estimation of extracellular water (ECW), intracellular water (ICW) and total body water (TBW) could be acquired. Measurement of BP, biochemistry and dialysis adequacy A dedicated renal nurse was in charge of all blood pressure measurements, and the mercury sphygmomanometer used was calibrated regularly. All measurements were performed in a quiet room. Brachial blood pressure was measured three times in the sitting position after patients had rested 410 min. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were averaged from values of the three measurements. Serum albumin was determined by the bromocresol green method. Other biochemical indices, such as blood urea, serum creatinine, were determined using standard methods. Residual renal function (RRF) was measured as glomerular filtration rate (GFR) using the mean of urea and creatinine clearances. Total fluid removal (TFR) was the sum of daily urine and ultrafiltration (UF). Small solute removal was determined by measurement of total (peritoneal dialysis and renal) weekly urea Kt/V using standard methods. The contributions to total Kt/V (tKt/V) by peritoneal dialysis (pKt/V) and RRF (rKt/V) were estimated separately. The volume of urea distribution (V) was derived using Watson’s formula.
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Subjective global assessment (SGA) SGA scores were used to assess patient’s nutritional status. SGA comprises six subjective assessments, three based on the patient’s history of weight loss, presence of anorexia and vomiting, and three based on the examiners’ grading of muscle wasting, presence of edema and loss of subcutaneous fat. On the basis of these assessments, patients’ nutritional status was graded as ‘‘A’’, ‘‘B’’ or ‘‘C’’, reflecting normal nutrition, mild protein–energy wasting (PEW) and moderate or severe PEW, respectively. In this study, no patient was characterized as Grade ‘‘C’’. Follow-up Patients were followed until the end of point (death, transfer to HD or transplant) or until 31 December 2011. Survival times were recorded from the day of the time of the 5-min electrocardiogram recording until death or censoring. The event of this study was death. Survival times were considered to be censored at the date of a kidney transplant, transfer to HD or at the end of the follow-up period on 31 December 2011. All the baseline data in the present analysis also referred to the time when the 5-min electrocardiogram recording was done. Statistical analysis Statistical analysis was performed using SPSS for Windows software, version 12.0 (SPSS Inc., Chicago, IL). Measures of HRV were analyzed after logarithmic transformation. All data were expressed as mean ± SD for continuous variables or percentages for categorical variables. To compare different groups, unpaired Student’s t-test was used for continuous variables as appropriate, the Chi-square test was used for categorical variables. Survival analysis was performed using a Cox proportional hazards model. Variables that achieved statistical significance in the univariate analysis were subsequently included in a multivariate analysis using a stepwise Cox regression procedure. Kaplan–Meier cumulative survival curves were constructed and compared using the log rank test. All tests were two-sided, a p-value below 0.05 was considered significant.
Results Baseline clinical data and outcomes Eighty-one patients who met the inclusion criteria were included in the present study. The demographic characteristic of the study population is shown in Table 1. During the 4-year follow-up period (mean 43.78 ± 14.77 months), 25 patients died (two sudden death, two acute myocardial infarctions, six fatal strokes, three malignant neoplasms, four multiple organ failure and eight severe infection), four patients were transferred to HD (three because of peritonitis and one because of economical reason). The prognostic factors for mortality Baseline parameters of patients grouped by survival status are shown in Table 2. Compared with the patients who survived during follow-up, those who died were older and had lower
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levels of urine volume, DBP, serum potassium, ICW, GFR and rKt/V. There were no significant differences between the two groups on gender, body mass index (BMI), PD duration, diabetes, SBP, creatinine, blood urea nitrogen (BUN), PEW, hemoglobin, albumin, ECW, TBW, hs-CRP, parathyroid hormone (PTH), high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, triglycerides,
Table 1. Demographic characteristics of the study population.
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Variable
Result
Number of patients Number of males (%) Age (years) Time on PD (months) Primary disease, n (%) Glomerulonephritis Diabetic nephropathy Hypertensive nephropathy Interstitial nephritis Polycystic kidney disease Other or unknown Diabetes, n (%) Protein–energy wasting, n (%)
81 41 (50.61) 65.25 ± 11.97 14 (range 3–99) 22 23 13 13 2 8 26 13
(27.16) (28.40) (16.05) (16.05) (2.47) (9.88) (32.10) (18.31)
pKt/V, tKt/V, UF, TFR, use of beta-blocker and angiotensinconverting enzyme inhibitors (ACEI). The analysis of HRV measures in peritoneal dialysis patients grouped by survival status is shown in Table 3. In the group of non-survival, rMSSD and SDSD were higher, the normalized LF and LF/HF were lower and the normalized HF was higher as compared with the survival group. There were no significant differences in other indices of HRV between the two groups. The univariate analysis of the relative risk for mortality is shown in Tables 4 and 5. Elder, lower urine volume, rKt/V, serum potassium, ICW and higher hs-CRP are all risks for high mortality. Among HRV measures, a lower normalized LF, LF/HF and a higher rMSSD, SDSD were related to high mortality. Multivariate Cox regression analysis showed that urine volume, hs-CRP and LF/HF were three independent prognostic factors for death during the follow-up (Table 6). Figure 1 shows the comparison of survival rate of patients with different LF/HF values (grouped by median of LF/HF, LF/HF 41.56 vs. 1.56). The 4-year mortality rate for the patients who had an LF/HF of 41.56 or 1.56 was 17.5% or 43.9%, respectively. Lower LF/HF (1.56) was a strong predictor for high mortality. Figure 2 shows that patients with
Table 2. Clinical features of patients grouped by survival status.
Age (years) Male, n (%) BMI Time on PD (months) Diabetes, n (%) Hypertension, n (%) SBP DBP Hemoglobin (g/L) BUN (mmol/L) Creatinine (mmol/L) Albumin (g/L) Serum potassium (mmol/L) hs-CRP (mg/L) PTH (pg/mL) PEW, n (%) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL) Triglycerides (mg/dL) Urine volume (mL/day) UF(mL/day) TFR (mL/day) GFR (mL/min) rKt/V pKt/V tKt/V ECW (L) ICW (L) TBW (L) ACEI, n (%) Beta-blocker, n (%)
Survival (n ¼ 56)
Non-survival (n ¼ 25)
p
60.04 ± 12.23 30 (30/56) 23.83 ± 3.97 21.15 ± 16.62 17 (17/56) 28 (28/52) 136.15 ± 24.84 82.65 ± 13.15 115.92 ± 19.25 21.15 ± 5.49 853.02 ± 264.87 38.22 ± 4.13 4.46 ± 0.79 1.07 ± 0.982 206.29 ± 181.54 9 (9/49) 1.24 ± 0.401 3.02 ± 0.95 2.36 ± 1.43 609.41 ± 492.35 424.98 ± 310.15 1019.35 ± 377.48 2.25 ± 2.10 0.58 ± 0.50 1.32 ± 0.43 1.91 ± 0.48 15.67 ± 3.58 13.17 ± 4.04 29.06 ± 6.95 15 (37.5) 26 (40.5)
68.52 ± 9.03 11 (11/26) 24.80 ± 3.05 20.63 ± 25.87 9 (9/25) 12 (12/23) 132.52 ± 26.87 73.91 ± 11.67 113.57 ± 21.68 18.90 ± 5.96 779.43 ± 218.02 36.83 ± 3.55 4.04 ± 0.97 6.66 ± 17.58 204.98 ± 222.64 4 (4/22) 1.20 ± 0.35 3.44 ± 1.45 2.74 ± 2.12 231.43 ± 308.37 595.00 ± 391.32 868.96 ± 396.33 1.26 ± 1.70 0.27 ± 0.35 1.49 ± 0.41 1.80 ± 0.47 15.12 ± 3.51 11.04 ± 3.81 26.16 ± 6.92 3 (21.4) 8 (23.8)
0.004 0.426 0.298 0.915 0.415 0.894 0.571 0.008 0.638 0.106 0.232 0.164 0.048 0.056 0.919 0.606 0.709 0.173 0.394 0.000 0.061 0.113 0.047 0.002 0.067 0.381 0.525 0.029 0.088 0.272 0.600
Notes: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; hs-CRP, high-sensitivity C-reactive protein; PEW, protein–energy wasting; PTH, parathyroid hormone; HDL, high density lipoprotein; LDL, low density lipoprotein; UF, ultrafiltration; TFR, total fluid removal; GFR, glomerular filtration rate; pKt/V, peritoneal Kt/V; rKt/V, renal Kt/V; tKt/V; total Kt/V (rKt/V + pKt/V); ECW, extracellular water; ICW, intracellular water; TBW, total body water; ACEI, angiotensin converting enzyme inhibitors.
Heart rate variability and mortality in PD patients
DOI: 10.3109/0886022X.2015.1061729
Table 3. HRV measures of patients grouped by survival status.
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SDNN (ms) rMSSD (ms) SDSD (ms) pNN50 TP power (ms2) VLF (ms2) LF (ms2) LFnor power (nu) HF power (ms2) HFnor power (nu) LF/HF
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Table 6. Multivariate Cox regression analysis of clinical and HRV measures in relation to death.
Survival (n ¼ 56)
Non-survival (n ¼ 25)
p
36.12 ± 39.77 36.12 ± 39.77 56.55 ± 78.05 11.12 ± 21.75 1826.09 ± 4944.06 379.27 ± 698.52 499.41 ± 1415.07 54.12 ± 23.64 715.68 ± 2297.69 30.75 ± 14.83 2.82 ± 2.77
57.44 ± 48.43 57.44 ± 48.43 104.72 ± 104.16 13.00 ± 21.97 2996.92 ± 3776.97 433.40 ± 551.27 588.76 ± 821.43 36.48 ± 21.03 1372.28 ± 1879.91 41.68 ± 13.93 1.21 ± 1.19
0.061 0.046 0.046 0.721 0.295 0.733 0.770 0.001 0.214 0.003 0.000
All-cause death RR (95% CI) Age (per 1 year increase) hs-CRP (per 1 mg/L increase) Urine volume (per 1 mL increase) LF/HF (per 1 increase)
1.054 1.035 0.998 0.701
(1.009–1.101) (1.009–1.061) (0.997–1.000) (0.501–0.979)
p 0.012 0.010 0.024 0.028
Notes: hs-CRP, high-sensitivity C-reactive protein; LF, low frequency; HF, high frequency.
Notes: SDNN, SD of all normal-to-normal NN intervals; rMSSD, the square root of the mean of the squared differences between adjacent normal NN intervals; SDSD, the SD of differences between adjacent NN intervals; pNN50, the percentage of differences between adjacent normal NN intervals that were 450 ms computed; TP, total power; VLF, very low frequency; LF, low frequency; HF, high frequency.
Table 4. Univariate Cox regression analysis of clinical characteristics related to death. RR (95% CI) Age (per 1 year increase) Male versus female Diabetes versus non-diabetes PEW (by SGA) versus no PEW Potassium (per 1 mmol/L increase) Serum albumin (per 1 g/L increase) serum urea (per 1 mmol/L increase) Hemoglobin (per 1 g/L increase) Beta-blocker versus no beta-blocker ACEI versus no ACEI SBP (per 1 mmHg increase) DBP (per 1 mmHg increase) ICW (per 1 L increase) Urine volume (per 1 mL increase) rKt/V (per 1 increase) PTH (per 1 pg/mL increase) hs-CRP (per 1 mg/L increase)
1.061 1.504 1.558 1.412 0.501 0.928 0.924 0.996 0.441 0.781 0.994 0.952 0.880 0.998 0.195 1.000 1.054
(1.016–1.109) (0.650–3.479) (0.673–3.610) (0.471–4.223) (0.270–0.900) (0.843–1.022) (0.811–1.007) (0.975–1.017) (0.146–1.331) (0.521–1.233) (0.977–1.011) (0.920–0.985) (0.786–0.986) (0.996–0.999) (0.057–0.675) (0.997–1.002) (1.023–1.085)
p 0.007 0.340 0.301 0.538 0.021 0.128 0.070 0.695 0.146 0.272 0.492 0.070 0.028 0.004 0.010 0.769 0.000
Figure 1. Kaplan–Meier survival curves for death in patients dichotomized by the median values of LF/HF. LF, low frequency; HF, high frequency.
Notes: PEW, protein–energy wasting; ACEI, angiotensin converting enzyme inhibitors; SBP, systolic blood pressure; DBP, diastolic blood pressure; ICW, intracellular water; rKt/V, renal Kt/V; PTH, parathyroid hormone; hs-CRP, high-sensitivity C-reactive protein.
Table 5. Univariate Cox regression analysis of heart rate variability measures in relation to death. RR (95% CI) SDNN (per 1 ms increase) rMSSD (per 1 ms increase) SDSD (per 1 ms increase) TP (per 1 ms2 increase) LF (per 1 ms2 increase) LF norm (per 1 nu increase) HF (per 1 ms2 increase) HF norm (per 1 nu increase) LF/HF (per 1 increase)
1.007 1.005 1.004 1.162 1.005 0.977 1.122 1.148 0.685
(1.000–1.014) (1.000–1.010) (1.000–1.007) (0.903–1.496) (0.794–1.273) (0.961–0.993) (0.936–1.346) (0.851–1.549) (0.496–0.947)
p 0.060 0.039 0.039 0.244 0.966 0.004 0.213 0.366 0.022
Notes: SDNN, SD of all normal-to-normal NN intervals; rMSSD, the square root of the mean of the squared differences between adjacent normal NN intervals; SDSD, the SD of differences between adjacent NN intervals; pNN50, the percentage of differences between adjacent normal NN intervals that were 450 ms computed; TP, total power; VLF, very low frequency; LF, low frequency; HF, high frequency.
Figure 2. Kaplan–Meier survival curves for cardiac death in patients with a urine volume of 4100 versus 100 mL/L.
urine volume of 4100 mL per day had significantly better survival. Figure 3 shows that the inflamed patients who were defined as hs-CRP values higher than 3 mg/L had significantly higher mortality as compared to the non-inflamed patients.
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Figure 3. Kaplan–Meier survival curves for death in patients with a hsCRP 43 mg/L versus 3 mg/L. hs-CRP, high-sensitivity C-reactive protein.
Discussion In this prospective study, we examined the prognostic value of 5-min HRV measures in 81 chronic peritoneal dialysis patients with a follow-up period of 43.78 ± 14.77 months. A mortality predictive effect of HRV indictors especially LF/HF values was observed by the present study even with adjustment of other confounding factors. To our knowledge this is the first report investigating the prognostic value of HRV in PD patients. The abnormalities of HRV in patients with renal impairment have been documented by pervious study. It has been found that chronic PD and HD patients had reduced HRV as compared to the healthy controls.16,17 Furuland et al. showed that CKD patients not yet on dialysis had a reduced HRV (both time domain and frequency domain) as compared with a reference group without impaired renal function.18 Chandra et al. found that lower HRV (frequency domain) was independently associated with higher risk of CVD, the composite of CVD/death and ESRD in non-dialysis CKD patients.14 Fukuta et al. found that HRV is reduced in chronic HD patients compared with healthy individuals, further, those patients with greatest reduction in this variable also have the greatest risk for cardiac death.13 Hayano et al. showed that HRV has an independent prognostic value in chronic HD patients and identifies an increased risk for all-cause and sudden death,19 and Oikawa et al. reported that decreased HRV (SDNN) on 24-h ambulatory ECG was an independent predictor of all-death and cardiovascular death in chronic HD patients.20 However, this predictive value of HRV in PD patients has not been elucidated previously. Thus, our study added valuable evidence in this area. In our study, a link between decreased LF/HF and high mortality was discovered, which is consistent with a recent study showing that the low LF/HF has a prognostic value of CVD and ESRD in non-dialysis CKD patients,14 and this observation has been confirmed by Furuland et al.;18 a decrease in LF/HF can significantly predict cardiac death in CKD patients. Brotman et al.21 studied 13,241 adults (45–64-years old) followed for a median of 16 years in the Atherosclerosis Risk in Communities study and
Ren Fail, 2015; 37(7): 1132–1137
underwent 2-min ECG monitoring between follow-up periods to assess prognostic value of HRV. Multivariate Cox proportional hazard model revealed that low LF/HF and other HRV parameters could predict higher hospitalization and development of kidney disease, and the reduced LF/HF could predict cardiac death in chronic HD patients.13 With these results it is evident that a reduced LF/HF has an independent prognostic value in communities, CKD and chronic HD patients and identifies patients with an increased risk for hospitalization, development of kidney disease, cardiac-death and all-cause death. The potential mechanism to explain the relationship between reduced LF/HF and mortality remains unclear. As the LF/HF is considered by some investigators to mirror sympatho/vagal balance or to reflect sympathetic modulations, we speculated that this impaired autonomic nerve system in PD patients may result in inefficiency of the natural stress coping system and thus subject the patients to high mortality when comorbidity occurs. Further study is warranted in this field. In line with previous studies, our study also showed that anuric status and hs-CRP were strong predictors of death in PD patients.8,22 It is interesting to note that our study found that the non-survival group had a lower ICW as compared to the survival group. ICW reflects water content in the body cell mass.23 Changes in body protein mainly occur in the cellular compartment;24 consequently, the alterations in body protein are generally accompanied by changes in ICW.25 A decreased ICW may indicate the loss of intracellular proteins caused by protein–energy malnutrition, which is common in PD patients. Our study found that rMSSD and SDSD were almost the same, since many studies showed that the time domain measures correlated closely with others,15,26 our observations seem basically in line with those studies. We conclude that decrease of LF/HF which shows impaired sympathetic nerve modulation is an independent predictor of mortality in PD patients.
Declaration of interest There are no financial conflict of interest to report.
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ISSN: 0886-022X (Print) 1525-6049 (Online) Journal homepage: http://www.tandfonline.com/loi/irnf20
Heart rate variability predicts mortality in peritoneal dialysis patients Juan Pei, Wen Tang, Li-Xian Li, Chun-Yan Su & Tao Wang To cite this article: Juan Pei, Wen Tang, Li-Xian Li, Chun-Yan Su & Tao Wang (2015) Heart rate variability predicts mortality in peritoneal dialysis patients, Renal Failure, 37:7, 1132-1137, DOI: 10.3109/0886022X.2015.1061729 To link to this article: http://dx.doi.org/10.3109/0886022X.2015.1061729
Published online: 30 Jun 2015.
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Date: 05 November 2015, At: 17:14
http://informahealthcare.com/rnf ISSN: 0886-022X (print), 1525-6049 (electronic) Ren Fail, 2015; 37(7): 1132–1137 ! 2015 Informa Healthcare USA, Inc. DOI: 10.3109/0886022X.2015.1061729
CLINICAL STUDY
Heart rate variability predicts mortality in peritoneal dialysis patients Juan Pei1,2, Wen Tang2, Li-Xian Li3, Chun-Yan Su2, and Tao Wang2 Division of Nephrology, Xiamen University First Hospital, Xiamen, China, 2Division of Nephrology, Peking University Third Hospital, Beijing, China, and 3Division of Nephrology, Guangdong Provincial Hospital of Traditional Chinese Medicine, China
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Abstract
Keywords
Background: The predictive value of heart rate variability (HRV) in peritoneal dialysis (PD) has never been tested. Methods: In this study, the associations between HRV measures and the mortality in 81 PD patients were analyzed. HRV was measured by using 5-min recordings of a stationary system by a standardized method. Both time domain and frequency domain parameters were analyzed. Results: During a follow-up period of 43.78 ± 14.77 months, 25 patients died, four patients were transferred to hemodialysis. Of the 81 patients, the time domain parameters, such as the standard deviation of differences between adjacent normal sinus to normal sinus (NN) intervals (SDSD) and the square root of the mean of the squared differences between adjacent normal NN intervals (RMSSD), were higher; the frequency domain parameters, such as the ratio of low-frequency power to high-frequency power (LF/HF) and the normalized LF, were lower, and the normalized HF was higher in the non-survived group as compared with the survived group. A Cox proportional hazards model analysis revealed that, of the HRV measures, decrease of the normalized LF, LF/HF and increase of rMSSD, SDSD, normalized HF had significant predictive value for mortality. After adjustment for other univariate predictors including age, urine volume, renal Kt/V, high-sensitivity C-reactive protein (hs-CRP), the predictive value of decreased LF/HF remained significant. Kaplan–Meier survival analysis showed mortality rate was much higher in patients with a low LF/HF (median value of 1.56). Conclusion: The decreases of LF/HF which reflects impaired sympathetic nerve regulation is an independent predictor of mortality in PD patients.
Autonomic nervous system, chronic kidney disease, heart rate variability, mortality, peritoneal dialysis
Introduction The mortality of patients with end-stage renal disease (ESRD) on chronic peritoneal dialysis remains high despite significant advances in technologies in the past decades.1–3 Factors related to mortality include age, serum albumin level, protein–energy wasting (PEW), high-sensitivity C-reactive protein (hs-CRP) level, cardiovascular disease, peritonitis, comorbidity, depression, volume status, residual kidney function (RRF), etc.,4–8 in which cardiovascular death accounts for more than half of the death. Heart rate variability (HRV), which is defined as ‘‘the amount of fluctuations from the mean heart rate’’, is primarily controlled by the continuous interplay of the autonomic nervous system, and it is used as a useful noninvasive tool to assess the autonomic function. Reduced HRV has prognostic value in various populations,9–12 and it is reported that reduced HRV predicted an increased risk of mortality in chronic hemodialysis (HD) patients and non-dialysis chronic kidney disease (CKD) patients.13,14 However, the prognostic effect of HRV on peritoneal dialysis patients remains unclear. Address correspondence to Tao Wang, M.D., Ph.D., Division of Nephrology, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing 100191, China. Tel: +86 1062080399; Fax: +86 1062080399; E-mail: [email protected]
History Received 29 January 2015 Revised 10 May 2015 Accepted 31 May 2015 Published online 30 June 2015
Therefore, in the present study, we tried to investigate the predictive effect of HRV analysis parameters on mortality of continuous ambulatory peritoneal dialysis (CAPD) patients.
Subjects and methods Study populations This is a prospective observational study. In December 2007, CAPD patients from our peritoneal dialysis (PD) unit were enrolled. The exclusion criteria were: (a) dialysis duration of less than 3 months, (b) unwillingness to participate in the study, (c) a cardiac event that occurred less than 3 months before the study, (d) a history of hemodynamically significant valvular or congestive heart failure, (e) atrial fibrillation or flutter and (f) high grade heart block or a permanent pacemaker. Analysis of HRV A dedicated doctor was in charge of all HRV measurements with standardized method. Briefly, all patients were studied in a quiet, comfortable room from 8 am to 10 am. Before recording, we asked all patients to breathe at normal frequency and the respiratory rate was recorded. The patients were then lying in supine position for at least 10 min before
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DOI: 10.3109/0886022X.2015.1061729
starting a baseline. The patients underwent ambulatory ECG recording for 5 min. All recordings were analyzed using ECG Explorer 500 A pc electrocardiograph (Multiparameter Analysis and Review System). An automatic analysis was made to classify QRS morphology, to distinguish normal and non-normal QRS complexes and to identify normal sinus rhythm, to exclude ectopic beats and arrhythmias that would interfere with HRV analysis. The following time domain analyses of normal (NN) variability were calculated: the standard deviation of all normal NN intervals (SDNN, ms); the standard deviation of differences between adjacent NN intervals (SDSD, ms); the square root of the mean of the squared differences between adjacent normal NN intervals (RMSSD, ms); the percentage of differences between adjacent normal NN intervals that were 450 ms computed (pNN50, %). Frequency domain measures of R–R variability were assessed with power spectral analysis of ECG recordings by a nonparametric (fast Fourier transformation) method for the following frequency bands: the low-frequency energy in the power spectrum 0.0400–0.1500 Hz (LF, ms2); the highfrequency energy in the power spectrum 0.1500–0.400 Hz (HF, ms2); the total power spectrum of 0.0033–1.7070 Hz (TP, ms2) and the ratio of low- to high-frequency power (LF/HF). The LF and HF bands were expressed in normalized units (LF normalized units ¼ LF/[TP VLF]; HF normalized units ¼ HF/[TP VLF]).15 Bioimpedance analysis Multiple-frequency bioelectrical impedance analysis (BIA) was performed with the Hydra analyzer (Xitron Technologies, San Diego, CA) after patients emptied their dialysis solutions. The procedure to perform this measurement was described in detail elsewhere. From this measurement, the estimation of extracellular water (ECW), intracellular water (ICW) and total body water (TBW) could be acquired. Measurement of BP, biochemistry and dialysis adequacy A dedicated renal nurse was in charge of all blood pressure measurements, and the mercury sphygmomanometer used was calibrated regularly. All measurements were performed in a quiet room. Brachial blood pressure was measured three times in the sitting position after patients had rested 410 min. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were averaged from values of the three measurements. Serum albumin was determined by the bromocresol green method. Other biochemical indices, such as blood urea, serum creatinine, were determined using standard methods. Residual renal function (RRF) was measured as glomerular filtration rate (GFR) using the mean of urea and creatinine clearances. Total fluid removal (TFR) was the sum of daily urine and ultrafiltration (UF). Small solute removal was determined by measurement of total (peritoneal dialysis and renal) weekly urea Kt/V using standard methods. The contributions to total Kt/V (tKt/V) by peritoneal dialysis (pKt/V) and RRF (rKt/V) were estimated separately. The volume of urea distribution (V) was derived using Watson’s formula.
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Subjective global assessment (SGA) SGA scores were used to assess patient’s nutritional status. SGA comprises six subjective assessments, three based on the patient’s history of weight loss, presence of anorexia and vomiting, and three based on the examiners’ grading of muscle wasting, presence of edema and loss of subcutaneous fat. On the basis of these assessments, patients’ nutritional status was graded as ‘‘A’’, ‘‘B’’ or ‘‘C’’, reflecting normal nutrition, mild protein–energy wasting (PEW) and moderate or severe PEW, respectively. In this study, no patient was characterized as Grade ‘‘C’’. Follow-up Patients were followed until the end of point (death, transfer to HD or transplant) or until 31 December 2011. Survival times were recorded from the day of the time of the 5-min electrocardiogram recording until death or censoring. The event of this study was death. Survival times were considered to be censored at the date of a kidney transplant, transfer to HD or at the end of the follow-up period on 31 December 2011. All the baseline data in the present analysis also referred to the time when the 5-min electrocardiogram recording was done. Statistical analysis Statistical analysis was performed using SPSS for Windows software, version 12.0 (SPSS Inc., Chicago, IL). Measures of HRV were analyzed after logarithmic transformation. All data were expressed as mean ± SD for continuous variables or percentages for categorical variables. To compare different groups, unpaired Student’s t-test was used for continuous variables as appropriate, the Chi-square test was used for categorical variables. Survival analysis was performed using a Cox proportional hazards model. Variables that achieved statistical significance in the univariate analysis were subsequently included in a multivariate analysis using a stepwise Cox regression procedure. Kaplan–Meier cumulative survival curves were constructed and compared using the log rank test. All tests were two-sided, a p-value below 0.05 was considered significant.
Results Baseline clinical data and outcomes Eighty-one patients who met the inclusion criteria were included in the present study. The demographic characteristic of the study population is shown in Table 1. During the 4-year follow-up period (mean 43.78 ± 14.77 months), 25 patients died (two sudden death, two acute myocardial infarctions, six fatal strokes, three malignant neoplasms, four multiple organ failure and eight severe infection), four patients were transferred to HD (three because of peritonitis and one because of economical reason). The prognostic factors for mortality Baseline parameters of patients grouped by survival status are shown in Table 2. Compared with the patients who survived during follow-up, those who died were older and had lower
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levels of urine volume, DBP, serum potassium, ICW, GFR and rKt/V. There were no significant differences between the two groups on gender, body mass index (BMI), PD duration, diabetes, SBP, creatinine, blood urea nitrogen (BUN), PEW, hemoglobin, albumin, ECW, TBW, hs-CRP, parathyroid hormone (PTH), high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, triglycerides,
Table 1. Demographic characteristics of the study population.
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Variable
Result
Number of patients Number of males (%) Age (years) Time on PD (months) Primary disease, n (%) Glomerulonephritis Diabetic nephropathy Hypertensive nephropathy Interstitial nephritis Polycystic kidney disease Other or unknown Diabetes, n (%) Protein–energy wasting, n (%)
81 41 (50.61) 65.25 ± 11.97 14 (range 3–99) 22 23 13 13 2 8 26 13
(27.16) (28.40) (16.05) (16.05) (2.47) (9.88) (32.10) (18.31)
pKt/V, tKt/V, UF, TFR, use of beta-blocker and angiotensinconverting enzyme inhibitors (ACEI). The analysis of HRV measures in peritoneal dialysis patients grouped by survival status is shown in Table 3. In the group of non-survival, rMSSD and SDSD were higher, the normalized LF and LF/HF were lower and the normalized HF was higher as compared with the survival group. There were no significant differences in other indices of HRV between the two groups. The univariate analysis of the relative risk for mortality is shown in Tables 4 and 5. Elder, lower urine volume, rKt/V, serum potassium, ICW and higher hs-CRP are all risks for high mortality. Among HRV measures, a lower normalized LF, LF/HF and a higher rMSSD, SDSD were related to high mortality. Multivariate Cox regression analysis showed that urine volume, hs-CRP and LF/HF were three independent prognostic factors for death during the follow-up (Table 6). Figure 1 shows the comparison of survival rate of patients with different LF/HF values (grouped by median of LF/HF, LF/HF 41.56 vs. 1.56). The 4-year mortality rate for the patients who had an LF/HF of 41.56 or 1.56 was 17.5% or 43.9%, respectively. Lower LF/HF (1.56) was a strong predictor for high mortality. Figure 2 shows that patients with
Table 2. Clinical features of patients grouped by survival status.
Age (years) Male, n (%) BMI Time on PD (months) Diabetes, n (%) Hypertension, n (%) SBP DBP Hemoglobin (g/L) BUN (mmol/L) Creatinine (mmol/L) Albumin (g/L) Serum potassium (mmol/L) hs-CRP (mg/L) PTH (pg/mL) PEW, n (%) HDL cholesterol (mg/dL) LDL cholesterol (mg/dL) Triglycerides (mg/dL) Urine volume (mL/day) UF(mL/day) TFR (mL/day) GFR (mL/min) rKt/V pKt/V tKt/V ECW (L) ICW (L) TBW (L) ACEI, n (%) Beta-blocker, n (%)
Survival (n ¼ 56)
Non-survival (n ¼ 25)
p
60.04 ± 12.23 30 (30/56) 23.83 ± 3.97 21.15 ± 16.62 17 (17/56) 28 (28/52) 136.15 ± 24.84 82.65 ± 13.15 115.92 ± 19.25 21.15 ± 5.49 853.02 ± 264.87 38.22 ± 4.13 4.46 ± 0.79 1.07 ± 0.982 206.29 ± 181.54 9 (9/49) 1.24 ± 0.401 3.02 ± 0.95 2.36 ± 1.43 609.41 ± 492.35 424.98 ± 310.15 1019.35 ± 377.48 2.25 ± 2.10 0.58 ± 0.50 1.32 ± 0.43 1.91 ± 0.48 15.67 ± 3.58 13.17 ± 4.04 29.06 ± 6.95 15 (37.5) 26 (40.5)
68.52 ± 9.03 11 (11/26) 24.80 ± 3.05 20.63 ± 25.87 9 (9/25) 12 (12/23) 132.52 ± 26.87 73.91 ± 11.67 113.57 ± 21.68 18.90 ± 5.96 779.43 ± 218.02 36.83 ± 3.55 4.04 ± 0.97 6.66 ± 17.58 204.98 ± 222.64 4 (4/22) 1.20 ± 0.35 3.44 ± 1.45 2.74 ± 2.12 231.43 ± 308.37 595.00 ± 391.32 868.96 ± 396.33 1.26 ± 1.70 0.27 ± 0.35 1.49 ± 0.41 1.80 ± 0.47 15.12 ± 3.51 11.04 ± 3.81 26.16 ± 6.92 3 (21.4) 8 (23.8)
0.004 0.426 0.298 0.915 0.415 0.894 0.571 0.008 0.638 0.106 0.232 0.164 0.048 0.056 0.919 0.606 0.709 0.173 0.394 0.000 0.061 0.113 0.047 0.002 0.067 0.381 0.525 0.029 0.088 0.272 0.600
Notes: BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; hs-CRP, high-sensitivity C-reactive protein; PEW, protein–energy wasting; PTH, parathyroid hormone; HDL, high density lipoprotein; LDL, low density lipoprotein; UF, ultrafiltration; TFR, total fluid removal; GFR, glomerular filtration rate; pKt/V, peritoneal Kt/V; rKt/V, renal Kt/V; tKt/V; total Kt/V (rKt/V + pKt/V); ECW, extracellular water; ICW, intracellular water; TBW, total body water; ACEI, angiotensin converting enzyme inhibitors.
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DOI: 10.3109/0886022X.2015.1061729
Table 3. HRV measures of patients grouped by survival status.
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SDNN (ms) rMSSD (ms) SDSD (ms) pNN50 TP power (ms2) VLF (ms2) LF (ms2) LFnor power (nu) HF power (ms2) HFnor power (nu) LF/HF
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Table 6. Multivariate Cox regression analysis of clinical and HRV measures in relation to death.
Survival (n ¼ 56)
Non-survival (n ¼ 25)
p
36.12 ± 39.77 36.12 ± 39.77 56.55 ± 78.05 11.12 ± 21.75 1826.09 ± 4944.06 379.27 ± 698.52 499.41 ± 1415.07 54.12 ± 23.64 715.68 ± 2297.69 30.75 ± 14.83 2.82 ± 2.77
57.44 ± 48.43 57.44 ± 48.43 104.72 ± 104.16 13.00 ± 21.97 2996.92 ± 3776.97 433.40 ± 551.27 588.76 ± 821.43 36.48 ± 21.03 1372.28 ± 1879.91 41.68 ± 13.93 1.21 ± 1.19
0.061 0.046 0.046 0.721 0.295 0.733 0.770 0.001 0.214 0.003 0.000
All-cause death RR (95% CI) Age (per 1 year increase) hs-CRP (per 1 mg/L increase) Urine volume (per 1 mL increase) LF/HF (per 1 increase)
1.054 1.035 0.998 0.701
(1.009–1.101) (1.009–1.061) (0.997–1.000) (0.501–0.979)
p 0.012 0.010 0.024 0.028
Notes: hs-CRP, high-sensitivity C-reactive protein; LF, low frequency; HF, high frequency.
Notes: SDNN, SD of all normal-to-normal NN intervals; rMSSD, the square root of the mean of the squared differences between adjacent normal NN intervals; SDSD, the SD of differences between adjacent NN intervals; pNN50, the percentage of differences between adjacent normal NN intervals that were 450 ms computed; TP, total power; VLF, very low frequency; LF, low frequency; HF, high frequency.
Table 4. Univariate Cox regression analysis of clinical characteristics related to death. RR (95% CI) Age (per 1 year increase) Male versus female Diabetes versus non-diabetes PEW (by SGA) versus no PEW Potassium (per 1 mmol/L increase) Serum albumin (per 1 g/L increase) serum urea (per 1 mmol/L increase) Hemoglobin (per 1 g/L increase) Beta-blocker versus no beta-blocker ACEI versus no ACEI SBP (per 1 mmHg increase) DBP (per 1 mmHg increase) ICW (per 1 L increase) Urine volume (per 1 mL increase) rKt/V (per 1 increase) PTH (per 1 pg/mL increase) hs-CRP (per 1 mg/L increase)
1.061 1.504 1.558 1.412 0.501 0.928 0.924 0.996 0.441 0.781 0.994 0.952 0.880 0.998 0.195 1.000 1.054
(1.016–1.109) (0.650–3.479) (0.673–3.610) (0.471–4.223) (0.270–0.900) (0.843–1.022) (0.811–1.007) (0.975–1.017) (0.146–1.331) (0.521–1.233) (0.977–1.011) (0.920–0.985) (0.786–0.986) (0.996–0.999) (0.057–0.675) (0.997–1.002) (1.023–1.085)
p 0.007 0.340 0.301 0.538 0.021 0.128 0.070 0.695 0.146 0.272 0.492 0.070 0.028 0.004 0.010 0.769 0.000
Figure 1. Kaplan–Meier survival curves for death in patients dichotomized by the median values of LF/HF. LF, low frequency; HF, high frequency.
Notes: PEW, protein–energy wasting; ACEI, angiotensin converting enzyme inhibitors; SBP, systolic blood pressure; DBP, diastolic blood pressure; ICW, intracellular water; rKt/V, renal Kt/V; PTH, parathyroid hormone; hs-CRP, high-sensitivity C-reactive protein.
Table 5. Univariate Cox regression analysis of heart rate variability measures in relation to death. RR (95% CI) SDNN (per 1 ms increase) rMSSD (per 1 ms increase) SDSD (per 1 ms increase) TP (per 1 ms2 increase) LF (per 1 ms2 increase) LF norm (per 1 nu increase) HF (per 1 ms2 increase) HF norm (per 1 nu increase) LF/HF (per 1 increase)
1.007 1.005 1.004 1.162 1.005 0.977 1.122 1.148 0.685
(1.000–1.014) (1.000–1.010) (1.000–1.007) (0.903–1.496) (0.794–1.273) (0.961–0.993) (0.936–1.346) (0.851–1.549) (0.496–0.947)
p 0.060 0.039 0.039 0.244 0.966 0.004 0.213 0.366 0.022
Notes: SDNN, SD of all normal-to-normal NN intervals; rMSSD, the square root of the mean of the squared differences between adjacent normal NN intervals; SDSD, the SD of differences between adjacent NN intervals; pNN50, the percentage of differences between adjacent normal NN intervals that were 450 ms computed; TP, total power; VLF, very low frequency; LF, low frequency; HF, high frequency.
Figure 2. Kaplan–Meier survival curves for cardiac death in patients with a urine volume of 4100 versus 100 mL/L.
urine volume of 4100 mL per day had significantly better survival. Figure 3 shows that the inflamed patients who were defined as hs-CRP values higher than 3 mg/L had significantly higher mortality as compared to the non-inflamed patients.
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Figure 3. Kaplan–Meier survival curves for death in patients with a hsCRP 43 mg/L versus 3 mg/L. hs-CRP, high-sensitivity C-reactive protein.
Discussion In this prospective study, we examined the prognostic value of 5-min HRV measures in 81 chronic peritoneal dialysis patients with a follow-up period of 43.78 ± 14.77 months. A mortality predictive effect of HRV indictors especially LF/HF values was observed by the present study even with adjustment of other confounding factors. To our knowledge this is the first report investigating the prognostic value of HRV in PD patients. The abnormalities of HRV in patients with renal impairment have been documented by pervious study. It has been found that chronic PD and HD patients had reduced HRV as compared to the healthy controls.16,17 Furuland et al. showed that CKD patients not yet on dialysis had a reduced HRV (both time domain and frequency domain) as compared with a reference group without impaired renal function.18 Chandra et al. found that lower HRV (frequency domain) was independently associated with higher risk of CVD, the composite of CVD/death and ESRD in non-dialysis CKD patients.14 Fukuta et al. found that HRV is reduced in chronic HD patients compared with healthy individuals, further, those patients with greatest reduction in this variable also have the greatest risk for cardiac death.13 Hayano et al. showed that HRV has an independent prognostic value in chronic HD patients and identifies an increased risk for all-cause and sudden death,19 and Oikawa et al. reported that decreased HRV (SDNN) on 24-h ambulatory ECG was an independent predictor of all-death and cardiovascular death in chronic HD patients.20 However, this predictive value of HRV in PD patients has not been elucidated previously. Thus, our study added valuable evidence in this area. In our study, a link between decreased LF/HF and high mortality was discovered, which is consistent with a recent study showing that the low LF/HF has a prognostic value of CVD and ESRD in non-dialysis CKD patients,14 and this observation has been confirmed by Furuland et al.;18 a decrease in LF/HF can significantly predict cardiac death in CKD patients. Brotman et al.21 studied 13,241 adults (45–64-years old) followed for a median of 16 years in the Atherosclerosis Risk in Communities study and
Ren Fail, 2015; 37(7): 1132–1137
underwent 2-min ECG monitoring between follow-up periods to assess prognostic value of HRV. Multivariate Cox proportional hazard model revealed that low LF/HF and other HRV parameters could predict higher hospitalization and development of kidney disease, and the reduced LF/HF could predict cardiac death in chronic HD patients.13 With these results it is evident that a reduced LF/HF has an independent prognostic value in communities, CKD and chronic HD patients and identifies patients with an increased risk for hospitalization, development of kidney disease, cardiac-death and all-cause death. The potential mechanism to explain the relationship between reduced LF/HF and mortality remains unclear. As the LF/HF is considered by some investigators to mirror sympatho/vagal balance or to reflect sympathetic modulations, we speculated that this impaired autonomic nerve system in PD patients may result in inefficiency of the natural stress coping system and thus subject the patients to high mortality when comorbidity occurs. Further study is warranted in this field. In line with previous studies, our study also showed that anuric status and hs-CRP were strong predictors of death in PD patients.8,22 It is interesting to note that our study found that the non-survival group had a lower ICW as compared to the survival group. ICW reflects water content in the body cell mass.23 Changes in body protein mainly occur in the cellular compartment;24 consequently, the alterations in body protein are generally accompanied by changes in ICW.25 A decreased ICW may indicate the loss of intracellular proteins caused by protein–energy malnutrition, which is common in PD patients. Our study found that rMSSD and SDSD were almost the same, since many studies showed that the time domain measures correlated closely with others,15,26 our observations seem basically in line with those studies. We conclude that decrease of LF/HF which shows impaired sympathetic nerve modulation is an independent predictor of mortality in PD patients.
Declaration of interest There are no financial conflict of interest to report.
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