Background/Aims: While systolic blood pressure variability (SBPV) is an independent risk factor for mortality in the general population, its association with outcomes in hemodialysis patients has been less well-investigated. Methods: In this retrospective study, we enrolled 99 eligible HD patients from 2006 to 2016. Predialysis blood pressure measurements obtained over 1-year period were used to determine each patient’s BPV. The standard deviation (SD), the coefficient of variation (CV) and the variation independent of the mean (VIM) were used as metrics of BPV. Results: During a median follow-up period of 68 months, 52 patients died, and cardiovascular disease (31.3%) was the primary cause of death in these patients. After adjusting for covariates, the hazard ratios (HRs) for all-cause and cardiovascular mortality were 1.80 (95% confidence interval (CI) 1.11-2.92) and 1.71 (95% CI 1.01-2.90), respectively, for a one percent increase in CV. Variability in the volume removed per session and predialysis serum albumin and calcium levels were identified as factors associated with BPV. Conclusion: In this study, we demonstrate that greater variability in predialysis SBP is associated with long-term mortality in hemodialysis patients. Controlling volume variation, avoiding hypoalbuminemia and reducing blood calcium levels might reduce SBP variability and thereby improve prognoses in these patients.

Blood pressure variability (BPV) reflects fluctuation in blood pressure and can be divided into the two following types: short-term BPV and long-term BPV. The former includes beat-to-beat, minute-to-minute, hour-to-hour, and day-to-night changes. The latter includes variations in BP that occur over more prolonged periods of time, such as days, weeks, months, seasons, and even years [1]. A growing number of studies performed in the general population have shown that BPV is a risk factor for target organ damage and mortality, independent of absolute BP level [2-5].

In hemodialysis (HD) patients, while the effects of short-term BPV on prognosis have been extensively studied [6-10], the effects of long-term BPV are less well-investigated [11-13]. A few previous studies found that predialysis BPV was associated with mortality in HD patients. However, most of these studies [11, 12] used blood pressure measurements obtained over 3 months to calculate BPV which didn’t consider seasonal changes in BP, and generally lacked long-term follow-up [11, 13]. We therefore sought to investigate the association between predialysis BPV measurements obtained during a full year and all-cause and cardiovascular mortality over a 10-year follow-up period in a cohort study of HD patients.

Patients

We performed a longitudinal cohort study at the hemodialysis center of Peking Union Medical College Hospital (PUMCH). ESRD patients were included if they were over 18 years old and had received regular hemodialysis for more than 90 days prior to January 1, 2006. Patients with any one of the following criteria were excluded: (1) died or received a kidney transplant or switched to peritoneal dialysis or transferred to a different renal unit during 2006; and (2) incomplete dialysis records (no data for >3 months).

Blood pressure measurement and blood pressure variability

Blood pressure was measured using automated oscillometric devices (Philips C3 patient monitor, Philips) as recommended by the NKF K/DOQI guidelines while the patient was in a seated position. Measurements were obtained immediately before, after, and during each dialysis session from January 1, 2006 to December 31, 2006. Raw BPs were transformed into the following 3 candidate BPV metrics, which are widely used in BPV studies [3, 4, 12, 14]: the standard deviation (SD), the coefficient of variation (CV), and the variation independent of the mean (VIM). VIM is a transformation of the standard deviation that is uncorrelated with mean BP and is calculated as follows:

VIM=k x SD/x̅m

where m is calculated as follows by fitting a power model: SD=constant × x̅m and k=mean(mean(SBP))m. In this study, m=1.145, k=314.691.

Baseline covariates and follow-up

Data for other baseline covariates were collected from clinical records. Demographic factors included age and sex. Comorbid diseases included hypertension, diabetes and existing cardiovascular disease. Dialysis-related variables included dialysis vintage, vascular access type, volume removed per dialysis session, equilibrated Kt/V and normalized protein catabolic rate (nPCR). Biochemical covariates included the levels of hemoglobin, serum albumin, creatinine, phosphate, calcium, potassium, sodium, low density lipoprotein (LDL), triglyceride (TG), total cholesterol (TC) and intact PTH (iPTH). All laboratory values were measured using automated and standardized methods at a centralized laboratory. Most laboratory values were measured monthly and included serum creatinine, phosphate, calcium, potassium, and sodium levels. Serum albumin, LDL, TG, TC and iPTH levels were measured at least quarterly. Hemoglobin levels were measured at least monthly in essentially all patients and biweekly in some patients. Most blood samples were collected predialysis with the exception of urea, which was collected postdialysis to calculate urea kinetics. The averaged or median values during the exposure period served as the baseline data.

Follow-up and Outcome

The follow-up period was from January 1, 2007 to December 31, 2016. The primary end-point was all-cause mortality, and the secondary end-point was cardiovascular mortality. The date of death and attributed cause of death were obtained from clinical records for those who died in hospital. For patients who died out of hospital, we interviewed family members by telephone to determine a detailed cause. Cardiovascular deaths were defined as those that could be attributed to ischemic heart disease, heart failure/pulmonary edema, arrhythmia, sudden death, cerebral infarction and cerebral hemorrhage. Follow-up was censored in patients who received a kidney transplant, switched to peritoneal dialysis or transferred to a different renal unit.

The study protocol was approved by the Institutional Review Board of PUMCH, and all methods were performed in accordance with the relevant guidelines and regulations. Informed consent was not required in this retrospective study, and our IRB committee approved this. All individual information was securely protected and was made available to only the investigators.

Statistical Analysis

The Kolmogorov–Smirnov test was used to determine whether variables were normally distributed. A p-value of > 0.05 was required to assume a normal distribution. Continuous variables were described in terms of their mean ±SD or median (interquartile range). Categorical variables were described in terms of their frequency. Patients were divided into two groups with BP variability dichotomized at the median. Variables were compared between the two groups using Pearson’s chi-squared tests for categorical variables, independent sample t-tests for normally distributed continuous variables and Mann-Whitney rank sum tests for abnormally distributed continuous variables. Kaplan-Meier curves and log-rank tests were used to compare survival between the two BPV groups. Cox proportional hazard analysis was used to evaluate the associations between BPV and study outcomes, including all-cause and cardiovascular mortality, initially without adjustment. A multivariate Cox regression analysis was then performed with adjustment for the following variables, which were plausibly associated with both exposure and outcome: age, dialysis vintage, diabetes, existing cardiovascular disease, mean predialysis SBP, mean predialysis weight, anti-hypertensives, predialysis blood level of calcium, phosphorus, albumin, hemoglobin, and LDL. The associations between BPV and other covariates were tested using a Pearson’s rank correlation test for normally distributed continuous variables or a Spearman’s correlation test for abnormally distributed continuous variables and categorical variables. A multiple linear regression analysis was then performed for the three BPV metrics, and variables with p < 0.1 were included based on stepwise elimination of data. A two-sided p-value less than 0.05 was considered to indicate statistical significance. We performed all analyses using SPSS version 22.0 (IBM SPSS statistics, Armonk, New York, USA).

Patient demographic

A flow diagram of the enrollment process is shown in Fig. 1. After we screened for all adult patients (n=155) who received regular hemodialysis for at least 3 months in 2006, a total of 99 patients were included in this study. Table 1 presents the overall baseline characteristics of the study patients. The patients with BPV were dichotomized at the median. The mean age of the study patients was 61.1±12.4 years old, and 65.7% of the patients were female. Glomerulonephropathy was the primary cause of end-stage renal disease (ESRD) (30.3%) and was followed by hypertension (21.2%) and diabetes (18.2%). At baseline, 88.9% of patients had hypertension. The median baseline dialysis vintage was 49 months. The average baseline predialysis systolic blood pressure (SBP) was 152±17 mmHg.

Table 1.

Demographic and clinical characteristics of groups above and below the median for pre-dialysis SBPCV. Values are expressed as the mean ± SD, number (percentage) or median [i.e., 25th and 75th percentiles]. Abbreviations: ESRD, end-stage renal disease; GN, glomerulonephropathy; AVF, arteriovenous fistula; AVG, arteriovenous grafts; *p-value <0.05

Demographic and clinical characteristics of groups above and below the median for pre-dialysis SBPCV. Values are expressed as the mean ± SD, number (percentage) or median [i.e., 25th and 75th percentiles]. Abbreviations: ESRD, end-stage renal disease; GN, glomerulonephropathy; AVF, arteriovenous fistula; AVG, arteriovenous grafts; *p-value <0.05
Demographic and clinical characteristics of groups above and below the median for pre-dialysis SBPCV. Values are expressed as the mean ± SD, number (percentage) or median [i.e., 25th and 75th percentiles]. Abbreviations: ESRD, end-stage renal disease; GN, glomerulonephropathy; AVF, arteriovenous fistula; AVG, arteriovenous grafts; *p-value <0.05
Fig. 1.

Flow diagram for enrollment. Abbreviations: HD, hemodialysis and PUMCH, Peking Union Medical College Hospital. * Most patients were in hospital patients who needs emergent or urgent dialysis and continued HD in local HD centers after discharge.

Fig. 1.

Flow diagram for enrollment. Abbreviations: HD, hemodialysis and PUMCH, Peking Union Medical College Hospital. * Most patients were in hospital patients who needs emergent or urgent dialysis and continued HD in local HD centers after discharge.

Close modal

Blood pressure variability and correlation factors

The mean standard deviation (SD), the coefficient of variation (CV) and the variation independent of the mean (VIM) of predialysis SBP were 16.4±4.2 mmHg, 10.7±2.3% and 16.3±3.5, respectively. Each pair of parameters among these three metrics was closely correlated with each other (SD and CV, r=0.887, p<0.001; CV and VIM, r=0.996, p<0.001; and SD and VIM, r=0.846, p<0.001). Diabetes was more common in patients with CV of SBP (SBPCV)above the median than in those with a lower CV of SBP, but there was no other difference between the two groups (Table 1). We evaluated the associations between patient parameters and BPV metrics as continuous dependent variables. The mean of SBP, the SD of the volume removed per session, hypertension, diabetes and serum albumin levels were associated with the SD of SBP (SBPSD). The SD of the volume removed per session, serum albumin levels and diabetes were associated with the CV of SBP. The SD of the volume removed per session, serum albumin levels, diabetes and hypertension were associated with VIM. A multiple linear regression analysis revealed that the mean of SBP, the SD of the volume removed per session, serum albumin levels and hypertension remained associated with SBPSD, whereas the SD of the volume removed per session, serum albumin levels and calcium levels were associated with SBPCV and VIM (Table 2). Predialysis SBP showed the same trend in variation that was observed for the volume removed per session. Both of these were highest in winter and lowest in summer during the 1-year study period (Fig. 2). When the SBPCV for the first 3 months of 2006 was compared to the SBPCV for the full 1-year study period, the former was significantly lower than the latter (9.6±2.7% VS. 10.7±2.3%, p<0.001). The results were similar when we compared the SBPCV of the other three quarters to the SBPCV for the full 1-year study period (9.6±2.6% VS. 10.7±2.3%, p<0.001, 9.6±2.7% VS. 10.7±2.3%, p<0.001, and 9.6±2.7% VS. 10.7±2.3%, p<0.001, respectively).

Table 2.

Factors associated with predialysis systolic BPV. Abbreviations: SBPSD, standard deviation of systolic blood pressure; SBP, systolic blood pressure; SD, standard deviation; and SBPCV, the coefficient of variation of systolic blood pressure. Variables entered in the model include age, sex, dialysis vintage, accompanied by hypertension, accompanied by diabetes, mean predialysis systolic blood pressure, standard deviation of the volume removed per session, and blood levels of sodium, calcium, hemoglobin and albumin

Factors associated with predialysis systolic BPV. Abbreviations: SBPSD, standard deviation of systolic blood pressure; SBP, systolic blood pressure; SD, standard deviation; and SBPCV, the coefficient of variation of systolic blood pressure. Variables entered in the model include age, sex, dialysis vintage, accompanied by hypertension, accompanied by diabetes, mean predialysis systolic blood pressure, standard deviation of the volume removed per session, and blood levels of sodium, calcium, hemoglobin and albumin
Factors associated with predialysis systolic BPV. Abbreviations: SBPSD, standard deviation of systolic blood pressure; SBP, systolic blood pressure; SD, standard deviation; and SBPCV, the coefficient of variation of systolic blood pressure. Variables entered in the model include age, sex, dialysis vintage, accompanied by hypertension, accompanied by diabetes, mean predialysis systolic blood pressure, standard deviation of the volume removed per session, and blood levels of sodium, calcium, hemoglobin and albumin
Fig. 2.

Variation in predialysis SBP and volume removed per session throughout one full year.

Fig. 2.

Variation in predialysis SBP and volume removed per session throughout one full year.

Close modal

Association with Death

Over a median follow-up period of 68 months, 52 patients (52.5%) died, 2 patients (2.0%) received a kidney transplant, and 12 patients (12.0%) were transferred to a different dialysis center. Cardiovascular disease (31.3%) was the primary cause of death. The mortality rate was 92.7 deaths/1000 patient-years, and the median survival time was 89.7 months.

In an unadjusted analysis, a higher predialysis BPV was significantly associated with higher rates of all-cause mortality. A higher predialysis SBPSD and SBPCV were significantly associated with cardiovascular mortality. After adjusting for age, dialysis vintage, diabetes, existing cardiovascular disease, mean predialysis SBP, mean predialysis weight, anti-hypertensives, predialysis blood level of calcium, phosphorus, albumin, hemoglobin, and LDL, we found that all three of the BPV metrics remained significantly associated with higher rates of all-cause and SBPCV and VIM were independent risk factors for cardiovascular mortality (Table 3).

Table 3.

Prognostic significance of pre-dialysis SBP variability for all-cause mortality and cardiovascular mortality. Abbreviations: HR, hazard ratio; CI, confidence interval; SD, standard deviation; CV, coefficient of variation; and VIM, variation independent of the mean. # Adjusted for age, dialysis vintage, diabetes, existing cardiovascular disease, mean predialysis SBP, mean predialysis weight, anti-hypertensives, predialysis blood level of calcium, phosphorus, albumin, hemoglobin, and LDL

Prognostic significance of pre-dialysis SBP variability for all-cause mortality and cardiovascular mortality. Abbreviations: HR, hazard ratio; CI, confidence interval; SD, standard deviation; CV, coefficient of variation; and VIM, variation independent of the mean. # Adjusted for age, dialysis vintage, diabetes, existing cardiovascular disease, mean predialysis SBP, mean predialysis weight, anti-hypertensives, predialysis blood level of calcium, phosphorus, albumin, hemoglobin, and LDL
Prognostic significance of pre-dialysis SBP variability for all-cause mortality and cardiovascular mortality. Abbreviations: HR, hazard ratio; CI, confidence interval; SD, standard deviation; CV, coefficient of variation; and VIM, variation independent of the mean. # Adjusted for age, dialysis vintage, diabetes, existing cardiovascular disease, mean predialysis SBP, mean predialysis weight, anti-hypertensives, predialysis blood level of calcium, phosphorus, albumin, hemoglobin, and LDL

This study showed that predialysis blood pressure variability (BPV) was an independent risk factor for all-cause and cardiovascular mortality in hemodialysis patients. Variability in the volume removed per session and predialysis serum albumin and calcium levels were associated with predialysis BPV.

Increased long-term BPV was found to be associated with stroke [4], cardiovascular events [2], arterial stiffnes s [5] and decreased GFR [3] and is therefore considered an independent risk factor for all-cause and cardiovascular mortality in non-hemodialysis patients. These remain, however, less well-investigated in hemodialysis patients. SD, CV and VIM were recognized as the most common BPV metrics [3, 4, 12, 14],but no evidence supporting which one is better. The ROC curves showed no difference among the three metrics in predicting the mortality (data not shown). Because SD is often strongly correlated with mean BP, it displayed minimal discriminatory capacity for BP fluctuations and ambient BP level [15]. VIM required a complex calculation, which limited its clinical application to some extent. So CV might be the most practical metric marker of BPV in dialysis patients [14]. In this study, predialysis SBPCV was 10.7%, which is close to findings reported in Japanese (10.1%) [13] and French (10.43%) hemodialysis patients [14] but higher than the reported visit-to-visit BPV in the general population (6.8%) [16].

We found that predialysis BPV over a 1-year period was an independent predictor of long-term mortality in hemodialysis patients. The relationship between predialysis BPV and outcomes in hemodialysis patients has been investigated in several previous studies in which BPV was reported to be associated with all-cause and cardiovascular mortality [12, 14, 17]. However, these studies used blood pressure measurements obtained during a period consisting of a consecutive 3 months or less to determine BPV. Because we know that blood pressure fluctuates with season changes [18, 19] and our study also shows that BPV measurements produce different results when obtained over 3 months than when obtained over 12 months, we used a consecutive 12 months of predialysis blood pressure measurements to determine BPV. Tozawa et al. [13] also investigated predialysis SBPCV over a 1-year period to predict prognoses in hemodialysis patients, but they did not find an association between BPV and cardiovascular mortality because their follow-up period was short (38 months). We followed our patients for 10 years, which is so far as we know the longest reported follow-up period. Our data indicate that variability in predialysis SBP is associated with all-cause and cardiovascular mortality in HD patients when the follow-up period is long.

In our study, we found that variability in the volume removed per session was positively correlated with predialysis SBP variability. This result indicates that it is important to control blood volume in hemodialysis patients. Few studies have explored the relationship between changes in volume and BPV, especially long-term BPV, in hemodialysis patients, and these studies have resulted in inconsistent conclusions [20-22]. For example, Shafi et al. [22] investigated long-term BPV and volume changes in HD patients and reported that a greater amount of fluid removal was associated with lower variability in predialysis SBP. They proposed that this result was attributable to the finding that higher fluid removal benefits BP control by maintaining dry weight. However, greater fluid removal is not equivalent to the attainment of dry weight. Despite finding that the percentage of patients who attained dry weight was different among the three BPV groups, there was no difference in the relative amount of fluid removal per session in their study. Because fluid overload is an important factor that contributes to uncontrolled hypertension in dialysis patients and because the BP of most dialysis patients is volume-dependent, other studies have produced conflicting results. Flythe et al. [20] found that higher variability in intradialytic SPB was associated with greater dialytic fluid removal and rate. Kursat et al. [21] noted that daytime SBP variability was positively correlated with changes in body weight or the amount of ultrafiltration. In our study, we did not find that there was a relationship between BPV and the absolute volume removed per session, but we did find that variability in the volume removed per session was positively correlated with predialysis variability in SBP. Because volume removed per session was correlated with interdialytic weight gain (IDWG), controlling IDWG maybe a good method for achieving a smaller BPV. Most of the HD patients are dialyzed three times each week with one-day or two-day interval. In order to minimize the variation of volume removed per session patients should be asked to restrict volume ingested especially during the two-day interval.

In addition to volume removal, we found that predialysis SBP variability was also associated with additional modifiable factors. We first found that serum albumin levels were negatively correlated with predialysis SBP variability. Among HD patients, BPV is believed to result partially as a result of arterial stiffness and atherosclerosis [23], which are closely associated with low serum albumin levels and inflammation. Some authors have used the terminology MIA (malnutrition, inflammation and atherosclerosis) syndrome to emphasize the importance of atherosclerosis that is a consequence of the malnutrition-inflammation complex [24, 25]. Serum albumin levels are the most commonly used nutritional marker in hemodialysis patients. These levels also reflect the initiation of inflammatory processes [24, 26]. Inflammatory cytokines play important roles in MIA syndrome and are important contributors to atherosclerosis [25, 27]. Pulse wave velocity (PWV), a surrogate marker of arterial stiffness, has been shown to be negatively correlated with serum albumin levels [28]. The underlying cause of hypoalbuminemia in each HD patient needs to be determined. Nutritional intervention and treating the underlying inflammation might reduce BPV variability.

In our study, increased serum calcium levels were another important predictor of higher BPV. One possible explanation for this finding may be that calcium deposits in vascular structures cause vascular calcification, which was reported to share a significantly positive association with arterial stiffness in both a healthy population [29] and ESRD patients [30]. This finding deserves special consideration given the high incidence of chronic kidney disease-mineral and bone disorder (CKD-MBD) and the widespread use of calcium-based binders in hemodialysis patients. A growing number of studied have demonstrated that higher calcium levels are associated with higher mortality [31, 32]. It is possible that the impact of calcium levels on BPV is a mechanism underlying this relationship. This study indicated that lower serum calcium levels might improve the survival of dialysis patients by reducing the SBP variability. So lower concentration of calcium in dialysate and less calcium-based phosphate binders would be recommended to these patients when necessary.

There are several limitations to this study. First, the relatively small sample size is a major limitation. However, for each patient, we included predialysis SBP measurements that were obtained over a full year, and as a result, we analyzed 14703 dialysis sessions in total. Secondly, this is a retrospective and observational study. We could not prove a causal relationship between BPV and mortality. Thirdly, the individuals who participates in this study were prevalent dialysis patients, not incident dialysis patients. The history of hemodialysis prior the start of this study may therefore have impacted the survival of the patients. To reduce the impact of this factor, we included dialysis vintage as one of the adjustments in our analysis of the effect of BPV on mortality. Finally, patients who died during the one-year period for BP measurements collection were excluded from this study which may cause survival selection bias.

In this study, we demonstrate that greater predialysis SBP variability over the course of a full year is associated with long-term mortality in prevalent hemodialysis patients. Controlling volume variation, avoiding hypoalbuminemia and reducing blood calcium levels may reduce predialysis SBP variability. Further prospective studies are needed to confirm and generalize these findings and to determine whether BPV can be used as an important therapeutic target in hemodialysis patients.

The authors declare they have no competing financial interests and nothing to disclose.

This study was supported by grants from the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS 2016-12 M-2-004 to Limeng Chen), a “Xiehe Scholar” Distinguished Professor grant (to Limeng Chen), and the National Natural Science Foundation of China (81470937 and 81641024 to C.L).

1.
Mancia G: Short- and long-term blood pressure variability: present and future. Hypertension 2012; 60: 512-517.
2.
Chang TI, Tabada GH, Yang J, Tan TC, Go AS: Visit-to-visit variability of blood pressure and death, end-stage renal disease, and cardiovascular events in patients with chronic kidney disease. J Hypertens 2016; 34: 244-252.
3.
Chia YC, Lim HM, Ching SM: Long-Term Visit-to-Visit Blood Pressure Variability and Renal Function Decline in Patients With Hypertension Over 15 Years. J Am Heart Assoc 2016; 5:e003825.
4.
Rothwell PM, Howard SC, Dolan E, O’Brien E, Dobson JE, Dahlof B, Sever PS, Poulter NR: Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet 2010; 375: 895-905.
5.
Tedla YG, Yano Y, Carnethon M, Greenland P: Association Between Long-Term Blood Pressure Variability and 10-Year Progression in Arterial Stiffness: The Multiethnic Study of Atherosclerosis. Hypertension 2017; 69: 118-127.
6.
Erturk S, Ertug AE, Ates K, Duman N, Aslan SM, Nergisoglu G, Diker E, Erol C, Karatan O, Erbay B: Relationship of ambulatory blood pressure monitoring data to echocardiographic findings in haemodialysis patients. Nephrol Dial Transplant 1996; 11: 2050-2054.
7.
Goldsmith DJ, Covic AC, Venning MC, Ackrill P: Ambulatory blood pressure monitoring in renal dialysis and transplant patients. Am J Kidney Dis 1997; 29: 593-600.
8.
Liu M, Takahashi H, Morita Y, Maruyama S, Mizuno M, Yuzawa Y, Watanabe M, Toriyama T, Kawahara H, Matsuo S: Non-dipping is a potent predictor of cardiovascular mortality and is associated with autonomic dysfunction in haemodialysis patients. Nephrol Dial Transplant 2003; 18: 563-569.
9.
Mitsuhashi H, Tamura K, Yamauchi J, Ozawa M, Yanagi M, Dejima T, Wakui H, Masuda S, Azuma K, Kanaoka T, Ohsawa M, Maeda A, Tsurumi-Ikeya Y, Okano Y, Ishigami T, Toya Y, Tokita Y, Ohnishi T, Umemura S: Effect of losartan on ambulatory short-term blood pressure variability and cardiovascular remodeling in hypertensive patients on hemodialysis. Atherosclerosis 2009; 207: 186-190.
10.
Tripepi G, Fagugli RM, Dattolo P, Parlongo G, Mallamaci F, Buoncristiani U, Zoccali C: Prognostic value of 24-hour ambulatory blood pressure monitoring and of night/day ratio in nondiabetic, cardiovascular events-free hemodialysis patients. Kidney Int 2005; 68: 1294-1302.
11.
Brunelli SM, Thadhani RI, Lynch KE, Ankers ED, Joffe MM, Boston R, Chang Y, Feldman HI: Association between long-term blood pressure variability and mortality among incident hemodialysis patients. Am J Kidney Dis 2008; 52: 716-726.
12.
Selvarajah V, Pasea L, Ojha S, Wilkinson IB, Tomlinson LA: Pre-dialysis systolic blood pressure-variability is independently associated with all-cause mortality in incident haemodialysis patients. PLoS One 2014; 9:e86514.
13.
Tozawa M, Iseki K, Yoshi S, Fukiyama K: Blood pressure variability as an adverse prognostic risk factor in end-stage renal disease. Nephrol Dial Transplant 1999; 14: 1976-1981.
14.
Rossignol P, Cridlig J, Lehert P, Kessler M, Zannad F: Visit-to-visit blood pressure variability is a strong predictor of cardiovascular events in hemodialysis: insights from FOSIDIAL. Hypertension 2012; 60: 339-346.
15.
Rosner, B. Fundamentals of Biostatistics. 6th ed. Belmont, CA: Duxbury, an imprint of Thomson Brooks/ Cole. 2005, pp. 23–24.
16.
Juhanoja EP, Niiranen TJ, Johansson JK, Puukka PJ, Thijs L, Asayama K, Langen VL, Hozawa A, Aparicio LS, Ohkubo T, Tsuji I, Imai Y, Stergiou GS, Jula AM, Staessen JA, International Database on Home Blood Pressure in Relation to Cardiovascular Outcome I: Outcome-Driven Thresholds for Increased Home Blood Pressure Variability. Hypertension 2017; 69: 599-607.
17.
Chang TI, Flythe JE, Brunelli SM, Muntner P, Greene T, Cheung AK, Chertow GM: Visit-to-visit systolic blood pressure variability and outcomes in hemodialysis. J Hum Hypertens 2014; 28: 18-24.
18.
Hanazawa T, Asayama K, Watabe D, Hosaka M, Satoh M, Yasui D, Obara T, Inoue R, Metoki H, Kikuya M, Imai Y, Ohkubo T: Seasonal variation in self-measured home blood pressure among patients on antihypertensive medications: HOMED-BP study. Hypertens Res 2017; 40: 284-290.
19.
Usvyat LA, Carter M, Thijssen S, Kooman JP, van der Sande FM, Zabetakis P, Balter P, Levin NW, Kotanko P: Seasonal variations in mortality, clinical, and laboratory parameters in hemodialysis patients: a 5-year cohort study. Clin J Am Soc Nephrol 2012; 7: 108-115.
20.
Flythe JE, Kunaparaju S, Dinesh K, Cape K, Feldman HI, Brunelli SM: Factors associated with intradialytic systolic blood pressure variability. Am J Kidney Dis 2012; 59: 409-418.
21.
Kursat S, Ozgur B, Alici T: Effect of ultrafiltration on blood pressure variability in hemodialysis patients. Clin Nephrol 2003; 59: 289-292.
22.
Shafi T, Sozio SM, Bandeen-Roche KJ, Ephraim PL, Luly JR, St Peter WL, McDermott A, Scialla JJ, Crews DC, Tangri N, Miskulin DC, Michels WM, Jaar BG, Herzog CA, Zager PG, Meyer KB, Wu AW, Boulware LE, Investigators DENPOiESRDS: Predialysis systolic BP variability and outcomes in hemodialysis patients. J Am Soc Nephrol 2014; 25: 799-809.
23.
Kooman JP, Wijnen JA, Draaijer P, van Bortel LM, Gladziwa U, Peltenburg HG, Struyker-Boudier HA, van Hooff JP, Leunissen KM: Compliance and reactivity of the peripheral venous system in chronic intermittent hemodialysis. Kidney Int 1992; 41: 1041-1048.
24.
Locatelli F, Fouque D, Heimburger O, Drueke TB, Cannata-Andia JB, Horl WH, Ritz E: Nutritional status in dialysis patients: a European consensus. Nephrol Dial Transplant 2002; 17: 563-572.
25.
Stenvinkel P, Heimburger O, Lindholm B, Kaysen GA, Bergstrom J: Are there two types of malnutrition in chronic renal failure? Evidence for relationships between malnutrition, inflammation and atherosclerosis (MIA syndrome). Nephrol Dial Transplant 2000; 15: 953-960.
26.
Danielski M, Ikizler TA, McMonagle E, Kane JC, Pupim L, Morrow J, Himmelfarb J: Linkage of hypoalbuminemia, inflammation, and oxidative stress in patients receiving maintenance hemodialysis therapy. Am J Kidney Dis 2003; 42: 286-294.
27.
Stenvinkel P, Heimburger O, Paultre F, Diczfalusy U, Wang T, Berglund L, Jogestrand T: Strong association between malnutrition, inflammation, and atherosclerosis in chronic renal failure. Kidney Int 1999; 55: 1899-1911.
28.
Avramovski P, Janakievska P, Sotiroski K, Sikole A: Accelerated progression of arterial stiffness in dialysis patients compared with the general population. Korean J Intern Med 2013; 28: 464-474.
29.
Guo J, Fujiyoshi A, Willcox B, Choo J, Vishnu A, Hisamatsu T, Ahuja V, Takashima N, Barinas-Mitchell E, Kadota A, Evans RW, Miura K, Edmundowicz D, Masaki K, Shin C, Kuller LH, Ueshima H, Sekikawa A, Group EJS: Increased Aortic Calcification Is Associated With Arterial Stiffness Progression in Multiethnic Middle-Aged Men. Hypertension 2017; 69: 102-108.
30.
Sigrist MK, Taal MW, Bungay P, McIntyre CW: Progressive vascular calcification over 2 years is associated with arterial stiffening and increased mortality in patients with stages 4 and 5 chronic kidney disease. Clin J Am Soc Nephrol 2007; 2: 1241-1248.
31.
Lin YC, Lin YC, Hsu CY, Kao CC, Chang FC, Chen TW, Chen HH, Hsu CC, Wu MS, Taiwan Society of N: Effect Modifying Role of Serum Calcium on Mortality-Predictability of PTH and Alkaline Phosphatase in Hemodialysis Patients: An Investigation Using Data from the Taiwan Renal Registry Data System from 2005 to 2012. PLoS One 2015; 10:e0129737.
32.
Rivara MB, Ravel V, Kalantar-Zadeh K, Streja E, Lau WL, Nissenson AR, Kestenbaum B, de Boer IH, Himmelfarb J, Mehrotra R: Uncorrected and Albumin-Corrected Calcium, Phosphorus, and Mortality in Patients Undergoing Maintenance Dialysis. J Am Soc Nephrol 2015; 26: 1671-1681.
Open Access License / Drug Dosage / Disclaimer
This article is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND). Usage and distribution for commercial purposes as well as any distribution of modified material requires written permission. Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug. Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.