Open Access

Changes in the ankle-brachial blood pressure index among hemodialysis patients

  • Takayuki Abe1,
  • Shigeru Otsubo2, 3Email author,
  • Naoki Kimata4,
  • Tomoki Okajima1,
  • Yumi Otani1,
  • Jun Murakami1,
  • Iwakazu Kaneko1,
  • Naoko Miwa4,
  • Michio Mineshima1,
  • Ken Tsuchiya3, 4,
  • Kosaku Nitta3 and
  • Takashi Akiba5
Renal Replacement Therapy20162:40

DOI: 10.1186/s41100-016-0053-z

Received: 28 January 2016

Accepted: 20 May 2016

Published: 29 August 2016

Abstract

Background

The ankle-brachial blood pressure index (ABI) is an independent predictor of mortality in hemodialysis patients. In the present study, we investigated the factors that predict changes in the ABI in hemodialysis patients.

Methods

A total of 61 consecutive patients receiving maintenance hemodialysis who successfully underwent ABI examinations in 2005 and 2011 were enrolled in this study. The change in the ABI (2011 measurement versus 2005 measurement) was estimated. We set the baseline at 2011 and investigated the patient outcomes. Furthermore, we compared the change in the ABI and several clinical factors observed in 2005.

Results

The mean follow-up period was 3.1 ± 0.7 years. In the univariate Cox proportional hazard analysis, predictive variables for mortality included ABI (0.43 [0.25–0.64]; [per 0.1 increase]) and the change in the ABI (0.62 [0.49–0.79]; [per 0.1 increase]). Patient age was negatively correlated and the serum creatinine level was positively correlated with the change in the ABI (P = 0.030 and 0.022, respectively).

Conclusions

We confirmed that not only the value of the ABI itself but also the change in the ABI was a risk factor for mortality among hemodialysis patients. The change in the ABI was negatively correlated with age and was positively correlated with the serum creatinine level. Careful observation of the ABI is needed for old patients and patients with a low serum creatinine level.

Keywords

Ankle-brachial index Hemodialysis Peripheral artery disease

Background

Epidemiological and clinical studies of the general population have demonstrated that the presence of peripheral artery disease (PAD) is associated with an increased risk of myocardial infarction and stroke [1, 2]. PAD is a regular complication of hemodialysis patients [3, 4] and is associated with poor outcomes [49]. Clinically, the ankle-brachial blood pressure index (ABI) is highly correlated with PAD of the lower extremities [10], and ABI is an independent predictor of all-cause mortality in hemodialysis patients [69]. A recent study also revealed that not only the value of the ABI itself but also the rate of the reduction in the ABI was a risk factor for cardiovascular mortality [11]. In the present study, we investigated the effect of changes in the ABI on mortality and the factors that predict changes in the ABI in hemodialysis patients.

Methods

A total of 61 consecutive patients receiving maintenance hemodialysis who successfully underwent ABI examinations in both 2005 and 2011 were retrospectively enrolled in this study. All patients were not treated by interventional or surgical repair for PAD during 2005 and 2011. Clinical data including age, sex, durations of hemodialysis therapy, presence of diabetes mellitus and/or hypertension complications, and biological examinations were collected from the patient’s records. Hypertension was defined as a systolic blood pressure of 140 mmHg or higher, a diastolic blood pressure of 90 mmHg or higher, and/or the current use of antihypertensive drugs. Diabetes mellitus was defined as fasting glucose ≥126 mg/dL, nonfasting glucose ≥200 mg/dL, or the use of medication. A peripheral blood sample was obtained before hemodialysis on a Monday or a Tuesday. The ABI was determined in all the patients using an ABI form (Colin, Japan), which simultaneously measures bilateral arm and ankle blood pressures (brachial and posterior tibial arteries, respectively) using an oscillometric method. The blood pressure was measured after the patients had rested in a supine position for at least 5 min. The ABI was calculated using the ratio of the ankle systolic pressure divided by the arm systolic pressure. Blood pressure measurement in the arm with blood access is not desirable. So, the systolic pressure of the arm without dialysis access was used for the calculation. Patients who failed to measure ABI of either side of the legs were excluded from the study. The change in the ABI (2011 measurement versus 2005 measurement) was estimated. Delta ABI was defined and estimated as the following formula.
$$ \mathrm{Delta}\;\mathrm{A}\mathrm{B}\mathrm{I}=\mathrm{A}\mathrm{B}\mathrm{I}\;\mathrm{in}\;2011-\mathrm{A}\mathrm{B}\mathrm{I}\;\mathrm{in}\;2005 $$

We set the baseline at 2011 and investigated the patient outcomes. The clinical endpoints were defined as cardiovascular disease (CVD) events and death from any cause. CVD events include cardiac events (angina pectoris, hospitalization for heart failure, percutaneous coronary intervention, coronary artery bypass surgery, or acute myocardial infarction), cerebral events (hospitalization for cerebral infarction or bleeding), aortic dissection (hospitalization for aortic dissection), and PAD events (hospitalization for PAD including intervention, graft bypass surgery, or amputation). The smaller values for either ABI or delta ABI among the two values of both legs were adopted and incorporated into the analysis. Averages of ABI and delta ABI values for both legs were also examined.

Furthermore, we compared the change in the ABI (including both legs) and several clinical factors observed in 2005. Oral informed consent was obtained from the subjects. This study was conducted in accordance with the principles of the Declaration of Helsinki and permitted by the research ethics committee of the Tokyo Women’s Medical University (Approved No. 3601).

The data were expressed as means ± SD or median (interquartile range, IQR). A univariate Cox proportional hazard model was used to examine the predictors of the overall outcomes. A simple regression analysis was used to examine the relationship between two continuous variables. All the statistical calculations were performed using JMP 5.1 software. P values less than 0.05 were considered statistically significant.

Results

The mean follow-up period was 3.1 ± 0.7 years. The patient background characteristics are shown in Table 1. The mean age was 57.2 ± 13.2 years, and the mean duration of hemodialysis therapy was 19.3 ± 8.7 years. The results of the biochemistry analyses and the primary causes of end-stage kidney disease are also shown in Table 1. Chronic glomerulonephritis was the major cause of end-stage kidney disease in this study. Hypertension and diabetes mellitus were observed in 59.0 and 14.8 % of the study participants, respectively.
Table 1

Background characteristics of the study participants

 

All patients (n = 61)

Age (year)

57.2 ± 13.2

Gender (M/F)

33/28

Duration of HD (year)

19.8 (11.1–28.1)

Albumin (g/dL)

3.7 ± 0.3

Creatinine (mg/dL)

11.8 ± 2.3

Uric acid (mg/dL)

7.7 ± 1.4

Corrected calcium (mg/dL)

9.4 ± 0.8

Phosphate (mg/dL)

5.4 ± 1.4

Ca × P (mg/dL)2

50.6 ± 12.2

Total cholesterol (mg/dL)

159 ± 36

Triglyceride (mg/dL)

102 ± 41

C-reactive protein (mg/dL)

0.07 (0.05–0.16)

Hemoglobin (g/dL)

10.7 ± 0.8

Primary Cause of ESKD, n (%)

 Chronic glomerulonephritis

43 (70.5)

 Diabetic nephropathy

5 (8.2)

 Polycystic kidney disease

3 (4.9)

 Chronic pyelonephritis

1 (1.7)

 Post transplantation

8 (13.1)

 Unknown and others

1 (1.7)

Complication, n (%)

 Hypertension

36 (59.0)

 Diabetes mellitus

9 (14.8)

Mean ± SD, median (interquartile range: IQR)

HD hemodialysis, Ca calcium, P phosphate, ESKD end-stage kidney disease

During the follow-up period, seven deaths were recorded. The causes of death were infection in five cases, cerebrovascular disease in one case, and unknown in one case. Table 2 shows the Cox proportional hazard analysis of the covariates for predicting mortality. In the univariate analysis, predictive variables for mortality included old age (HR, 1.06 [1.00–1.13]; P = 0.048 [per 1 year increase]), low level of serum albumin (HR, 0.12 [0.02–0.82]; P = 0.032 [per 1 g/dL increase]), creatinine (HR, 0.49 [0.32–0.71]; P < 0.001 [per 1 mg/dL increase]), uric acid (HR, 0.42 [0.20–0.80]; P = 0.008 [per 1 mg/dL increase]), phosphate (HR, 0.26 [0.09–0.65]; P = 0.002 [per 1 mg/dL increase]), ABI (smaller values among both legs) (HR, 0.43 [0.25–0.64]; P < 0.0001 [per 0.1 increase]), and delta ABI (smaller values among both legs) (HR, 0.62 [0.49–0.79]; P < 0.001 [per 0.1 increase]). The average values of ABI and delta ABI among both legs were also risk factors for mortality (HR, 0.47 [0.29–0.71]; P < 0.001 [per 0.1 increase], HR, 0.49 [0.31–0.74]; P < 0.001 [per 0.1 increase], respectively).
Table 2

Cox proportional hazard analysis of the covariates for predicting mortality (univariate analysis)

Parameters (n = 61)

Hazard ratio (95 % CI)

P value

Age (per 1 year)

1.06 (1.00 to 1.13)

0.048

Gender (M)

0.77 (0.34 to 1.64)

0.493

Duration of HD (per 1 year)

1.00 (0.91 to 1.09)

0.969

Albumin (per 1 g/dL)

0.12 (0.02 to 0.82)

0.032

Creatinine (per 1 mg/dL)

0.49 (0.32 to 0.71)

<0.001

Uric acid (per 1 mg/dL)

0.42 (0.20 to 0.80)

0.008

Corrected calcium (per 1 mg/dL)

1.14 (0.42 to 2.81)

0.789

Phosphate (per 1 mg/dL)

0.26 (0.09 to 0.65)

0.002

Ca × P (per 1 (mg/dL)2)

0.98 (0.92 to 1.03)

0.446

Total cholesterol (per 1 mg/dL)

1.00 (0.98 to 1.02)

0.919

Triglyceride (per 1 mg/dL)

1.00 (0.98 to 1.02)

0.916

C-reactive protein (per 1 mg/dL)

1.04 (0.29 to 1.75)

0.912

Hemoglobin (per 1 g/dL)

0.60 (0.25 to 1.44)

0.250

Hypertension (Y)

2.15 (0.89 to 9.36)

0.095

Diabetes mellitus (Y)

0.95 (0.22 to 2.29)

0.918

ABI (per 0.1)a

0.43 (0.25 to 0.64)

<0.0001

ΔABI (per 0.1)a

0.62 (0.49 to 0.79)

<0.001

ΔABI = ABI in 2011 − ABI in 2005

HD hemodialysis, Ca calcium, P phosphate, ABI ankle-brachial blood pressure index

aABI and ΔABI: smaller values among both legs

Table 3 shows the Cox proportional hazard analysis of the covariates for predicting composite endpoints including CVD events and mortality. Nine CVD events were observed during the follow-up period including three in cardiac events (two angina pectoris, one acute myocardial infarction), three in cerebral events (one cerebral infarction, one acute subdural hematoma, one chronic subdural hematoma), two in PAD events (interventional therapy for PAD), and one in aortic dissection. In the univariate analysis, predictive variables for the composite endpoints included old age (HR, 1.04 [1.00–1.09]; P = 0.044 [per 1 year increase]), low level of serum albumin (HR, 0.16 [0.05–0.64]; P = 0.011 [per 1 g/dL increase]), creatinine (HR, 0.67 [0.52–0.86]; P = 0.001 [per 1 mg/dL increase]), and phosphate (HR, 0.60 [0.34–0.96]; P = 0.033 [per 1 mg/dL increase]), low level of hemoglobin (HR, 0.52 [0.27–0.98]; P = 0.042 [per 1 g/dL increase]), ABI (smaller values among both legs) (HR, 0.65 [0.50–0.86]; P = 0.025, [per 0.1 increase]), and delta ABI (smaller values among both legs) (HR, 0.73 [0.61–0.90]; P = 0.005, [per 0.1 increase]). The average values of ABI and delta ABI among both legs were also risk factors for composite endpoints including CVD events and mortality (HR, 0.68 [0.50–0.92]; P = 0.014 [per 0.1 increase], HR, 0.01 [0.00–0.33]; P = 0.001 [per 0.1 increase], respectively).
Table 3

Cox proportional hazard analysis of the covariates for predicting composite endpoints including cardiovascular events and mortality (univariate analysis)

Parameters (n = 61)

Hazard ratio (95 % CI)

P value

Age (per 1 year)

1.04 (1.00 to 1.09)

0.044

Gender (M)

1.25 (0.73 to 2.25)

0.419

Duration of HD (per 1 year)

1.03 (0.97 to 1.09)

0.357

Albumin (per 1 g/dL)

0.16 (0.05 to 0.64)

0.011

Creatinine (per 1 mg/dL)

0.67 (0.52 to 0.86)

0.001

Uric acid (per 1 mg/dL)

0.80 (0.51 to 1.21)

0.294

Corrected calcium (per 1 mg/dL)

1.68 (0.74 to 3.64)

0.211

Phosphate (per 1 mg/dL)

0.60 (0.34 to 0.96)

0.033

Ca × P (per 1 (mg/dL)2)

0.96 (0.91 to 1.01)

0.084

Total cholesterol (per 1 mg/dL)

0.99 (0.97 to 1.00)

0.141

Triglyceride (per 1 mg/dL)

1.01 (1.00 to 1.02)

0.153

C-reactive protein (per 1 mg/dL)

1.44 (0.84 to 2.05)

0.154

Hemoglobin (per 1 g/dL)

0.52 (0.27 to 0.98)

0.042

Hypertension (Y)

1.40 (0.81 to 2.67)

0.239

Diabetes mellitus (Y)

0.62 (0.15 to 1.40)

0.299

ABI (per 0.1)a

0.65 (0.50 to 0.86)

0.025

ΔABI (per 0.1)a

0.73 (0.61 to 0.90)

0.005

ΔABI = ABI in 2011 − ABI in 2005

HD hemodialysis, Ca calcium, P phosphate, ABI ankle-brachial blood pressure index

a ABI and ΔABI: smaller values among both legs

Table 4 shows the relationships between the change in the ABI (2005 to 2011) and other clinical parameters observed in 2005. Patient age was negatively correlated and the serum creatinine level was positively correlated with the change in the ABI (P = 0.030 and 0.022, respectively).
Table 4

The relationship between the change of ABI (2005 to 2011) and other clinical parameters of 2005

 

r

P value

Age (year)

-0.1968

0.030

Duration of HD (year)

0.0762

0.404

Albumin (g/dL)

0.0490

0.592

Creatinine (mg/dL)

0.2079

0.022

Corrected calcium (mg/dL)

0.0783

0.391

Phosphate (mg/dL)

−0.0371

0.685

Ca × P (mg/dL)2

0.0542

0.553

Total cholesterol (mg/dL)

−0.1385

0.128

Triglyceride (mg/dL)

−0.0002

0.998

C-reactive protein (mg/dL)

0.1233

0.176

Hemoglobin (g/dL)

−0.0134

0.884

fABI ankle-brachial blood pressure index, HD hemodialysis, Ca calcium, P phosphate

Discussion

We confirmed that not only the value of the ABI itself but also the change in the ABI was a risk factor for mortality among hemodialysis patients. The change in the ABI was negatively correlated with patient age and was positively correlated with the serum creatinine level.

PAD is a regular complication of hemodialysis patients, and the HEMO study reported a prevalence of 23.0 % [12]. In addition, we previously reported a high prevalence (22.1 %) of PAD among hemodialysis patients [7]. PAD is recognized as a polyvascular disease because of the frequent coexistence of coronary artery and cerebrovascular atherosclerosis [13]. Moreover, we recently showed that, in a study examining 117 hemodialysis patients, infection was significantly more prevalent in the PAD group than in the non-PAD group [8]. Both the short-term and long-term outcomes of hemodialysis patients with PAD have been reported to be poor [69]. The ABI is a simple, reliable diagnostic tool that can be used to define the existence and severity of PAD. We also confirmed that a low ABI was a risk factor for mortality.

Recently, Kuwahara et al. estimated 300 hemodialysis patients and showed that a higher rate of ABI decline was a prominent predictor of survival among patients receiving hemodialysis therapy (HR, 4.044; P < 0.001 [per 0.1/year decrease]) in multivariate Cox analysis [11]. We also showed that the change in the ABI was a risk factor for mortality, but it was a univariate analysis because of the small sample size. Their observation period for the change in the ABI was 1 year, which was 6 years in our study. We conformed that the change in the ABI in a relatively long period was also a risk factor for mortality. We added analysis of CVD events and conformed that not only the low level of the ABI itself but also the reduction of the ABI were both significant risk factors for CVD events and mortality.

Another recent study reported that a high fasting glucose level and old age were independent determinants of the accelerated progression of the ABI [14]. We also observed a negative correlation between age and the change in the ABI. We could not examine the fasting glucose level. However, we did observe that the serum creatinine level was positively correlated with the change in the ABI. We previously compared PAD patients and non-PAD patients and found that the serum albumin and creatinine levels were lower, the serum C-reactive protein level was higher, and the hemoglobin level was lower among PAD hemodialysis patients than non-PAD patients [8]. A low serum creatinine level is not only correlated with a low ABI but also predicts further reductions in the ABI. Serum creatinine is a marker of nutrition [15], and malnutrition and atherosclerosis are well known to affect each other in a condition known as malnutrition inflammation atherosclerosis (MIA) syndrome [16]. A malnutrition status is correlated with a low serum creatinine level and causes atherosclerosis, which reduces the ABI. The reason why other markers of malnutrition, such as the serum albumin level, did not predict the change in the ABI remained unclear. A relatively long-term observation period may be one of the reasons.

Our study had several limitations. The study was a retrospective examination of patients evaluated at a single institution, and differences in care, including medications, occurred over the course of the study. The prevalence of diabetes mellitus and nephrosclerosis was relatively low because of our institution’s specialty. But we first revealed that low serum level of creatinine predict reduction of ABI.

Conclusions

We confirmed that not only the value of the ABI itself but also the change in the ABI was a risk factor for mortality among hemodialysis patients. Careful attention should be given to changes in the ABI as well as the value of the ABI itself. The change in the ABI was negatively correlated with age and was positively correlated with the serum creatinine level. Careful observation of the ABI is needed for old patients and patients with a low serum creatinine level.

Declarations

Acknowledgements

The authors are very grateful to the dialysis staff who understood the clinical importance of this study and who provided high-quality data at the Tokyo Women’s Medical University Hospital.

Authors’ contributions

TAbe planned the study, searched the literature, assessed the studies, extracted the data, analyzed the data, and prepared the article. SO and NK searched the literature, assessed the studies, and assisted in the article preparation. NK and NM assessed the data extraction. TAbe, TO, and YO performed the ABI measurement. JM, IK, MM, KT, KN, and TAki assisted in the article preparation. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Clinical Engineering, Tokyo Women’s Medical University
(2)
Department of Blood Purification, Tohto Sangenjaya Clinic
(3)
Department of Medicine, Kidney Center, Tokyo Women’s Medical University
(4)
Department of Blood Purification, Kidney Center, Tokyo Women’s Medical University
(5)
Department of Nephrology, Sekikawa Hospital

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© The Author(s) 2016

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