Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2019 Aug 2.
Published in final edited form as: Am J Nephrol. 2018 Aug 2;48(1):56–64. doi: 10.1159/000491828

Effect of anti-hypertensive medication history on arteriovenous fistula maturation outcomes.

Ke Wang 1, Leila R Zelnick 1, Peter B Imrey 2,3, Ian H deBoer 1, Jonathan Himmelfarb 1, Michael D Allon 4, Alfred K Cheung 5,7,8, Laura M Dember 9,10, Prabir Roy-Chaudhury 11,12, Miguel A Vazquez 14, John W Kusek 15, Harold I Feldman 9,10, Gerald J Beck 2,3, Bryan Kestenbaum 1; Hemodialysis Fistula Maturation Study Group6,13
PMCID: PMC6421073  NIHMSID: NIHMS983075  PMID: 30071516

Abstract

Background

The arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis. However approximately half of AVFs fail to mature. Use of angiotensin converting enzyme inhibitors (ACE-Is), angiotensin receptor blockers (ARBs), and calcium channel blockers (CCBs) exert favorable endothelial effects and may promote AVF maturation. We tested associations of ACE-I and ARBs, CCBs, beta-blockers, and diuretics with maturation of newly created AVFs.

Methods

We evaluated 602 participants from the Hemodialysis Fistula Maturation Study, a multi-center, prospective cohort study of AVF maturation. We ascertained the use of each medication class within 45 days of AVF creation surgery. We defined maturation outcomes by clinical use within 9 months of surgery or 4 weeks of initiating hemodialysis.

Results

Unassisted AVF maturation failure without intervention occurred in 54.0% of participants, and overall AVF maturation failure (with or without intervention) occurred in 30.1%. After covariate adjustment, CCB use was associated with a 25% lower risk of overall AVF maturation failure (95% CI 3%-41% lower), but a non-significant 10% lower risk of unassisted maturation failure (95% CI 23% lower to 5% higher). ACE-I/ARB, beta-blocker and diuretic use were not significantly associated with AVF maturation outcomes. None of the antihypertensive medication classes were associated with changes in AVF diameter or blood flow over 6 weeks following surgery.

Conclusions

CCB use may be associated with a lower risk of overall AVF maturation failure. Further studies are needed to determine whether CCBs might play a causal role in improving AVF maturation outcomes

Keywords: arteriovenous fistula maturation failure, calcium channel blocker, anti-hypertensive medications

BACKGROUND

Hemodialysis is the most common form of renal replacement therapy in the United States with more than 100,000 Americans initiating maintenance hemodialysis each year.1 The arteriovenous fistula (AVF) is the preferred method of vascular access due to greater longevity, lower infection rates, and longer survival compared to other access methods.2 However, approximately one-half of surgically created AVFs fail to mature and no therapies can meaningfully improve AVF maturation outcomes.3 Identifying modifiable risk factors for AVF maturation failure is an important step toward suggesting new therapeutic interventions.

Angiotensin converting enzyme inhibitors (ACE-I), angiotensin receptor blockers (ARBs), and calcium channel blockers (CCB) are commonly prescribed to hemodialysis patients and may promote AVF maturation through diverse mechanisms. Angiotensin II induces growth factor production and stimulates vascular smooth muscle cell proliferation.4,5 In vitro and animal experimental models of vascular injury have demonstrated that angiotensin blockade halts vascular smooth cell migration and reduces endothelial intimal formation.6–9 CCBs reduce cellular calcium entry, which mitigates pathologic intracellular calcium overload of end-stage renal disease, increases stable nitric oxide metabolites, and prevents apoptosis.10,11 Previous studies have reported associations of ACE-I/ARBs and CCBs with greater patency of existing AVFs and arteriovenous grafts (AVGs), but few have investigated associations with primary AVF maturation.12–15

We hypothesized that ACE-I/ARB and CCB use would be associated with lower rates of AVF maturation failure. To test this hypothesis, we evaluated associations of ACE-I/ARB and CCB use, at baseline, with clinical AVF outcomes of overall and unassisted maturation in the prospective Hemodialysis Fistula Maturation (HFM) Study. In exploratory analyses we also assessed the associations of beta-blockers and diuretics, two other commonly prescribed antihypertensive medication classes, with clinical maturation outcomes.

METHODS

Study Population

The HFM Study enrolled 602 participants, each undergoing creation of a single autogenous upper extremity AVF between 2010 and 2013 at one of seven study sites in the U.S.16 Study participants were either receiving maintenance dialysis or expected to start dialysis within three months of planned AVF surgery. Exclusion criteria were age less than 18 years or greater than 80 years if not yet on dialysis, inability to provide informed consent, and life expectancy less than nine months. All participants gave written informed consent and Institutional Review Board approval was obtained at each study site.

Ascertainment of Antihypertensive Medication Use

HFM Study personnel obtained names of all active medications from participants by interview and chart review within 45 days prior to planned AVF surgery. Specific medication dosages and administration schedules were not collected. Moreover, information regarding intravenous medications provided during dialysis was not abstracted. All medications were coded using the World Health Organization (WHO) drug system. We classified antihypertensive medications into one of four mutually exclusive categories: (1) angiotensin converting enzyme inhibitors (ACE-Is) or angiotensin receptor blockers (ARBs), (2) beta-blockers, (3) calcium channel blockers (CCBs), and (4) diuretics. For additional analyses, we further sub-classified ACE-I/ARB separately as ACE-I and ARB, and CCB as dihydropyridine (DHP) and non-dihydropyridine (non-DHP).

Determination of AVF Maturation Outcomes

We evaluated the HFM Study outcomes of unassisted and overall AVF maturation.16 Unassisted AVF maturation, the primary HFM Study outcome, was defined as successful clinical use of the AVF for hemodialysis within 9 months of surgical placement, or within 4 weeks of initiating hemodialysis without maturation-enhancing procedures. Successful clinical use entailed the use of the AVF with two needles for at least 75% of hemodialysis sessions during a four week period plus either (1) a mean dialysis blood pump speed greater ≥300 ml/minute for four consecutive sessions, or (2) a measured spKt/V ≥ 1.4 or URR>70% in any session during the four week period, with the above time windows applied to the first of the 4 days that a mean pump speed ≥300 mL/minute, or the first day an spKt/V ≥1.4 or URR >70% is achieved. Overall AVF maturation was identified using the same criteria, but allowing additional ancillary procedures to promote AVF maturation if needed.

Preoperative and Postoperative Ultrasound Measurements

Centrally trained study sonographers at each clinical site performed preoperative (baseline) ultrasound mapping of arteries and veins in the arm intended for AVF creation. Sonographers obtained subsequent ultrasound images of the surgically created AVF early postoperatively (days 0-3, targeting day 1), and at weeks 2 and 6. All ultrasound measurements were obtained following a standardized HFM protocol that has been previously described.17 Measurements and imaging data were transmitted to the HFM Ultrasound Core, where a sonographer blinded to the study data evaluated images and measurements for accuracy and adherence to protocol. HFM Study radiologists, also blinded to the study data, then read the images and entered the results into the study database.

Other Study Variables

HFM Study personnel used patient interviews and medical records to complete standardized forms recording patient demographics, medical histories, dialysis and vascular access histories, and social habits. Such information was updated if more than 45 days elapsed before AVF surgery. Participant demographics and lifestyle factors included age, sex, race, smoking status, and highest education level completed. Comorbid conditions included histories of coronary artery disease (angina, myocardial infarction, coronary artery bypass, or percutaneous coronary revascularization), congestive heart failure (New York Heart Association Class II or greater), and diabetes. HFM Study personnel also collected height, weight, and three resting blood pressures in the non-access arm.

Statistical Analysis

We tabulated baseline characteristics of HFM Study participants by the use versus non-use of each anti-hypertensive medication class. We calculated the unassisted and overall AVF maturation failure proportions for users and non-users and used Poisson regression with robust standard errors to estimate the relative risk of AVF maturation failure associated with use of each antihypertensive medication class. We formulated three covariate adjustment models prior to analyses. The first model adjusted for age, sex, race (black versus non-black), and study site. The second model added adjustments for body mass index, history of diabetes and congestive heart failure, dialysis status, AVF location, smoking status, and highest level of education. A third model, viewed as primary, added adjustments for baseline systolic blood pressure and the remaining antihypertensive medication classes to estimate independent associations for each class. We also tested the 2-way, 3-way, and 4-way interactions among the four medication classes with a single omnibus test. We conducted additional analyses to assess separate associations for ACE-Is and ARBs, and for DHP and non-DHP CCBs. For these sensitivity analyses only we excluded participants who were concomitantly on ACE-Is and ARBs (n=14) or DHP and non-DHP CCBs (n=2).

We ascertained ultrasound measurements of AVF flow (mL/min) and draining vein diameter (mm) for each patient during a pre-operative vessel mapping examination, early postoperatively (days 0-3, targeting day 1), week 2, and week 6. We constructed linear mixed models to examine the differences in temporal changes in ultrasound measurement between users and non-users of each antihypertensive class. In each case, the linear mixed model allowed for a random effect of patient, main effects of time modeled continuously and the antihypertensive class of interest, the interaction between these two, and additional covariates as described earlier. Statistical significance of the linear interaction between time and use of the antihypertensive class of interest was taken to be evidence of a difference in the change in AVF diameter or flow between users and non-users.

For all regression analyses, we used multiple imputation with 10 imputations and chained equations to account for missingness in covariates and AVF outcomes.18,19 We combined the resulting estimates using Rubin’s rules to account for variability in the imputation procedure.20 A 2-sided p-value of 0.05 was considered significant for all analyses. Analyses were conducted using open-source package R version 3.3.0 (R Foundation, Vienna, Austria)21 and STATA 11 (College Station, TX).22 We used the lmer function in the lme4 R package23 for linear mixed models and the glm function in R for Poisson regression models.

RESULTS

Description of the Study Population

Among the 602-person HFM Study cohort, the mean age was 55.1 years; 423 participants (70.2%) were male, 264 (43.8%) were black, and 383 (63.6%) were receiving maintenance dialysis at the time of surgery. Anti-hypertensive medication use was highly prevalent in the HFM Study cohort, with respectively 70 (11.6%), 135 (22.4%), and 365 (60.6%) of participants reporting use of one, two, and 3-4 anti-hypertensive medication classes. The most commonly used anti-hypertensive medication classes were beta-blockers (73.0%), followed by CCBs (65.2%), diuretics (48.5%), and ACE-I/ARBs (47.1%). Baseline characteristics of ACE-I/ARB and CCB users were generally similar to those of non-users of these medications (Table 1). CCB users were more likely to be black (46.6 vs 38.8%) and less likely to be receiving maintenance hemodialysis (59.0 vs 72.2%) compared to non-CCB users. Diuretic and beta-blocker users tended to have more co-morbid conditions compared to non-users (Supplemental Tables 1-2). Users of each antihypertensive class tended to have higher baseline systolic blood pressures compared to non-users (Table 1, Supplemental Tables 1-2).

Table 1.

Baseline characteristics by ACE-I/ARB and CCB use.

ACE-I/ARB use CCB use
Yes No Yes No
(N=284) (N=318) (N=393) (N=209)
Age (years) 54.5 ±12.9 55.6 ±13.8 55.2 ±13.0 54.8 ±14.1
Male sex 197 (69.4) 226 (71.1) 280 (71.2) 143 (68.4)
Race/ethnicity
 White 131 (46.1) 152 (47.8) 178 (45.3) 105 (50.2)
 Black 128 (45.1) 136 (42.8) 183 (46.6) 81 (38.8)
 Other 25 (8.8) 30 (9.4) 32 (8.1) 23 (11.0)
Education1
 No high school diploma 87 (30.6) 74 (23.3) 107 (27.2) 54 (25.8)
 High school diploma 79 (27.8) 84 (26.4) 112 (28.5) 51 (24.4)
 Post-secondary education 107 (37.7) 153 (48.1) 163 (41.5) 97 (46.4)
Smoking2
 Current 45 (16.0) 60 (19.0) 72 (18.3) 33 (15.8)
 Former 103 (36.5) 117 (37.1) 146 (37.2) 74 (35.4)
 Never 134 (47.5) 138 (43.8) 170 (43.3) 102 (48.8)
Maintenance dialysis 179 (63.0) 204 (64.2) 232 (59.0) 151 (72.2)
History of diabetes 173 (60.9) 180 (56.6) 236 (60.1) 117 (56.0)
Prevalent cardiovascular disease 140 (49.3) 150 (47.2) 190 (48.3) 100 (47.8)
History of congestive heart failure 80 (28.2) 85 (26.7) 102 (26.0) 63 (30.1)
Body mass index (kg/m2) 30.8 ± 7.8 30.0 ± 7.4 30.5 ± 7.5 30.1 ± 7.8
Systolic blood pressure (mmHg) 153.7 ± 24.7 149.0 ± 22.9 153.5 ± 23.2 146.9 ± 24.4
Estimated GFR (mL/min/1.73m2)* 14.1 ± 4.7 13.5 ± 4.9 13.5 ± 4.6 14.5 ± 5.2
AVF location
 Forearm 75 (26.4) 68 (21.4) 91 (23.2) 52 (24.9)
 Upper arm 209 (73.6) 250 (78.6) 302 (76.8) 157 (75.1)
Antihypertensive medication use
No antihypertensive medications 0 (0.0) 32 (10.1) 0 (0) 32 (15.3)
ACE-I/ARB 284 (100.0) 0 (0.0) 183 (46.6) 101 (48.3)
 Beta-blockers 219 (77.1) 221 (69.5) 301 (76.6) 139 (66.5)
 CCB 183 (64.4) 210 (66.0) 393 (100) 0 (0)
 Diuretics 141 (49.6) 151 (47.5) 215 (54.7) 77 (36.8)
Open in a new tab

Values in the table expressed as mean ± standard deviation or number (percent).

GFR=glomerular filtration rate

1

Education data missing for 18 participants

2

Smoking status missing for 5 participants

*

Excludes participants on dialysis

Associations with Unassisted AVF Maturation Failure

In the multiply-imputed data, failure to achieve unassisted AVF maturation occurred in an average of 325 HFM Study participants (54.0%). None of the drug classes showed statistically significant association with unassisted maturation failure under any adjustment model (Table 2). More specifically, ACE-I/ARB users had a higher crude incidence of unassisted AVF maturation failure than non-users (57.4 vs 51.5%) and exhibited non-significant 11-14% higher risks of unassisted AVF maturation failure in minimally or fully adjusted models. CCB users had non-significantly lower crude incidence of unassisted AVF maturation failure than non-CCB users (51.4 vs. 59.8%) in minimally or fully adjusted models (Model 3 adjusted relative risk [RR] 0.90; 95% CI 0.77, 1.05; p=0.16). Subgroup comparisons were non-significant, although there was a trend towards lower risk of unassisted AVF maturation failure with non-DHP than with DHP CCBs (Model 3 adjusted RR 0.86; 95% CI 0.63, 1.18; p=0.35).

Table 2.

Associations of antihypertensive medication use with unassisted AVF failure.

Number of participants Number of failures (%) Relative risk (95% confidence interval)
Model 1 Model 2 Model 3
ACE-I/ARBs
 No 318 164 (51.5) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 284 163 (57.4) 1.14 (0.99, 1.32) 1.11 (0.96, 1.29) 1.11 (0.96, 1.29)
 P-value 0.07 0.15 0.16
Beta-blockers
 No 162 90 (55.5) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 440 237 (53.8) 1.01 (0.86, 1.19) 0.97 (0.82, 1.14) 0.96 (0.81, 1.14)
 P-value 0.88 0.71 0.68
CCBs
 No 209 125 (59.8) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 393 202 (51.4) 0.90 (0.77, 1.05) 0.90 (0.77, 1.04) 0.90 (0.77, 1.05)
 P-value 0.17 0.16 0.16
Diuretics
 No 310 165 (53.2) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 292 162 (55.4) 1.06 (0.92, 1.23) 1.00 (0.85, 1.18) 1.02 (0.86, 1.20)
 P-value 0.41 0.98 0.82
Open in a new tab

Model 1 adjusted for age, gender, race/ethnicity (black vs non-black) and study site.

Model 2 adds adjustment for body mass index, diabetes, dialysis status, AVF location, education, smoking, and history of congestive heart failure.

Model 3 adds adjustment for other anti-hypertensive medication classes and systolic blood pressure.

Associations with Overall AVF Maturation Failure

213 (35.4%) HFM participants required at least one intervention to promote maturation after AVF creation. Angioplasty was the most common intervention (26.1%), followed by occlusion of accessory vein (6.1%), fistula repositioning (4.2%), and thrombectomy (3.0%). Failure to achieve overall AVF maturation occurred in an average of 181 HFM Study participants (30.1%). Users and non-users of ACE-I/ARB and beta-blocker demonstrated similar unadjusted incidences of overall AVF maturation failure (Table 3). CCB use was associated with a statistically significant 23%-25% lower risk of overall maturation failure after full covariate adjustment in Models 2 and 3 (95% confidence interval 3% to 41% lower; p=0.03, after Model 3 adjustment). Diuretic use was associated with a non-significant 20-29% greater risk of overall maturation failure across all levels of adjustment.

Table 3.

Associations of antihypertensive medication use with overall AVF failure.

Number of participants Number of failures (%) Relative risk (95% confidence interval)
Model 1 Model 2 Model 3
ACE-I/ARBs
 No 318 94 (29.5) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 284 89 (31.3) 1.10 (0.86, 1.41) 1.08 (0.84, 1.38) 1.07 (0.84, 1.37)
 P-value 0.44 0.56 0.59
Beta-blockers
 No 162 49 (30.2) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 440 134 (30.4) 1.08 (0.82, 1.43) 1.00 (0.75, 1.32) 0.97 (0.73, 1.29)
 P-value 0.58 0.98 0.83
CCBs
 No 209 74 (35.4) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 393 109 (27.7) 0.80 (0.62, 1.02) 0.77 (0.60, 0.99) 0.75 (0.59, 0.97)
 P-value 0.07 0.04 0.03
Diuretics
 No 310 84 (27.1) 1.0 (Reference) 1.0 (Reference) 1.0 (Reference)
 Yes 292 99 (33.9) 1.29 (1.00, 1.67) 1.20 (0.90, 1.60) 1.26 (0.94, 1.69)
 P-value 0.05 0.20 0.12
Open in a new tab

Model 1 adjusted for age, gender, race/ethnicity (black vs non-black), and study site.

Model 2 adds adjustment for body mass index, diabetes, dialysis status, AVF location, education, smoking, and history of congestive heart failure.

Model 3 adds adjustment for other anti-hypertensive medication classes and systolic blood pressure.

The association of CCB use with lower rates of overall AVF maturation failure was not statistically significantly modified by use versus non-use of ACE-I/ARB, beta-blockers, or diuretics (Figure 1; omnibus p-for-interaction = 0.69). There were no statistically significant differences in overall AVF failure comparing ARB use to ACE-I use (Model 3 adjusted RR 0.90; 95% CI 0.60, 1.35; p value=0.61), or non-DHP use to DHP CCB use (Model 3 adjusted RR 0.79; 95% CI 0.45, 1.39; p value=0.41).

Figure 1. Overall maturation failure rates of CCB users and non-users stratified by the use of other antihypertensive medications.

Figure 1.

Open in a new tab

Bar graph comparing overall AVF maturation failure rates of CCB users and non-users (y-axis) stratified by use and non-use of ACE-I/ARB, beta-blocker, and diuretic (x-axis). Overall maturation failure rates are adjusted for Model 3 covariates and obtained using Poisson model that allowed for interaction between CCB and other antihypertensive classes. Error bars delineate upper limit of 95% confidence interval.

Association with Postoperative AVF Diameter and Flow

Venous diameter and blood flow increased substantially within one day following AVF creation surgery, followed by smaller increases after two and six weeks. Diameter and flow were substantially greater in upper arm AVFs compared to forearm AVFs (Supplemental Figure 1).17 There were no significant differences in changes in vein diameter (p=0.12) or AVF flow (p=0.96) over time between ACE-I/ARB users and non-users (Supplemental Figure 1), CCB users and non-users (diameter p=0.63, blood flow p=0.35; Supplemental Figure 2), or between users and non-users of beta-blockers (Supplemental Figure 3) and diuretics (Supplemental Figure 4).

DISCUSSION

In summary, we observed an association of CCB use within 45 days of AVF creation surgery with a lower incidence of overall AVF maturation failure in a prospective cohort study of 602 patients undergoing AVF creation surgery. CCB use was also associated with a numerically lower, but not statistically significantly different, incidence of unassisted AVF maturation failure. In contrast, the use of ACE-I/ARBs and other anti-hypertensive medication classes were not associated with unassisted or overall AVF maturation failure.

To our knowledge, this is only the second study to assess anti-hypertensive medication use in relation to primary AVF maturation. A previous single center study of 97 AVFs also observed higher rate of AVF maturation among CCB users (84%) compared to non-users (46%).15 Most previous studies have evaluated ACE-Is and CCBs in relation to maintenance of AVF and AVG patency. For example, a retrospective cohort study of hemodialysis patients undergoing angioplasty for access stenosis reported CCB use to be associated with a 32% higher patency rate for AVFs and 25% higher patency rate for AVGs.12 The Dialysis Outcomes and Practice Patterns (DOPPS) study, which evaluated 900 hemodialysis patients with AVFs and 1944 patients with AVGs, observed associations of CCB use with higher rates of primary AVG patency and ACE-I use with improved secondary AVF patency.13 Moreover, Chen et al reported lower risks of AVF and AVG failure associated with ACE-I/ARB and CCB use in a study of 37,771 AVFs and 4,473 AVGs.14 These observational data suggest that ACE-I and CCBs may enhance the life span of existing vascular accesses, but do not address possible effects on primary AVF maturation.

Several factors have been suggested to influence AVF maturation. The innate ability of the artery to dilate, as reflected by both flow- and nitroglycerin-mediated dilation, was associated with AVF development on ultrasonography in a prior HFM study.19 CCBs may promote vasodilation of the arterialized vein post AVF creation. However, we were unable to detect differences in post-surgical trajectories of AVF diameter or flow, assessed by ultrasonography, comparing CCB users to non-users in the present study.

CCBs may also inhibit endothelial neointimal hyperplasia through inhibition of calcium influx via L-type calcium channels, a critical process for vascular smooth muscle cell growth and migration.24 In-vitro models of coronary atherosclerosis have demonstrated dose-dependent inhibitory actions of CCBs on smooth muscle cell proliferation.25,26 In animal models of vascular injury, the administration of CCB decreased aortic neointimal area.27,28 In a human trial of patients with concomitant hypertension, diabetes, and coronary artery disease, CCBs reduced the progression of carotid neointimal area more than diuretics, beta-blockers, or ACE-I despite comparable blood pressure reductions.29 CCBs may also decrease intracellular calcium signaling by inhibiting calcium release from the sarcoplasmic reticulum.30,31 However, the role of neointimal hyperplasia in AVF maturation is uncertain. Several studies, including the HFM Study, have not established a significant association of preexisting venous hyperplasia with clinical AVF maturation failure.32–34 On the other hand, post-operative venous stenosis on ultrasound was associated with both unassisted and overall AVF maturation failure.34 To the extent that AVF maturation may be attributed to neointimal hyperplasia and stenosis, it is plausible that CCB promotes AVF maturation by inhibiting hyperplasia that develops after creation of the AVF.

We observed a non-statistically significant trend toward lower rates of overall AVF maturation failure associated with non-DHP versus DHP CCBs. In experimental models, verapamil and diltiazem have been shown to be more potent inhibitors of vascular smooth muscle cell functions compared to nifedipine.35 Verapamil has also been shown to be a potent inhibitor of platelet aggregation and promoter of endothelial-dependent relaxation of coronary artery bypass graft.36,37 Observational studies have demonstrated that that CCBs confer mortality benefit in dialysis patients, and a retrospective study suggested lower all-cause and cardiovascular mortality in users of DHP compared to non-DHP.38–42 DHP and non-DHP CCBs differ in their peripheral and central physiologic effects. DHP CCBs share a common chemical structure derived from pyridine and have known vascular dilatory properties. In comparison, non-DHP CCBs exert more negative chronotropic effects.43 While DHP CCBs are recommended agents for hypertension treatment in dialysis patients,44 we advise cautious interpretation of our findings and do not suggest selective prescription of CCBs for the purpose of fistula maturation.

The primary limitation of this study is the potential for confounding-by-indication. It is possible that unmeasured characteristics of CCB users versus non-CCB users distorted the observed association with overall AVF maturation failure. The possibility of confounding is moderated to some degree by concurrent evaluation of other anti-hypertensive medication classes, which may be prescribed for similar indications, and by adjustment for known predictors of AVF maturation that were measured using standardized procedures. A second limitation is the possibility of false discovery in the context of multiple testing. CCB use was associated with significant differences in overall AVF maturation failure, but not with unassisted maturation failure, in the context of testing four medication classes, two AVF maturation outcomes, and across multiple adjustment models. Failure to reach statistical significance for unassisted AVF maturation failure may have been due to inadequate study power. However, the possibility of false discovery requires a cautious interpretation and replication of these findings in other studies. Combination effects from the simultaneous use of several antihypertensive classes may have also influenced our results. We have addressed this overlap by adjusting for use of other medication classes in our analyses, although we recognize that we are not powered to conduct individual sub-group analyses. Finally, the HFM Study assessed the use versus non-use of medications of interest at a single time point, precluding assessment of dosage or duration of therapy. A causal benefit of CCB also cannot be inferred based on our results.

An important strength of this study is evaluation of primary AVF maturation outcomes within the largest prospective study of AVF maturation conducted to date. The multi-center setting of the HFM Study enhances applicability of our findings beyond the practice patterns of a single clinical site. A second strength is the examination of several anti-hypertensive medication classes in association with AVF outcomes.

CONCLUSIONS

CCB use was associated with lower risk of overall clinical AVF maturation failure. Further studies are needed to probe potential relevant mechanisms of action. Ultimately, randomized clinical trials are necessary to determine whether CCBs play a causal role in promoting AVF maturation.

Supplementary Material

1

Acknowledgements:

The HFM Study was funded by grants U01DK082218, U01DK082222, U01DK082232, U01DK082236, U01DK082240, U01DK082179, U01DK082189 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This study was partially supported by NIDDK grant K24DK103986. The members of the Hemodialysis Fistula Maturation Study Group are as follows: Chair, Steering Committee, University of Pennsylvania: H. Feldman; Clinical Centers, Boston University: L. Dember (principal investigator [PI]), A. Farber, J. Kaufman, L. Stern, P. LeSage, C. Kivork, D. Soares, M. Malikova; University of Alabama: M. Allon (PI), C. Young, M. Taylor, L. Woodard, K. Mangadi; University of Cincinnati: P. Roy-Chaudhury (PI), R. Munda, T. Lee, R. Alloway, M. El-Khatib, T. Canaan, A. Pflum, L. Thieken, B. Campos-Naciff; University of Florida: T. Huber (PI), S. Berceli, M. Jansen, G. McCaslin, Y. Trahan; University of Texas Southwestern: M. Vazquez (PI), W. Vongpatanasin, I. Davidson, C. Hwang, T. Lightfoot, C. Livingston, A. Valencia, B. Dolmatch, A. Fenves, N. Hawkins; University of Utah: A. K. Cheung (PI), L. Kraiss, D. Kinikini, G. Treiman, D. Ihnat, M. Sarfati, Y.T. Shiu, C. Terry, I. Lavasani, M. Maloney, L. Schlotfeldt; University of Washington: J. Himmelfarb (PI), C. Buchanan, C. Clark, C. Crawford, J. Hamlett, J. Kundzins, L. Manahan, J. Wise; Data Coordinating Center, Cleveland Clinic: G. Beck (PI), J. Gassman, T. Greene, P. Imrey, L. Li, J. Alster, M. Li, J. MacKrell, M. Radeva, B.Weiss, K. Wiggins; histology core facility, University of Washington: C. Alpers (PI), K. Hudkins, T. Wietecha; US core facility, University of Alabama at Birmingham: M. Robbin (PI), H. Umphrey, L. Alexander, C. Abts, L. Belt; vascular function core facility, Boston University: J. Vita (PI, deceased), N. Hamburg (PI). M. Duess, A. Levit; National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) biosample repository, Fisher BioServices: H. Higgins, S. Ke, O. Mandaci, C. Snell; NIDDK DNA repository, Fred Hutchinson Cancer Research Center: J. Gravley, S. Behnken, R. Mortensen; External Expert Panel: G. Chertow (Chair), A. Besarab, K. Brayman, M. Diener-West, D. Harrison, L. Inker, T. Louis, W. McClellan, J. Rubin; NIDDK: J. Kusek, R. Star. We thank the patients for their participation in the HFM Study. We acknowledge Lin Belt, Carl Abts and Lauren Alexander for their invaluable expertise at the HFM Ultrasound Core. This ancillary study was funded by grant R01 DK094891-01.

Funding: this work was supported by R01DK094891 granted to Dr. Kestenbaum.

LIST OF ABBREVIATIONS

ACE-I

angiotensin converting enzyme inhibitor

ARB

angiotensin receptor blocker

AVF

arteriovenous fistula

AVG

arteriovenous grafts

CCB

calcium channel blocker

DHP

dihydropyridine

HFM

Hemodialysis Fistula Maturation

spKt/V

single-pooled Kt/V

URR

urea reduction ratio

Footnotes

Conflicts of Interest: The authors do not report any conflicts of interest.

REFERENCES

  • 1.Saran R, Li Y, Robinson B, et al. US Renal Data System 2014 Annual Data Report: Epidemiology of Kidney Disease in the United States. American journal of kidney diseases : the official journal of the National Kidney Foundation. 2015;66(1 Suppl 1):Svii, S1–305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Polkinghorne KR, McDonald SP, Atkins RC, Kerr PG. Vascular access and all-cause mortality: a propensity score analysis. Journal of the American Society of Nephrology : JASN. 2004;15(2):477–486. [DOI] [PubMed] [Google Scholar]
  • 3.Allon M, Robbin ML. Increasing arteriovenous fistulas in hemodialysis patients: problems and solutions. Kidney Int. 2002;62(4):1109–1124. [DOI] [PubMed] [Google Scholar]
  • 4.Gibbons GH, Dzau VJ. The emerging concept of vascular remodeling. The New England journal of medicine. 1994;330(20):1431–1438. [DOI] [PubMed] [Google Scholar]
  • 5.Daemen MJ, Lombardi DM, Bosman FT, Schwartz SM. Angiotensin II induces smooth muscle cell proliferation in the normal and injured rat arterial wall. Circulation research. 1991;68(2):450–456. [DOI] [PubMed] [Google Scholar]
  • 6.Powell JS, Clozel JP, Muller RK, et al. Inhibitors of angiotensin-converting enzyme prevent myointimal proliferation after vascular injury. Science (New York, NY). 1989;245(4914):186–188. [DOI] [PubMed] [Google Scholar]
  • 7.Prescott MF, Webb RL, Reidy MA. Angiotensin-converting enzyme inhibitor versus angiotensin II, AT1 receptor antagonist. Effects on smooth muscle cell migration and proliferation after balloon catheter injury. The American journal of pathology. 1991;139(6):1291–1296. [PMC free article] [PubMed] [Google Scholar]
  • 8.Liu SQ. Focal expression of angiotensin II type 1 receptor and smooth muscle cell proliferation in the neointima of experimental vein grafts: relation to eddy blood flow. Arteriosclerosis, thrombosis, and vascular biology. 1999;19(11):2630–2639. [DOI] [PubMed] [Google Scholar]
  • 9.Wong J, Rauhoft C, Dilley RJ, Agrotis A, Jennings GL, Bobik A. Angiotensin-converting enzyme inhibition abolishes medial smooth muscle PDGF-AB biosynthesis and attenuates cell proliferation in injured carotid arteries: relationships to neointima formation. Circulation. 1997;96(5):1631–1640. [DOI] [PubMed] [Google Scholar]
  • 10.Xie L, Clunn GF, Lymn JS, Hughes AD. Role of intracellular calcium ([Ca2+]i) and tyrosine phosphorylation in adhesion of cultured vascular smooth muscle cells to fibrinogen. Cardiovascular research. 1998;39(2):475–484. [DOI] [PubMed] [Google Scholar]
  • 11.Scherberich A, Campos-Toimil M, Ronde P, Takeda K, Beretz A. Migration of human vascular smooth muscle cells involves serum-dependent repeated cytosolic calcium transients. Journal of cell science. 2000;113(Pt 4):653–662. [DOI] [PubMed] [Google Scholar]
  • 12.Doi S, Masaki T, Shigemoto K, Harada S, Yorioka N. Calcium channel antagonists reduce restenosis after percutaneous transluminal angioplasty of an arteriovenous fistula in hemodialysis patients. Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy. 2008;12(3):232–236. [DOI] [PubMed] [Google Scholar]
  • 13.Saran R, Dykstra DM, Wolfe RA, Gillespie B, Held PJ, Young EW. Association between vascular access failure and the use of specific drugs: the Dialysis Outcomes and Practice Patterns Study (DOPPS). American journal of kidney diseases : the official journal of the National Kidney Foundation. 2002;40(6):1255–1263. [DOI] [PubMed] [Google Scholar]
  • 14.Chen FA, Chien CC, Chen YW, Wu YT, Lin CC. Angiotensin Converting-Enzyme Inhibitors, Angiotensin Receptor Blockers, and Calcium Channel Blockers Are Associated with Prolonged Vascular Access Patency in Uremic Patients Undergoing Hemodialysis. PloS one. 2016;11(11):e0166362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Bashar K, Zafar A, Elsheikh S, et al. Predictive parameters of arteriovenous fistula functional maturation in a population of patients with end-stage renal disease. PloS one. 2015;10(3):e0119958. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Dember LM, Imrey PB, Beck GJ, et al. Objectives and design of the hemodialysis fistula maturation study. American journal of kidney diseases : the official journal of the National Kidney Foundation. 2014;63(1):104–112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Robbin ML, Greene T, Cheung AK, et al. Arteriovenous Fistula Development in the First 6 Weeks after Creation. Radiology. 2016;279(2):620–629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Royston P: Multiple imputation of missing values. Stata J 4:227–241, 2004 [Google Scholar]
  • 19.Allon M, Greene T, Dember LM, et al. Association between Preoperative Vascular Function and Postoperative Arteriovenous Fistula Development. Journal of the American Society of Nephrology : JASN. 2016;27(12):3788–3795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Rubin D: Multiple Imputation for Nonresponse in Surveys, New York: Wiley, 1987 [Google Scholar]
  • 21.R Core Team: R: A language and environment for statistical computing. Foundation for Statistical Computing, Vienna, Austria: URL https://www.R-project.org/, 2017 [Google Scholar]
  • 22.StataCorp: Stata Statistical Software: Release 11. College Station, TX: StataCorp LP, 2009 [Google Scholar]
  • 23.Bates DMM, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015(67: 1–48). [Google Scholar]
  • 24.Clunn GF, Sever PS, Hughes AD. Calcium channel regulation in vascular smooth muscle cells: synergistic effects of statins and calcium channel blockers. International journal of cardiology. 2010;139(1):2–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Yang Z, Noll G, Luscher TF. Calcium antagonists differently inhibit proliferation of human coronary smooth muscle cells in response to pulsatile stretch and platelet-derived growth factor. Circulation. 1993;88(3):832–836. [DOI] [PubMed] [Google Scholar]
  • 26.Voisard R, Koschnick S, Baur R, et al. High-dose diltiazem prevents migration and proliferation of vascular smooth muscle cells in various in-vitro models of human coronary restenosis. Coronary artery disease. 1997;8(3–4):189–201. [DOI] [PubMed] [Google Scholar]
  • 27.Cristofori P, Crivellente F, Campagnola M, et al. Reduced progression of atherosclerosis in apolipoprotein E-deficient mice treated with lacidipine is associated with a decreased susceptibility of low-density lipoprotein to oxidation. International journal of experimental pathology. 2004;85(2):105–114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Jackson CL, Bush RC, Bowyer DE. Inhibitory effect of calcium antagonists on balloon catheter-induced arterial smooth muscle cell proliferation and lesion size. Atherosclerosis. 1988;69(2-3):115–122. [DOI] [PubMed] [Google Scholar]
  • 29.Wang JG, Staessen JA, Li Y, et al. Carotid intima-media thickness and antihypertensive treatment: a meta-analysis of randomized controlled trials. Stroke. 2006;37(7):1933–1940. [DOI] [PubMed] [Google Scholar]
  • 30.Brozovich FV, Nicholson CJ, Degen CV, Gao YZ, Aggarwal M, Morgan KG. Mechanisms of Vascular Smooth Muscle Contraction and the Basis for Pharmacologic Treatment of Smooth Muscle Disorders. Pharmacological reviews. 2016;68(2):476–532. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Valdivia HH, Valdivia C, Ma J, Coronado R. Direct binding of verapamil to the ryanodine receptor channel of sarcoplasmic reticulum. Biophysical journal. 1990;58(2):471–481. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Tabbara M, Duque JC, Martinez L, et al. Pre-existing and Postoperative Intimal Hyperplasia and Arteriovenous Fistula Outcomes. American journal of kidney diseases : the official journal of the National Kidney Foundation. 2016;68(3):455–464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Allon M, Robbin ML, Young CJ, et al. Preoperative venous intimal hyperplasia, postoperative arteriovenous fistula stenosis, and clinical fistula outcomes. Clinical journal of the American Society of Nephrology : CJASN. 2013;8(10):1750–1755. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Cheung AK, Imrey PB, Alpers CE, et al. Intimal Hyperplasia, Stenosis, and Arteriovenous Fistula Maturation Failure in the Hemodialysis Fistula Maturation Study. Journal of the American Society of Nephrology : JASN. 2017;28(10):3005–3013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Block LH, Emmons LR, Vogt E, Sachinidis A, Vetter W, Hoppe J. Ca2+-channel blockers inhibit the action of recombinant platelet-derived growth factor in vascular smooth muscle cells. Proceedings of the National Academy of Sciences of the United States of America. 1989;86(7):2388–2392. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.L LL, Lam JY, Hung J, Waters D. Oral verapamil inhibits platelet thrombus formation in humans. Circulation. 1994;89(2):630–634. [DOI] [PubMed] [Google Scholar]
  • 37.He GW. Verapamil plus nitroglycerin solution maximally preserves endothelial function of the radial artery: comparison with papaverine solution. The Journal of thoracic and cardiovascular surgery. 1998;115(6):1321–1327. [DOI] [PubMed] [Google Scholar]
  • 38.Kestenbaum B, Gillen DL, Sherrard DJ, Seliger S, Ball A, Stehman-Breen C. Calcium channel blocker use and mortality among patients with end-stage renal disease. Kidney Int. 2002;61(6):2157–2164. [DOI] [PubMed] [Google Scholar]
  • 39.Ishani A, Herzog CA, Collins AJ, Foley RN. Cardiac medications and their association with cardiovascular events in incident dialysis patients: cause or effect? Kidney Int. 2004;65(3):1017–1025. [DOI] [PubMed] [Google Scholar]
  • 40.Griffith TF, Chua BS, Allen AS, Klassen PS, Reddan DN, Szczech LA. Characteristics of treated hypertension in incident hemodialysis and peritoneal dialysis patients. American journal of kidney diseases : the official journal of the National Kidney Foundation. 2003;42(6):1260–1269. [DOI] [PubMed] [Google Scholar]
  • 41.Wetmore JB, Shireman TI. The ABCs of cardioprotection in dialysis patients: a systematic review. American journal of kidney diseases : the official journal of the National Kidney Foundation. 2009;53(3):457–466. [DOI] [PubMed] [Google Scholar]
  • 42.Wetmore JB, Mahnken JD, Phadnis MA, Ellerbeck EF, Shireman TI. Relationship between calcium channel blocker class and mortality in dialysis. Pharmacoepidemiology and drug safety. 2015;24(12):1249–1258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Elliott WJ, Ram CV. Calcium channel blockers. Journal of clinical hypertension (Greenwich, Conn). 2011;13(9):687–689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Georgianos PI, Agarwal R. Pharmacotherapy of Hypertension in Chronic Dialysis Patients. Clinical journal of the American Society of Nephrology : CJASN. 2016;11(11):2062–2075. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS983075-supplement-1.pdf (208KB, pdf)

RESOURCES