Published online before print October 23, 2006, doi: 10.1161/CIRCULATIONAHA.105.607390
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(Circulation. 2006;114:1914-1922.)
© 2006 American Heart Association, Inc.
Epidemiology |
Peripheral Arterial Disease in Patients With End-Stage Renal Disease
Observations From the Dialysis Outcomes and Practice Patterns Study (DOPPS)
From the Division of Cardiovascular Medicine, Ohio State University College of Medicine, Columbus (S.R); Zena and M.A. Wiener Cardiovascular Institute and M.-J. and H.R. Kravis Center for Cardiovascular Health, Mount Sinai Medical Center, New York, NY (S.R., S.D.); University Renal Research and Education Association (A.L.F., S.S., F.K.P.), Kidney Epidemiology and Cost Center (B.W.G., R.S.), Department of Biostatistics (B.W.G.), and Division of Nephrology, School of Medicine (R.S.), University of Michigan, Ann Arbor; University Hospital Renal Division, Ghent, Belgium (N.L.); Institute of Medical Science, Tokai University, Kanagawa, Japan (A.S.); Blood Purification and Internal Medicine, Tokyo Women’s Medical University, Tokyo, Japan (T.A.); Nephrology Department, Cliniques Universitaires St-Luc, Université Catholique de Louvain, Bruxelles, Belgium (M.J.); Department of Research, Renal Research Institute, New York, NY (N.G.); Department of Nephrology, Amgen, Inc, Thousand Oaks, Calif (M.K.); and Gill Heart Institute, Division of Cardiology, University of Kentucky, Lexington (D.M.).
Correspondence to Sanjay Rajagopalan, MD, Division of Cardiovascular Medicine, 473 W 12th Ave, Ohio State University, Columbus, OH 43202. E-mail Sanjay.Rajagopalan{at}osumc.edu
Received January 3, 2006; revision received July 25, 2006; accepted August 18, 2006.
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Abstract |
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Background— Patients with end-stage renal disease are at high risk for cardiovascular morbidity and mortality. The aims of the present study were to describe the prevalence of peripheral arterial disease (PAD) and its effects on prognosis and health-related quality of life (HRQOL) in an international cohort of patients on hemodialysis.
Methods and Results— Data from the Dialysis Outcomes and Practice Patterns Study (DOPPS), a prospective, international, observational study of hemodialysis patients (n=29 873), were analyzed. Associations between baseline clinical variables and PAD were evaluated by logistic regression analysis. Cox regression models were used to test the association between PAD and risk for all-cause mortality, cardiac mortality, and hospitalization. PAD was diagnosed in 7411 patients (25.3%) with significant geographic variation. Traditional cardiovascular risk factors including age, male sex, diabetes, hypertension, and smoking were identified, together with the duration of hemodialysis, as significant correlates of PAD. Diagnosis of PAD was associated with increased all-cause mortality (hazard ratio [HR]=1.36; P<0.0001), cardiac mortality (HR=1.43; P<0.0001), all-cause hospitalization (HR=1.19; P<0.0001), and hospitalization for a major adverse cardiovascular event (HR=2.05; P<0.0001). HRQOL questionnaires revealed physical health scores that were significantly lower in PAD compared with non-PAD patients (P<0.0001).
Conclusions— PAD is common in hemodialysis patients and is associated with increased risk of cardiovascular mortality, morbidity, and hospitalization and reduced HRQOL.
Key Words: renal insufficiency, chronic • epidemiology • renal dialysis • morbidity • mortality • peripheral vascular disease
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Introduction |
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End-stage renal disease (ESRD) is a powerful determinant of atherosclerotic vascular disease and is associated with a remarkably high incidence of cardiovascular morbidity and mortality.1 Peripheral arterial disease (PAD) is an important manifestation of systemic atherosclerosis and is common among ESRD patients.2,3 Most traditional risk factors for atherosclerosis (advanced age, smoking, and diabetes mellitus, in particular) are strongly associated with PAD, but the understanding of factors specifically influencing the development of PAD and its progression in ESRD patients is still quite limited.4 Compared with other forms of cardiovascular disease, relatively little attention has been paid to the overall prevalence of PAD in patients with ESRD and its effects on health-related quality of life (HRQOL) and, eventually, on long-term prognosis.
Clinical Perspective p 1922
We evaluated a large international cohort of patients on hemodialysis with the primary aim of assessing the effect of a diagnosis of PAD on prognosis in this patient population. Secondary aims were to describe the prevalence of PAD and its associations with clinical variables and, finally, to determine the effect of PAD on HRQOL measures in hemodialysis patients.
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Methods |
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Patient Selection
The data in the study were derived from the Dialysis Outcomes and Practice Patterns Study (DOPPS), a prospective observational study of adult hemodialysis patients. The first phase of the study (DOPPS I) included 308 nationally representative dialysis facilities in the United States (n=142), Europe (France, Germany, Italy, Spain, and United Kingdom; n=101), and Japan (n=65). The second phase (DOPPS II) included 320 dialysis facilities in the United States (n=80), Europe (Belgium, France, Germany, Italy, Spain, Sweden, and United Kingdom; n=140), Canada (n=20), Australia/New Zealand (n=20), and Japan (n=60). Patients were enrolled into DOPPS I during 1996–2001 in the United States, 1998–2000 in Europe, and 1999–2000 in Japan. All patients in DOPPS II were enrolled during 2002–2004. Detailed descriptions of the DOPPS design have been reported previously.5 In brief, facilities participating in DOPPS were selected randomly from a list of all dialysis units within each country. Institutional review board approval and informed consent were obtained as required in each country. Selection was stratified by geographic region and by type of dialysis facility (eg, hospital, private clinic, national chain) within each country. A random sample of 20 to 40 patients (aged
18 years), representing a prevalent cross section, was initially selected from each facility (DOPPS I, n=8615; DOPPS II, n=9117). As patients departed the study (because of death, transplantation, withdrawal, transfer, or dialysis modality changes), replacement patients were randomly selected in DOPPS I (n=8419). Patients new to hemodialysis were added sequentially in DOPPS II (n=3722) from the pool of patients new to the facility since the last sample selection. In the present study, the total sample of 29 873 patients was used for all analyses unless otherwise noted.
Data Collection
Data regarding patient demographic characteristics, comorbidities, laboratory values, and medical history were collected at patient entry with the use of standardized data collection instruments and used to assess associations. The variables considered for analysis included age, race (black versus nonblack), ethnicity (Hispanic versus non-Hispanic), sex, body mass index, vintage (time from start of ESRD to DOPPS study entry), country, cigarette smoking (nonsmoker versus current smoker, former smoker <1 year, and former smoker >1 year), hemoglobin, albumin, single-pool Kt/V (K indicates dialyzer clearance of the urea [provided by the manufacturer]; t, dialysis time; and V, patient’s total body water), normalized protein catabolic rate, total cholesterol, serum phosphorus, calcium, calcium-phosphorus product, parathyroid hormone level, pulse pressure (predialysis systolic blood pressure–predialysis diastolic blood pressure), and mean arterial pressure (predialysis diastolic blood pressure+1/3xpulse pressure). Information was also collected on comorbid conditions, including coronary artery disease, congestive heart failure, other cardiovascular disease (eg, atrial fibrillation, arrhythmias, left ventricular hypertrophy, pericarditis, valvular heart disease, prosthetic heart valve, and hyperlipidemia), cerebrovascular disease, hypertension, diabetes mellitus (types 1 and 2), cancer, gastrointestinal bleeding, HIV/AIDS, lung disease, neurological disease, and psychiatric disorder (including depression).
At baseline, PAD was determined by chart review and included a diagnosis for at least 1 of the following conditions: (1) prior diagnosis of PAD; (2) intermittent claudication; (3) critical limb ischemia encompassing rest pain, skin necrosis, and gangrene and may include recurrent skin infections; (4) surgical revascularization for PAD; (5) amputation for PAD; and (6) aortic aneurysm or surgery for aortic aneurysm. Patients with missing data for the presence or absence of PAD (n=618) were excluded from the analysis.
Events and corresponding dates were recorded by the facility coordinator on interval summary questionnaires collected every 4 months for all-cause deaths, cardiovascular deaths (attributed to acute myocardial infarction [MI], atherosclerotic heart disease, cardiac arrest of unknown cause), all-cause hospitalization, and hospitalization due to major adverse cardiovascular events (MACE) (including MI, stroke, amputation, coronary artery bypass graft surgery, peripheral arterial bypass surgery, and carotid endarterectomy). The mean follow-up period was 1.5 years (range, 0 to 5.4 years) for DOPPS I and 1.5 years (range, 0 to 3.3 years) for DOPPS II.
The DOPPS Patient Questionnaire was completed at baseline and at each 12-month follow-up for both DOPPS I and II. This included HRQOL questions, assessed through the Medical Outcomes Study Short Form Health Survey (SF-36) in DOPPS I and SF-12 in DOPPS II.6,7 The SF-36 and SF-12 are validated questionnaires that include a health transition item and assess 4 physical health domains (physical functioning, role–physical, bodily pain, general health) to derive a physical component summary. The SF-36 and SF-12 scales are both scored from 0 to 100, with a higher score representing better health.
Statistical Analysis
Baseline population characteristics are presented with the use of standard descriptive statistics and unadjusted tests of association. Unadjusted pairwise correlations among baseline laboratory measures and comorbid conditions were used to check for potential collinearity before inclusion in regression analyses. Logistic regression analyses were used to test associations between the presence of PAD and baseline demographic variables and comorbidities. Linear mixed models, with SF-36 subscales as the outcome, were used to estimate associations between PAD and HRQOL measures. Logistic and linear mixed models were adjusted for demographics, medication use, comorbidities, study phase, and country, as noted in table footnotes. Models also accounted for facility clustering assuming a compound symmetry (working) covariance structure: for logistic regression, a robust variance with generalized estimating equations was used; for linear mixed models, a model-based variance was used.
All-cause mortality, MI-specific mortality, and total cardiac mortality, as well as time to first all-cause and MACE hospitalization, were analyzed with the use of Cox regression models. Mortality models were based on time from ESRD, left-truncated at study entry. Hospitalization analyses were based on time from study entry to first hospitalization, with vintage included as a covariate (hospitalizations were not collected before study entry). The standard errors of parameter estimates were adjusted for facility clustering by using a robust (sandwich) estimator, assuming an independence working covariance structure.8 Censoring was due to transplantation, withdrawal from dialysis, transfer to another facility, change in dialysis modality, death (for hospitalization models only), or study end. All models were adjusted for age, race, sex, 12 comorbid conditions (listed above), laboratory measures, and (for hospitalization models) vintage. Cox regression models were stratified by study phase and region (United States/Canada, Europe, and Japan) to adjust for possible variation in the baseline hazard function across time for each stratum level. All analyses were performed with the use of SAS software version 9.1 (SAS Institute, Cary, NC).
The authors of this article had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
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Results |
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The prevalence of PAD in the total DOPPS patient population was 25.3% (n=7411), with wide variation among the 12 DOPPS countries, from 12% in Japan to 38% in Belgium (Figure 1). In every country, the pattern of prevalence for the PAD diagnostic categories was similar, with the most common category being a previous diagnosis of PAD.
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Associations With PAD
Table 1 outlines the demographic characteristics of the PAD patient population versus patients without PAD. Overall, PAD patients had a higher prevalence of comorbidities, including coronary artery disease (65.8% versus 34.0%; P<0.0001), congestive heart failure (51.0% versus 26.8%; P<0.0001), other cardiovascular diseases (46.2% versus 28.2%; P<0.0001), and cerebrovascular disease (28.7% versus 12.3%; P<0.0001). Hypertension (86.2% versus 75.5%; P<0.0001) and diabetes mellitus (59.1% versus 30.5%; P<0.0001) were also significantly more common among PAD patients than among patients without PAD.
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Table 2 summarizes the multivariable associations with PAD in the entire DOPPS population. The adjusted odds ratio (OR) of having PAD was 1.39 for men versus women (P<0.0001). Hispanics exhibited 22% lower odds of having PAD (adjusted OR=0.78; P=0.004) versus non-Hispanics, and blacks had 21% lower odds of having PAD (adjusted OR=0.79; P<0.0001) versus nonblacks. Compared with the United States, the odds of having PAD were significantly higher in France (adjusted OR=1.82; P=0.02) and were 48% lower in Japan (adjusted OR=0.52; P=0.004).
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Diagnoses related to atherosclerosis and its complications were significantly associated with PAD, including coronary artery disease, cerebrovascular disease, congestive heart failure, and hypertension (adjusted OR=1.89, 1.75, 1.40, 1.31, respectively; P<0.0001 for all comparisons). The odds of having PAD were 6% higher for each 10 mm Hg higher pulse pressure (adjusted OR=1.06; P<0.0001). However, for each 10 mm Hg higher mean arterial pressure, the odds for PAD were 7% lower (adjusted OR=0.93; P<0.0001). Predialysis systolic blood pressure was tested in a multivariable model without mean arterial pressure or pulse pressure included and was not significantly associated with PAD (adjusted OR=1.00 per 10 mm Hg increase in systolic blood pressure; P=0.59; results not shown). Compared with nonsmokers, the adjusted OR of having PAD was 1.46 for current smokers, 1.68 for smokers who quit <12 months before study entry, and 1.51 for former smokers who quit >12 months before study entry (P<0.0001 for all comparisons). PAD was negatively associated with serum albumin (adjusted OR=0.83; P<0.0001), but there were no associations with other uremia-specific variables such as calcium, phosphate, calcium-phosphate product, or parathyroid hormone levels (data not shown).
In regard to its association with PAD, diabetes mellitus had significant interactions with age (P<0.0001) and vintage (in years) (P<0.0001). Figure 2 details the association between diabetes and PAD for varying levels of age and vintage on dialysis therapy, with a 60-year-old nondiabetic patient with 1-year vintage used as a reference. Figure 2A shows increasing odds of PAD for each additional year of ESRD (vintage) for patients with diabetes but no additional effect of age. In contrast, Figure 2B shows that for nondiabetic patients, the odds of PAD increase noticeably with age but to a lesser extent with vintage.
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PAD and Prognosis
For the overall DOPPS population, PAD patients presented a significantly higher risk of all-cause death (hazard ratio [HR]=1.36; P<0.0001), MI-related death (HR=1.38; P<0.0001), and cardiac death (HR=1.43; P<0.0001) versus non-PAD patients. These results were consistent across the regions of DOPPS but seemed particularly strong for Japan (Figure 3). Because of a limited sample size, however, PAD was not a significant predictor for MI-related deaths in Japan. The effect of a diagnosis of PAD on survival in hemodialysis patients is illustrated in Figure 4 by region. For simplicity, curves shown are for DOPPS II only (results were similar for DOPPS I). Although the impact of PAD on survival is evident in all regions, in Japan patients both with and without PAD have a better survival than their counterparts in Europe and the United States/Canada.
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Death from MI, cardiac arrest/sudden death, and atherosclerotic heart disease.
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The prognostic value of a diagnosis of PAD was further tested in the subgroup of patients with no baseline history of cardiovascular diseases (eg, coronary artery disease, other cardiovascular disease, or congestive heart failure; DOPPS I and II, n=11 687). The all-cause mortality risk in this subset was 1.41 (P<0.0001), the cardiac-specific mortality risk was 1.45 (P=0.006), and the MI-specific mortality risk was 1.59 (P=0.058) in patients with PAD versus non-PAD patients.
A diagnosis of PAD also had a significant impact on hospitalization for the entire DOPPS population, with a greater risk of all-cause hospitalization (HR=1.19; P<0.0001) and MACE-related hospitalization (HR=2.05; P<0.0001). Results were similar across the DOPPS regions (Figure 5). Again, MACE results for Japan were not significant because of limited sample size. Statin and aspirin use was higher in PAD patients than in non-PAD patients (21.8% vs 12.9%, P<0.001, and 33.5% vs 20.0%, P<0.0001).
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PAD and HRQOL
As shown in Table 3, after adjustment for demographics, baseline comorbidities, laboratory results, and medication use, we found the average SF-36 physical component summary score, physical functioning score, and role–physical score to be significantly lower among patients with PAD than among those without PAD (–2.59, –7.53, and –5.98, respectively; all P<0.0001).
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Discussion |
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The major findings of the present prospective, international, observational study of hemodialysis patients are as follows: (1) Clinically manifest PAD is frequent among the hemodialysis patient population with significant variation by country/geographic region. (2) PAD is an independent, powerful determinant of all-cause mortality and cardiovascular events in ESRD. (3) Traditional correlates of PAD, such as diabetes, hypertension, smoking, male sex, and preexisting cardiovascular diseases, remain operative in this patient population. (4) PAD in hemodialysis patients is associated with lower HRQOL.
Results from the present study provide new insights on the impact of a diagnosis of PAD on survival and risk for recurrent cardiovascular events in dialysis patients. PAD is a powerful determinant of overall survival in nondialysis subjects. Our data in ESRD patients confirm the independent predictive value of a diagnosis of PAD in determining death and other adverse outcomes, even after adjustment for a wide range of variables known to alter cardiovascular risk in this already high-risk patient population. This increase in relative risk was fairly uniform across countries, although it was slightly higher in Japan possibly because non-PAD survival in Japan was better than in the United States/Canada and Europe (Figure 4). Thus, PAD represents an indicator of a poor prognosis in hemodialysis patients and may help to identify a subset at very high risk that may benefit from aggressive therapeutic intervention. These results are even more striking when we consider that the increased risk for mortality and morbidity in PAD subjects was also observed in patients without prior cardiovascular disease and despite a relatively higher use of statins and aspirin in this group.8
The data on hospitalization provide an insight into the remarkable impact of having PAD and being on hemodialysis, with a >2-fold increase in the risk of hospitalization for MACE and a 19% increase in the risk for all-cause hospitalizations. As a consequence, cardiovascular causes appear to be responsible for a disproportionate expenditure in healthcare for this subgroup of patients.9
A powerful association between diabetes mellitus and PAD was demonstrated in this hemodialysis population, with age and vintage showing a significant interactive effect in modulating the effect of diabetes. Whereas vintage appeared to be able to modulate the risk of having PAD in nondiabetic as well as diabetic patients, age modulated the risk for PAD only in patients without diabetes mellitus. In patients with diabetes the effect of age was not apparent, possibly because of the overwhelming impact of a diagnosis of diabetes on PAD propensity or because of a higher representation of relatively younger type I diabetics among hemodialysis patients with clinically manifest PAD.
Correlates of PAD in ESRD patients have been examined in only limited studies. In the Hemodialysis (HEMO) Study (n=936 dialysis patients), diabetes and smoking, but not hypercholesterolemia, male sex, or hypertension, were more common in patients who had been diagnosed with PAD.10 This study did not consider uremia-related variables possibly associated with PAD. A report based on data from the US Renal Data System (USRDS) Dialysis Morbidity and Mortality Study examined the cross-sectional association of a range of conventional cardiovascular risk factors and dialysis-related variables in a large group of dialysis patients enrolled in the United States.11 Among these patients, the diagnosis of PAD was associated with increased age, male sex, white race, diabetes, smoking, increased diastolic blood pressure, and left ventricular hypertrophy. In an adjusted analysis, PAD was also associated with low serum parathyroid hormone levels, longer vintage, and low serum albumin. Between the uremia-related variables associated with PAD, in our data the association was direct with vintage and inverse with albumin.
Other traditional correlates of PAD, such as age, male sex, smoking (current or former), and preexisting cardiovascular diseases, remain operative in patients with ESRD. Pulse pressure was positively associated with PAD in this hemodialysis population, whereas mean arterial pressure was negatively associated with prevalence of PAD. These apparently contradictory findings can be explained when one considers the frequent significant reduction in arterial compliance (with increased pulse pressure) observed in ESRD patients.12 Furthermore, many previous studies failed to demonstrate an association between high blood pressure and PAD in dialysis populations.10,11
The inclusion in the DOPPS study of dialysis facilities from 12 different countries allowed a comparative analysis, revealing differences in the probability of a diagnosis of PAD and morbidity/mortality based on the geographic location in hemodialysis subjects. For instance, the odds of a PAD diagnosis were 82% higher in France and 48% lower in Japan versus the United States. These data on prevalence are consistent with a lower mortality risk noted in Japan versus the United States/Canada. The higher adjusted odds of clinical PAD among French dialysis patients (compared with the United States as reference category) appear to be at variance with the "French paradox" of lower cardiovascular disease despite presence of cardiovascular risk factors.13 However, it is consistent with the high hazard for hospitalization for cardiovascular disease among French dialysis patients seen in DOPPS compared with other European countries.14 It is possible that these country differences in prevalence of PAD could reflect variation in PAD diagnostic criteria and threshold for intervention and potential genetic variation among countries. Although enrollment bias may also account for some of the country variation, the likelihood is minimized in DOPPS by random selection of patients at each participating facility.
In the present study, Hispanics (compared with non-Hispanics) and blacks (versus nonblacks) revealed similarly decreased odds of having PAD, consistent with the lower risk of mortality among dialysis patients belonging to these 2 population groups compared with non-Hispanic whites.15–17 It is possible that variation in susceptibility and differential chronicity of exposure to various risk factors (eg, smoking, diabetes, hypertension) among the different ethnic groups may have influenced these results. Of relevance, these data on hemodialysis patients are in contrast to the findings of the San Diego Population Study, which has reported a greater susceptibility of blacks but not of Hispanics for the development of PAD (defined as ankle-brachial index 
0.90) in a non-ESRD population.18
Despite continuing advancements in the treatment of ESRD, the HRQOL of ESRD patients is considerably lower than that of the general population.6,19 In previous studies, lower scores for physical and mental summary scores have been strongly associated with higher risk of death and hospitalization in dialysis patients, with predictive power similar or superior to conventional determinants, such as serum albumin level.7 Previous reports have demonstrated reduced values for HRQOL indices in PAD patients, comparable to those of patients with terminal malignancies and heart failure.20 It is reasonable that the additional attrition in HRQOL indices due to the presence of PAD may powerfully modulate hospitalization and cardiovascular event rates in this hemodialysis patient population.19,21
Limitations of the Study
DOPPS is an observational study, and the limitations inherent to such an analysis are applicable to this study. In many studies (including the present) evaluating the prevalence of PAD, the diagnosis of this condition is clinical and not based on the rigorous application of uniform diagnostic criteria (eg, ankle-brachial index). Significant underascertainment of the diagnosis of PAD is possible in these circumstances. Conversely, overascertainment would result if some facilities/countries systematically sought subclinical PAD. Information on this type of practice pattern is not available in DOPPS at the present time. This study captured data on surgical revascularization procedures but not on percutaneous procedures, which may have contributed further to underreporting of diagnosis in this patient population. We did not test the association of PAD with defined lipid categories such as low-density lipoprotein or triglycerides because these data were not available on all patients. Webb et al22 demonstrated an important association between triglyceride levels and PAD in dialysis patients. In contrast, the HEMO and USRDS cohorts did not demonstrate such an association with lipid parameters and PAD.10,11 It is certainly possible that PAD outcomes may have correlated with lipid parameters; however, the high use of statins in this trial may have further attenuated some of these relationships. Detailed information on smoking status, including pack-year data for smoking history, was not collected in the study, and it is entirely possible that this may have influenced outcomes in PAD, as demonstrated in prior studies.10,11 Finally, we acknowledge that our results may have been affected by unknown variables such as social support and economic status that may cosegregate with other variables such as HRQOL parameters.23
Conclusions
To the best of our knowledge, this is the first study to provide detailed international comparisons of PAD and its correlates in the hemodialysis patient population. Overall, a high prevalence of clinically manifest PAD is observed, with significant geographic variation. A diagnosis of PAD powerfully modulates cardiovascular mortality, hospitalization risk, and HRQO in hemodialysis patients. These findings provide new insights into a subgroup of patients on hemodialysis who may need special medical and social attention.
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Acknowledgments |
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Sources of Funding
The Dialysis Outcomes and Practice Patterns Study is supported by research grants from Amgen and Kirin without restrictions on publications.
Disclosures
None.
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References |
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Footnotes |
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*The first 2 authors contributed equally to this work.
Presented in part at the American Society of Nephrology Renal Week 2003, San Diego, Calif, November 12–17, 2003, and published in abstract form (J Am Soc Nephrol. 2003;14:494A).
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S. R. Orth and S. I. Hallan Smoking: A Risk Factor for Progression of Chronic Kidney Disease and for Cardiovascular Morbidity and Mortality in Renal Patients Absence of Evidence or Evidence of Absence? Clin. J. Am. Soc. Nephrol., January 1, 2008; 3(1): 226 - 236. [Abstract] [Full Text] [PDF]
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