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Saudi Journal of Kidney Diseases and Transplantation
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Table of Contents   
ORIGINAL ARTICLE  
Year : 2020  |  Volume : 31  |  Issue : 1  |  Page : 90-99
Peripheral arterial disease diagnosed by ankle–brachial index: Predictor for early renal replacement therapy in chronic kidney disease


1 Department of Internal Medicine, University of Health Sciences, Kartal Dr. Lutfi Kirdar Training and Research Hospital, Istanbul, Turkey
2 Department of Nephrology, University of Health Sciences, Kartal Dr. Lutfi Kirdar Training and Research Hospital, Istanbul, Turkey

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Date of Submission04-Aug-2018
Date of Decision17-Sep-2018
Date of Acceptance19-Sep-2018
Date of Web Publication3-Mar-2020
 

   Abstract 

Our study aimed to investigate the relationship between ankle-brachial index (ABI) and need for early renal replacement therapy (RRT) in predialysis patients with chronic kidney disease (CKD). A total of 112 patients (62% men) with pre-dialysis CKD, seen in the outpatient clinic, were included, and ABI was obtained as per standard protocol. Peripheral arterial disease (PAD) was defined as ABI <0.9 or >1.3 in either leg. The clinical data were analyzed, and the risk factors for early RRT were determined by multivariate logistic regression analysis. The prevalence of PAD was 44% in predialysis CKD patients. Over three years’ follow- up, 14.2% required RRT; 11.3% developed major cardiovascular event (myocardial infarction, stroke, or death). A total of 26 events occurred. The incidence of all events was significantly higher in patients with abnormal ABI than in those with normal ABI (34.7% vs. 12.7%; log rank P = 0.02). PAD was associated with all events [hazard ratio (HR): 2.72; 95% CI: 1.04-7.17; P = 0.042] as also the need for RRT (HR 3.2; 95% Cl: 1.005-10.23; P = 0.049), on univariate cox proportional hazard analysis. Multivariate logistic regression analysis adjusted for other risk factors identified that PAD remained an independent predictor for the need for early RRT (HR: 12.2; 95%Cl: 2.2-66.5; P = 0.004) and all events (HR: 3.5; 95% Cl: 0.9-13.5; P = 0.032). PAD was an independent predictor for RRT requirement in predialysis CKD.

How to cite this article:
Ozgur Y, Akin S, Parmaksiz E, Meşe M, Bahcebasi ZB, Keskin O. Peripheral arterial disease diagnosed by ankle–brachial index: Predictor for early renal replacement therapy in chronic kidney disease. Saudi J Kidney Dis Transpl 2020;31:90-9

How to cite this URL:
Ozgur Y, Akin S, Parmaksiz E, Meşe M, Bahcebasi ZB, Keskin O. Peripheral arterial disease diagnosed by ankle–brachial index: Predictor for early renal replacement therapy in chronic kidney disease. Saudi J Kidney Dis Transpl [serial online] 2020 [cited 2020 Apr 4];31:90-9. Available from: http://www.sjkdt.org/text.asp?2020/31/1/90/279965

   Introduction Top


Patients with chronic kidney disease (CKD) have a higher prevalence of peripheral artery disease (PAD) compared to the general popu- lation.[1] The ankle-brachial index (ABI) is a simple, inexpensive, and noninvasive measure of subclinical PAD with high specificity.[2] Traditionally, an ABI cut-point of <0.9 is useful in diagnosis of subclinical peripheral arterial atherosclerosis.[3],[4] In addition, previous studies report that an ABI >1.3 is associated with vascular calcification in peripheral and distal medial arteries[4],[5] and significantly related to future clinical PAD among predialysis CKD patients.[6]

PAD is widely known as a strong predictor of future cardiovascular and cerebrovascular morbidity and mortality.[7],[8] It has been demonstrated that PAD and CKD share a number of risk factors, and patients with CKD are at an increased risk of PAD.[9] Furthermore, an ABI <0.9 has been shown to be a significant predictor of mortality in patients with advanced- stage CKD or who are undergoing hemodialysis (HD).[10] Both low and high ABI have been associated with increased cardiovascular disease (CVD) morbidity and mortality in the general population.[11] Adragao et al reported that both low (<0.9) and high (>1.3) ABI were independently associated with all-cause and CVD mortality in 219 HD patients.[12]

Data from the National Health and Nutrition Examination Survey indicate that 24% of persons with creatinine clearance <60 mL/min/ 1.73 m2 have prevalent PAD, defined as ABI <0.9, compared with only 3.7% of persons with creatinine clearance >60 mL/min/1.73 m.[13] In another study, the prevalence of PAD was 12.4%, in which 39.7% were CKD patients.[14]

We could not find any study that shows the correlation between the time of onset of renal replacement therapy (RRT) in predialysis CKD patients and abnormal ABI. In this study, we assessed whether or not a low (<0.9) and a high ABI (>1.3) were able to predict future cardiovascular events (CVE), time of onset of RRT, and mortality.


   Materials and Methods Top


Study populations

In this cross-sectional, observational study, 112 patients of CKD without RRT who were admitted to nephrology outpatient department, between June and August 2015, were approached. Patients who had myocardial infarction (MI), cerebrovascular accident, polycystic kidney disease, renal cell carcinoma, those on HD or peritoneal dialysis (PD) or recipients of a kidney transplant were excluded. Patients with diabetes mellitus (DM), hypertension (HTN), hyperlipidemia and coro- nary heart disease (CHD) and also CKD without RRT were included. All patients had their ABI measured on admission. This study was performed in accordance with the Declaration of Helsinki and was approved by the ethics committee of University of Health Sciences, Kartal Dr. Lutfi Kirdar Training and Research Hospital (89513307/1009/430-26/03/2015).

Data collection

Data were collected from the clinical records. At the baseline examination, medical history and demographic information were collected by trained research staff using standard questionnaires. Body weight and height were measured and used to calculate body mass index (in kg/m2). Waist circumference was measured midway between the lowest rib and the iliac crest.

Blood pressure (BP) measurements were obtained by trained staff after >5 min of quiet rest and systolic and diastolic values were recorded separately. HTN was defined as systolic BP >140 mm Hg and/or diastolic BP >90 mm Hg and/or normal BP under the treatment of antihypertensive medication.

DM was defined as a fasting glucose >126 mg/dL, or a random glucose >200 mg/dL, and/ or normal glucose level under the treatment of antidiabetic medication. Urinary albumin was measured by radioimmunoassay. Urinary albumin creatinine ratio (UACR) (expressed in mg/g) was calculated by dividing urinary albumin to creatinine from spot urine samples. Likewise, urinary protein creatinine ratio (UPCR) was calculated by dividing urinary protein to creatinine from spot urine samples.

The estimated glomerular filtration rate (eGFR) was calculated using the chronic kidney disease Epidemiology Collaboration formula after calibrating serum creatinine measurements.

Follow-up data were collected from medical records. Clinical diagnosis of CVD was concluded with history, electrocardiography, echocardiography, and physical examination by the following physician. Deaths were confirmed by death certificate of the National Death Notification System.

Ankle-brachial index measurement

The ABI measurements were obtained with the standard protocol according to ACCF/AHA 2011 Practice Guidelines for the management of patients with PAD.[2] After the participant rested supine for 5 min, systolic BP was measured in both arms with the appropriate-sized arm cuff. For each leg, systolic BP in each posterior tibial and dorsalis pedis artery was measured. All pressures were detected with a continuous-wave Doppler ultrasound probe. The ABI was calculated as the ratio of the higher systolic BP in the lower extremity divided by the higher systolic BP of the upper extremities.

We categorized ABI into three groups: normal (0.9 <ABI<1.3), low (ABI <0.9), and high (ABI >1.3). PAD was defined as ABI <0.9 or >1.3.


   Statistical Analysis Top


All statistical analyses were performed using IBM Statistics for Windows version 24.0 software (IBM Corp., Armonk, NY, USA). Normal data distribution was assessed via the Kolmogorov-Smirnov test. Variables were expressed as mean (standard deviation) for normally distributed and were reported as median and interquartile range (25 th-75 th percentile) for variables that were not normally distributed. Categorical variables were reported as numbers and percentages. Patient characteristics among each group were compared by the Chi-squared test for categorical variables and the Mann-Whitney U-test for continuous variables. Overall occurrences of major CVE (MACE), the onset of RRT, and all-cause death were calculated using the Kaplan-Meier method, and differences between the groups were compared by the logrank test. Univariate Cox proportional hazard analyses were performed to identify the independent predictors of RRT requirement and all events (myocardial infarction, stroke, death, or RRT requirement). Variables that exhibited P <0.05 in the univariate analysis were included in the multivariate model. The magnitude of the relationship between the variables and all events were expressed as the hazard ratio (HR) and 95% confidence interval (CI). P values of <0.05 were considered as statistically significant.


   Results Top


Characteristics of the study population

The baseline characteristics according to ABI categories are shown in [Table 1]. A total of 38 of the patients (34%) had low ABI and 11 (10%) had high ABI. The patients whose ABIs were outside the normal reference range (<0.9 or >1.3) were defined to have PAD. In this respect, the PAD prevalence in CKD patients was determined to be 44%. The ABI of the other 63 patients (56%) was within normal limits. When the sociodemographic data of the groups were compared, there were significant differences between the groups in terms of gender and height (P = 0.002 and 0.02, respectively). All the patients who had high ABI values, 44.7% of the patients with low ABI, and 66.7% of those who had normal ABI were male. The mean height of the patients who had high ABI was more both in those who had low and normal range.
Table 1: Baseline characteristics of study participants according to ankle–brachial index.

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When the patients were compared in terms of chronic diseases, it was determined that there were no significant differences in terms of DM, HTN, CHD, and hyperlipidemia according to ABI. Only in the patients with low ABI, the duration of DM disease was significantly more (P = 0.003) and the mean SBP was higher (P = 0.019) and the HbA1c rate was higher (P = 0.022) compared to the other groups. The mean serum urea levels and mean serum phosphorus (P) levels were significantly lower in patients with higher ABI level compared to the other groups (P = 0.001 and 0.016, respectively). While the UPCRs were significantly higher in the group with lower ABI compared to the other two groups, the UACRs were lower in the group with higher ABI levels compared to the other two groups (P = 0.001 and 0.004, respectively).

Cumulative survival of all events (myocardial infarction, stroke, death or renal replacement therapy requirement)

In the average three-year follow-up period, 12 MACE occurred (with 5 deaths in total; all deaths were due to cardiovascular reasons), and 15 patients started RRT. One patient developed both MACE and early commencement of RRT.

As a result, in 26 of the total 112 patients, these events (MI, stroke, starting RRT, or death) took place. Seventeen of these events were observed in patients with low ABI levels; eight were in normal ones; and one occurred in a patient with high ABI.

The Kaplan-Meier analysis, which was performed in this context, showed that the cumulative hazard rate in the development of any event occurring during the three-year follow-up period was 2.7 times higher (log rank P = 0.02) in those who had PAD than those without PAD. The estimated event development time was 32.6 months (95% CI: 30.834.5) and 33.2 months (95% CI: 31.4-35.3) in the PAD (+) and PAD (-) patients, respectively [Figure 1].
Figure 1: Kaplan–Meier plot for (a) all events (myocardial infarction, stroke, death, beginning renal replacement therapy), (b) only renal replacement therapy, (c) major cardiovascular events (myocardial infarction, stroke, cardiovascular death), (d) all-cause death.

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During the three-year follow-up period, 11 patients with PAD (+); and four patients with PAD (-) were started on RRT. The risk of having early onset of RRT in patients with PAD (+) was 3.2 times higher than those with PAD (-) (log rank P = 0.037).

Of the 12 patients who had MACE, eight were in the PAD (+) group while four were in the PAD (-) group. Of the five patients who died after three years of follow-up period, four patients were in the PAD (+) group while one patient was in the PAD (-) group. In the Kaplan-Meier analysis, there was no significant risk increase (log rank P = 0.13 and 0.12, respectively) in terms of MACE and death risk due to the small number of the patients and inadequate follow-up period.

Predictors of all events (major cardiovascular events including death and need for renal replacement therapy)

We performed Cox proportional hazard analyses to evaluate the prognostic value of PAD for all events. Univariate analysis showed that the HR of PAD for all events was 2.72 (95% Cl: 1.04-7.17; P = 0.042). After adjusting for CHD, duration of CHD, hemoglobin levels, neutrophil lymphocyte ratio (NLR), urea, crea- tinine, P, and UACR, PAD remained as an independent predictor of all events (HR: 3.5; Cl: 0.9-13.5; P = 0.032). [Table 2] and [Table 2]A contain the results of univariate and multi- variate Cox regression analyses for predictors of all events.
Table 2:

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Predictors of renal replacement therapy requirement

After the three-year observation period, while RRT was required in four of the PAD (-) patients (6.9%), 11 patients required RRT in PAD (+) patients (22.9%). Univariate analysis showed that the HR of PAD for RRT requirement was 3.2 (95% Cl: 1.005-10.23; P = 0.049). After adjusting for NLR, urea, eGFR, parathyroid hormone (pTH), and UACR, PAD remained as an independent predictor of early RRT requirement (HR: 12.27; Cl: 2.2-66.5; P = 0.004). [Table 3] and [Table 3]A contain the result of univariate and multivariate Cox regression analyses for prediction of requirement for RRT.
Table 3:

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   Discussion Top


When we reviewed the literature, we found limited number of studies conducted on ABI as an indicator of prognosis in CKD patients. However, we did not find any studies on the effect of ABI on the onset time of the RRT. For this reason, the present study is important in that it is the first in the literature. Although we have not reached a conclusion point with a three-year observation yet, we detected an increased risk in the requirement for RRT onset in our CKD patients who had PAD in our study which we conducted with limited number of patients. Another important finding of the current study was that PAD was an independent predictor for RRT requirement. Additional independent predictors of RRT requirement were NLR, urea, PTH, UACR, and eGFR.

It has been recently shown that NLR may be an indicator for the inflammation predicting CVD. An earlier study revealed a positive correlation between NLR and brachial-ankle pulse wave velocity.[15] These findings have important clinical and public health implications because patients with CKD are at an increased risk of developing PAD.[13],[16] In addition, CKD patients with PAD have a very high risk of CVD and all-cause mortality.[3],[17] Proper detection and intervention are the keys to prevent adverse CVD outcomes associated with PAD among patients with CKD. Vascular calcification is highly prevalent in CKD patients.[18],[19] Medial arterial calcification is common in CKD patients and causes arterial stiffness, a decrease in perfusion, and impairment of collateral circulation formation, which may contribute to PAD.[20],[21]

A previous study indicated that ABI <1.0 was related to risk of PAD, MI, composite CVD, and all-cause mortality whereas ABI >1.4 was related to clinical PAD. According to the study, these findings suggest that ABI cutpoints of <1.0 or >1.4 for diagnosing PAD and ABI <1.0 for CVD risk stratification should be further evaluated among CKD patients.[6]

According to another study, in a follow-up of nearly 10 years, 9% experienced rapid eGFR decline,[22] and compared with patients with normal ABI, low ABI was associated with an increased risk of rapid eGFR decline, suggesting that systemic atherosclerosis predicts decline in kidney function.[23] In patients, both decreased GFR and albuminuria were associated with an elevated prevalence of PAD, and the reported prevalence of ABI <0.9 was 45.7% in Spain.[24]

While most prior studies defined CKD as an eGFR <60 mL/min/1.73 m2, several recent findings have suggested that increased cardiovascular morbidity and mortality are already observed at eGFR levels below 90 mL/min/ 1.73 m2.[25],[26],[27] Despite its poor prognosis, the combined effect of PAD and CKD has not been discussed. Nishimura et al showed an increase in prevalence of low ABI in patients with mild renal insufficiency compared to the normal population (9.2% and 2.7%, respec- tively).[28]

Early detection of low ABI in CKD patients and treatment based on latest ACCF/AHA guidelines for the management of patients with PAD may help prevent the progression of cardiovascular disease.

We detected an increased risk for RRT requirement in patients who had PAD in the present study, which was conducted on limited number of patients. We could not detect the same risk increase in MACE or in deaths due to all reasons and the limited patient number and the limited duration of follow-up of three years. However, in a few previous studies that were conducted for longer durations, it was determined that low ABI or PAD caused an increased risk of both mortality and Cve.[10],[25],[28] However, PAD was not examined in terms of its effects on the onset time of renal replacement treatments such as HD or PD. There are only two studies on rapid eGFR decrease.[22],[23] Although we cannot measure indices of PAD directly in the aortic branches like in peripheral arteries, we can foresee that atherosclerosis starts, vascular calcification develops, the perfusion of the kidneys is impaired, and ESRD progression is accelerated. We concluded significant results by evaluating the peripheral arteries and predicting the renal arteries when we indirectly approached them. However, we believe that this study should be strengthened with wider studies in which renal artery measurement can be performed directly.

Conflict of interest: None declared.



 
   References Top

1.
Vanrenterghem Y, Ponticelli C, Morales JM, et al. Prevalence and management of anemia in renal transplant recipients: a European survey. Am J Transplant 2003;3:835-45.5.  Back to cited text no. 1
    
2.
2011 Writing Group Members; 2005 Writing Committee Members; ACCF/AHA Task Force Members. 2011 ACCF/AHA focused update of the guideline for the management of patients with peripheral artery disease (Updating the 2005 Guideline): A report of the American College of Cardiology Foundation/American heart Association Task Force on practice guidelines. Circulation 2011;124:2020-45.  Back to cited text no. 2
    
3.
Hirsch AT, Haskal ZJ, Hertzer NR, et al. ACC/AHA Guidelines for the Management of Patients with Peripheral Arterial Disease (lower extremity, renal, mesenteric, and abdominal aortic): a collaborative report from the American Associations for Vascular Surgery/ Society for Vascular Surgery, Society for Cardiovascular Angiography and Interventions, Society for Vascular Medicine and Biology, Society of Interventional Radiology, and the ACC/AHA Task Force on Practice Guidelines (writing committee to develop guidelines for the management of patients with peripheral arterial disease)-summary of recommendations. J Vasc Interv Radiol 2006;17: 1383-97.  Back to cited text no. 3
    
4.
Dhanoa D, Baerlocher MO, Benko AJ, et al. Position Statement on Noninvasive Imaging of Peripheral Arterial Disease by the Society of Interventional Radiology and the Canadian Interventional Radiology Association. J Vasc Interv Radiol 2016;27:947-51.  Back to cited text no. 4
    
5.
Orchard TJ, Strandness DE Jr. Assessment of peripheral vascular disease in diabetes. Report and recommendations of an international workshop sponsored by the American Heart Association and the American Diabetes Association September 18-20, 1992 New Orleans, Louisiana. Circulation 1993;88:819-28.  Back to cited text no. 5
    
6.
Chen J, Mohler ER 3rd, Garimella PS, et al. Ankle brachial index and subsequent cardiovascular disease risk in patients with chronic kidney disease. J Am Heart Assoc 2016;5: e003339.  Back to cited text no. 6
    
7.
Criqui MH, Langer RD, Fronek A, et al. Mortality over a period of 10 years in patients with peripheral arterial disease. N Engl J Med 1992;326:381-6.  Back to cited text no. 7
    
8.
Dachun Xu, Jue Li, Liling Zou, et al. Sensitivity and specificity of the ankle-brachial index to diagnose peripheral artery disease: A structured review. Vasc Med 2010;15:361-9.  Back to cited text no. 8
    
9.
Wattanakit K, Folsom AR, Selvin E, Coresh J, Hirsch AT, Weatherley BD. Kidney function and risk of peripheral arterial disease: Results from the Atherosclerosis Risk in Communities (ARIC) Study. J Am Soc Nephrol 2007;18: 629-36.  Back to cited text no. 9
    
10.
Ono K, Tsuchida A, Kawai H, et al. Ankle- brachial blood pressure index predicts all- cause and cardiovascular mortality in hemodialysis patients. J Am Soc Nephrol 2003;14: 1591-8.  Back to cited text no. 10
    
11.
Ankle Brachial Index Collaboration, Fowkes FG, Murray GD, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: A meta-analysis. JAMA 2008;300:197-208.  Back to cited text no. 11
    
12.
Adragao T, Pires A, Branco P, et al. Ankle- brachial index, vascular calcifications and mortality in dialysis patients. Nephrol Dial Transplant 2012;27:318-25.  Back to cited text no. 12
    
13.
O’Hare AM, Glidden DV, Fox CS, Hsu CY. High prevalence of peripheral arterial disease in persons with renal insufficiency: Results from the National Health and Nutrition Examination Survey 1999-2000. Circulation 2004;109:320-3.  Back to cited text no. 13
    
14.
Tranche-Iparraguirre S, Marín-Iranzo R, Fernández-de Sanmamed R, Riesgo-García A, Hevia-Rodríguez E, García-Casas JB. Peripheral arterial disease and kidney failure: A frequent association. Nefrologia 2012;32: 313-20.  Back to cited text no. 14
    
15.
Mozos I, Malainer C, Horbmczuk J, et al. Inflammatory markers for arterial stiffness in cardiovascular diseases. Front Immunol 2017; 8:1058.  Back to cited text no. 15
    
16.
Chen J, Mohler ER 3rd, Xie D, et al. Risk factors for peripheral arterial disease among patients with chronic kidney disease. Am J Cardiol 2012;110:136-41.  Back to cited text no. 16
    
17.
Liew YP, Bartholomew JR, Demirjian S, Michaels J, Schreiber MJ Jr. Combined effect of chronic kidney disease and peripheral arterial disease on all-cause mortality in a high-risk population. Clin J Am Soc Nephrol 2008;3:1084-9.  Back to cited text no. 17
    
18.
Kestenbaum BR, Adeney KL, de Boer IH, Ix JH, Shlipak MG, Siscovick DS. Incidence and progression of coronary calcification in chronic kidney disease: the Multi-Ethnic Study of Atherosclerosis. Kidney Int 2009;76:991-8.  Back to cited text no. 18
    
19.
He J, Reilly M, Yang W, et al. Risk factors for coronary artery calcium among patients with chronic kidney disease (from the Chronic Renal Insufficiency Cohort Study). Am J Cardiol 2012;110:1735-41.  Back to cited text no. 19
    
20.
Demer LL, Tintut Y. Vascular calcification: Pathobiology of a multifaceted disease. Circulation 2008;117:2938-48.  Back to cited text no. 20
    
21.
Dao HH, Essalihi R, Bouvet C, Moreau P. Evolution and modulation of age-related medial elastocalcinosis: impact on large artery stiffness and isolated systolic hypertension. Cardiovasc Res 2005;66:307-17.  Back to cited text no. 21
    
22.
Foster MC, Ghuman N, Hwang SJ, Murabito JM, Fox CS. Low ankle-brachial index and the development of rapid estimated GFR decline and CKD. Am J Kidney Dis 2013;61:204-10.  Back to cited text no. 22
    
23.
Violi F, Pastori D, Perticone F, et al. Relationship between low Ankle-Brachial Index and rapid renal function decline in patients with atrial fibrillation: A prospective multicentre cohort study. BMJ Open 2015;5:e008026.  Back to cited text no. 23
    
24.
Mostaza JM, Suarez C, Manzano L, et al. Relationship between ankle-brachial index and chronic kidney disease in hypertensive patients with no known cardiovascular disease. J Am Soc Nephrol 2006;17:S201-5.  Back to cited text no. 24
    
25.
Van Biesen W, De Bacquer D, Verbeke F, Delanghe J, Lameire N, Vanholder R. The glo- merular filtration rate in an apparently healthy population and its relation with cardiovascular mortality during 10 years. Eur Heart J 2007; 28:478-83.  Back to cited text no. 25
    
26.
Chronic Kidney Disease Prognosis Consortium, Matsushita K, van der Velde M, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: A collaborative meta-analysis. Lancet 2010;375:2073-81.  Back to cited text no. 26
    
27.
Henry RM, Kostense PJ, Bos G, et al. Mild renal insufficiency is associated with increased cardiovascular mortality: The Hoorn Study. Kidney Int 2002;62:1402-7.  Back to cited text no. 27
    
28.
Nishimura H, Miura T, Minamisawa M, et al. Ankle-brachial index for the prognosis of cardiovascular disease in patients with mild renal insufficiency. Intern Med 2017;56:2103- 11.  Back to cited text no. 28
    

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Correspondence Address:
Yasemin Ozgur
Department of Internal Medicine, University of Health Sciences, Kartal Dr. Lutfi Kirdar Training and Research Hospital, Istanbul
Turkey
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DOI: 10.4103/1319-2442.279965

PMID: 32129201

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