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NDT Advance Access originally published online on October 10, 2007
Nephrology Dialysis Transplantation 2008 23(2):580-585; doi:10.1093/ndt/gfm622
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© The Author [2007]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org


Prevalence and prediction of renal artery stenosis in patients with coronary and supraaortic artery atherosclerotic disease

Tadeusz Przewlocki1, Anna Kablak-Ziembicka1, Wieslawa Tracz1, Grzegorz Kopec1, Pawel Rubis1, Mieczyslaw Pasowicz1, Piotr Musialek1, Magdalena Kostkiewicz1, Artur Kozanecki1, Tomasz Stompór2, Wladyslaw Sulowicz2 and Andrzej Sokolowski3

1Department of Cardiac and Vascular Diseases, Institute of Cardiology, Jagiellonian University School of Medicine, The John Paul II Hospital, 2Department of Nephrology, Jagiellonian University and 3Department of Statistics, Krakow University of Economics, Krakow, Poland

Correspondence to: Tadeusz Przewlocki, MD, PhD, Department of Cardiac and Vascular Diseases, Institute of Cardiology, Jagiellonian University School of Medicine, The John Paul II Hospital, Pradnicka 80, 31-202 Krakow, Poland. Email: tadeuszprzewlocki{at}op.pl



   Abstract
Top
 Abstract
Introduction
 Subjects and methods
 Statistical analysis
 Results
 Discussion
 References
 
Background. Renal atherosclerosis is associated with increased cardiovascular mortality. This study aimed to determine the prevalence and predictors of renal artery stenosis (RAS) in patients with coronary artery disease (CAD) and supraaortic arteries (SA) stenosis.

Methods. Renal angiography was performed in 1193 (807 men) consecutive patients referred for coronary or SA angiography. Group I included 296 (136 men, 60.1 ± 9.5 years) patients with no significant (<50%) lesion in coronary arteries or SA; group II included 706 (526 men, 62.2 ± 9.7 years) patients with stenosis ≥50% within single arterial territory (coronary arteries or SA) and group III included 191 (145 men, 64.9 ± 8.5 years) patients with stenosis ≥50% in both territories.

Results. Some RAS was found in 55 (18.6%) patients in group I, 250 (35.4%) patients in group II and 115 (60.2%) patients in group III (P < 0.001). The proportion of patients with RAS ≥50% in groups I, II and III was 3.3, 6.2 and 18.3%, respectively (P < 0.001). RAS prevalence increased with the number of stenosed coronary arteries (38.4% in 1-vessel, 42.1% in 2-vessel, 48.5% in 3-vessel CAD, P < 0.001). Independent predictors of RAS ≥50% identified by logistic regression analysis were SA stenosis [relative risk (RR) = 3.28, P < 0.001], 2-3-vessel-CAD (RR = 2.04, P = 0.002), creatinine level ≥1.07 mg/dl (RR = 2.95, P < 0.001), hypertension (RR = 2.97, P = 0.012) and body mass index <25 kg/m2 (RR = 1.42, P = 0.169). A calculated score for RAS ≥50% prediction (based on the regression model) was reliable (coefficient of determination, R = 0.978) and showed a sensitivity of 77.5% and a specificity of 63.9%.

Conclusions. RAS prevalence and severity increases with the number of arterial territories involved and CAD severity. The following independent predictors of RAS ≥50% were identified: SA involvement, 2-3-vessel-CAD, serum creatinine level and hypertension.

Keywords: coronary and carotid angiography; coronary artery disease; renal artery stenosis; renal stenosis predictors; supraaortic artery stenosis



   Introduction
Top
 Abstract
 Introduction
Subjects and methods
 Statistical analysis
 Results
 Discussion
 References
 
Renal artery stenosis (RAS) often leads to progressive renal artery occlusion and constitutes a potentially correctable cause of hypertension and ischemic nephropathy [1–3]. Moreover, patients with RAS ≥50% suffer from myocardial infarction or require cardiac revascularization significantly more frequently than do non-RAS patients [4]. Concomitant significant RAS aggravates the course of coronary artery disease (CAD), and despite coronary revascularization, survival rate in patients with CAD and RAS ≥50% is significantly lower [4–6]. RAS often remains unrecognized as there are no specific RAS symptoms; also, guidelines addressing the timing or indications to renal artery evaluation have not been established [1,3,7–14].

Recently, the screening for RAS has gained more attention as more patients with refractory hypertension and impaired renal function are encountered in the catheterization laboratories [15–17]. Identification of patients with a high probability of RAS seems important as early renal intervention can be associated with better hypertension control, improvement or preservation of renal function and, possibly, an improved survival in patients with CAD or supraaortic artery (SA) disease referred to revascularization [17–20].

The present study aimed to evaluate the prevalence and identify the independent predictors of RAS in patients with suspected CAD and/or SA stenosis.



   Subjects and methods
Top
 Abstract
 Introduction
 Subjects and methods
Statistical analysis
 Results
 Discussion
 References
 
Between May 2005 and June 2006, 1193 consecutive patients (807 men, 386 women) aged 62.3 ± 9.5 (range 25–85) years, admitted to our department for coronary or SA angiography and possible intervention, were enrolled into the study. There were 1036 patients with suspected CAD and 157 patients referred for SA stenosis (carotid, subclavian or vertebral).

All the 157 patients with SA stenosis also underwent coronary angiography due to high prevalence of CAD in patients with SA atherosclerosis [21]. In the 1036 patients with suspected CAD, SA angiography was performed if Doppler ultrasound examination of SA (performed in all these patients) revealed stenosis ≥50%.

Angiographies were performed in all the patients during one session using a Coroscop system (Siemens AG, Munich, Germany) equipped with Quantcor version 2.0 quantitative coronary analysis software. All angiographic examinations were performed by the Seldinger technique through femoral or radial artery access. Coronary and SA angiographies were performed in several views that best displayed the lesion and enabled stenosis grade evaluation. Coronary and SA stenoses were defined significant with a threshold a lumen reduction of at least ≥50%.

Selective angiography of renal arteries was performed with a right 6 French Judkins catheter following coronary angiography. In some cases (with prior ventriculography), renal angiography was performed with the pigtail catheter. Both renal arteries were visualized in an anterior–posterior projection and if necessary also in additional oblique projections. The percentage of diameter stenosis was determined with software for quantitative angiography (QA). A stenosis <30% was defined as minimal, 30–49% as mild, 50–69% as moderate and ≥70% as severe.

In all patients, fasting blood samples were collected and serum total cholesterol, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), triglycerides, high-sensitivity C-reactive protein (hs-CRP) and creatinine were measured (Hitachi 917 analyzer, Roche Diagnostics, Division of Hoffmann-LaRoche, Basel, Switzerland).

The study protocol was reviewed and approved by the local ethical committee and all the patients signed informed consent.



   Statistical analysis
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 Abstract
 Introduction
 Subjects and methods
 Statistical analysis
Results
 Discussion
 References
 
Continuous variables were presented as mean ± 1 SD and categorical variables were expressed as frequencies and percentages. Means of analysed parameters across groups were tested with ANOVA and frequencies were compared by the Chi-square test for independence.

The prevalence and grade of RAS for each group were determined.

A backward stepwise binary logistic regression analysis (non-linear quasi-Newton estimation method) was performed to identify the independent predictors of any RAS for all the studied patients. The relative risk of having any RAS and confidence interval (CI) were estimated with a multiple logistic regression.

The prediction model for RAS ≥50% probability was built in three ‘steps’. First, we identified independent risk factors for RAS ≥50% by backward stepwise binary logistic regression analysis (non-linear quasi-Newton estimation method) for all studied patients. Fifteen clinical variables were included in the initial model: age, gender, number of patients involved, coronary arteries (CAD severity), presence of SA stenosis ≥50%, hypertension, body mass index (BMI), hyperlipidemia, diabetes, smoking habit, history of myocardial infarction, stroke/transient ischemic attack, total- and LDL cholesterol, hs-CRP and serum creatinine level (for continuous variables, cut-off values were obtained from ROC curves). Second, for each independent variable, estimation of relative risks (RR) of having RAS ≥50% was performed with a multiple logistic regression and CI was estimated on the basis of the whole sample in order to have the RR estimates with minimal variance. Third, for the presence of any independent RAS ≥50% predictor in a given patient, points reflecting the RR obtained from logistic regression analysis were assigned. The simple scores (integer values) assigned to each risk factor were proposed such that they are as close as possible to the obtained RR values (e.g. RR for SA = 3.28; SA score = 3). The risk score was created by adding up these points. At this point, we have tried different combinations of score values adding up to ranges from 9 through 15. The score system with maximum 12 points gave the best prediction results. The theoretical probability of having RAS ≥50% according to number of points achieved in the risk score was estimated by the logistic curve in the form of:


Formula

The risk score reliability was tested by random selection of a subgroup of approximately 951 (79.9%) patients following this three-step model building. With the same scoring system, we tested the remaining group of patients (n = 242).

A non-linear Levenberg–Marquardt estimation method was used to assess the goodness-of-fit of the model.

The specificity, sensitivity and positive and negative predictive values of the score was calculated for the optimal cut-off value.

Statistical analyses were performed with statistical software (Statistica 7.1, Stat Soft Inc, Tulsa, Oklahoma, USA) and StatsDirect (version 2.4.6). Differences were considered significant if P value was <0.05.



   Results
Top
 Abstract
 Introduction
 Subjects and methods
 Statistical analysis
 Results
Discussion
 References
 
Group I included 296 patients (136 men; 45.9%) aged 60.1 ± 9.5 (range 31–85) years without significant (≥50%) lesions in coronary arteries and SA.

Group II included 706 patients (526 men; 74.5%) aged 62.2 ± 9.7 (range 25–84) years with significant (≥50% lumen reduction) single arterial territory involvement, i.e. at least one stenosis within main coronary artery branch (660 subjects) or isolated SA stenosis (46 patients).

Group III included 191 patients (145 men; 75.9%) aged 64.9 ± 8.5 (range 44–82) years with atherosclerotic stenosis ≥50% in both coronary and SA territories.

With the increasing number of involved territories, we observed an increasing frequency of risk factors, age, levels of serum creatinine, LDL cholesterol and hs-CRP. Clinical and laboratory data are shown in Table 1.


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  		Table 1. Characteristics of the study groups

 
Among all the 1193 patients, some RAS was found in 420 (35.2%) and RAS ≥50% in 89 (7.5%) patients. The prevalence of renal artery atherosclerosis increased with the number of involved territories: 18.6% patients in group I, 35.4% in group II and 60.2% in group III, P < 0.001 (Table 2).


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  		Table 2. The grade of RAS by CAD and SA involvement (P < 0.001)

 
Notably, RAS ≥50% was found in 10 (3.3%) patients with no significant lesions in both coronary arteries and SA, in 44 (6.2%) patients with a significant lesion in one arterial territory (coronary or SA) and in 35 (18.3%) patients with both CAD and SA stenosis (P = 0.001 for trend) (Table 2).

The prevalence of RAS significantly increased with the extent of CAD (a number of main coronary arteries with ≥50% stenosis), occurring in 38.4% of patients with 1-vessel, 42.1% of patients with 2-vessel and 48.5% with 3-vessel CAD (P < 0.001). RAS ≥50% was present in 4.6% of patients without significant stenosis in coronary arteries, in 6.0% of patients with 1-vessel CAD and in 11.0% and 13.2% of patients with 2-3-vessel CAD, respectively (P = 0.0001). Interestingly, in a relatively small subgroup of 46 patients with isolated SA stenosis (subgroup II), some RAS and RAS ≥50% were found in 19 (42.2%) and 8 (17.4%) patients, respectively.

Backward stepwise binary logistic regression analysis showed the following independent predictors of RAS: SA stenosis (P < 0.001), 2-3-vessel CAD (P < 0.001), hypertension (P = 0.013), age (P < 0.001), LDL cholesterol ≥135 mg/dl (P < 0.001) and female gender (P = 0.009). In contrast, BMI ≥30 kg/m2 (P = 0.022) was observed to be a negative predictor (Table 3).


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  		Table 3. Predictors of any RAS and RAS ≥50% (backward logistic regression analysis)

 
The independent predictors of RAS ≥50% were as follows: SA stenosis (P < 0.001), 2-3-vessel CAD (P = 0.002), serum creatinine level ≥1.07 mg/dl (P < 0.001) and hypertension (P = 0.012) (Table 3). A trend to statistical significance between RAS frequency and BMI <25 kg/m2 was found (P = 0.169, RR = 1.42, CI 0.86–2.33). The relative risk of having RAS ≥50% was 3.28-fold (CI 2.06–5.21) in patients with SA stenosis, 2.95-fold (CI 1.86–4.69) for serum creatinine level >1.07 mg/dl 2.97-fold (CI 1.27–6.95) in those with hypertension and 2.04-fold (CI 1.29–3.23) in those with 2-3-vessel CAD.

For presence of RAS ≥50% predictor in a given patient, points 1–3 (depending on the relative risk obtained from above logistic regression analysis) were assigned: SA stenosis (3 points), serum creatinine ≥1.07 mg/dl (3 points), hypertension (3 points), 2-3-vessel CAD (2 points) and BMI <25 kg/m2 (1 point), which then were totalled to determine a risk score (maximum 12 points). The predicted probabilities of stenosis obtained from the model agreed well with the observed frequency of stenosis (goodness-of-fit, coefficient of determination R2 = 0.956) (Figure 1). In patients with a risk score of 12 points, the theoretical probability of having RAS ≥50% was 48.3% and observed 50% (Figure 1). In patients with a score ≥6 points, the sensitivity and specificity of finding RAS ≥50% on angiography was 77.5 and 63.9%, respectively, with a positive predictive value of 14.8% and negative predictive value of 97.2% (Table 4).


Figure 1
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  		Fig. 1. Probability of having RAS ≥50% based on the risk score (a non-linear Levenberg–Marquardt estimation method). Coefficient of determination, R = 0.978, R2 = 0.956. Blue dots indicate the real frequency of occurrence of RAS ≥50% in the study group, the red line indicates a theoretical probability of having RAS ≥50%.

 

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  		Table 4. Probability of RAS ≥50% in the relation to the risk score

 
To verify the accuracy of the prediction model, we tested it additionally on randomly taken subgroups and subsequently applied this formula to the remaining patients. The estimates were of course different in each case, but all of them led to the same simplified scoring system (12-point scale). For example, we have randomly selected a subgroup of approximately 80% of the patients (n = 951). We got the following RRs: SA involvement, 3.06; serum creatinine level, 3.13; hypertension, 2.59; 2-3-vessel CAD, 2.01 and BMI, 1.66. These ‘new’ estimates were quite comparable. With the same scoring system, we tested the remaining group of patients (n = 242). A total of 14 out of 242 patients had RAS ≥50 and 11 were identified correctly (a sensitivity of 78.6%). NPV was extremely good (97.9%). Comparing with the whole sample, the specificity was 61.8% and PPV was 11.2%. Similar data has been obtained several times, each time randomly selecting a part of patients and testing this rule for the remaining part of patients.



   Discussion
Top
 Abstract
 Introduction
 Subjects and methods
 Statistical analysis
 Results
 Discussion
References
 
The prevalence of RAS in patients with CAD is common, ranging from 11.4 to 34% for some RAS and from 6.3 to 23% for RAS ≥50% in patients undergoing cardiac catheterization [7–14,22]. In our study the coexistence of CAD and RAS was within the above ranges. Although the association of CAD severity and RAS prevalence is important, we showed that other clinical variables have a stronger association with RAS. In particular, in our study, the SA involvement was identified as a stronger RAS predictor than was CAD presence and severity. The prevalence of RAS ≥50% was doubled in patients with SA involvement as compared to all CAD patients, while the relative risk of RAS ≥50% was 3.28-fold higher in patients with SA stenosis as compared to 2.04-fold in patients with 2- and 3-vessel CAD. The highest prevalence of RAS ≥50% was observed in patients with involvement of both coronary arteries and SA (18.3%). Also, Park et al. [12] showed that significant atherosclerotic lesions situated in aortic arch arteries are related to the increased frequency of RAS in patients with CAD.

In addition to SA involvement and CAD, numerous clinical variables have been reported as RAS predictors [7,9,11–13,15,16,22]. Some of these predictors were confirmed in our study. For instance, serum creatinine level ≥1.07 mg/dl showed a predictive strength for RAS ≥50% similar to the SA involvement. Hypertension was associated with a 2.97-fold relative risk increase for RAS ≥50%. Age appeared to be an independent predictor of some RAS but not of RAS ≥50%.

Despite the high burden of RAS, as yet, there is no single ideal screening test or recommended screening algorithm generally accepted for RAS detection. The data obtained in the present study encouraged us to make an attempt to create a very simple score for detection of RAS ≥50%. First, patients who have all risk factors entering into this model such as hypertension, serum creatinine ≥1.07 mg/dl, supraaortic arteries stenosis, 2-3-vessel CAD and BMI <25 kg/m2 have a probability of having RAS ≥50% of nearly 50%. In patients with a score ≥6 points, the sensitivity and specificity of finding RAS ≥50% on angiography was 78 and 64%, respectively. Although moderate, this sensitivity and specificity is similar to those of renal scintigraphy [23]. High negative predictive value of the score indicates that patients obtaining less than six points are very unlikely to have RAS ≥50% (Table 4). This is important since it concerns 60% of all patients enrolled into our study. Moreover, the goodness-of-fit for the created model is satisfying (Figure 1). DRASTIC criteria reported by Krijnen et al. assumed very complex assessment of numerous parameters (including age, gender, atherosclerotic vascular disease, onset of hypertension, smoking history, BMI, presence of an abdominal bruit, serum creatinine and total cholesterol level) showed a sensitivity of 68% and a specificity of 87% [15]. In the more recent study performed by Cohen et al. [16], a risk score was based on the very complex analysis that yielded a sensitivity of 76% and a specificity of 71%, which is comparable to our data.

In conclusion, our data suggest that RAS may be predicted through the assessment of several clinical variables. Our model included only five easily assessable clinical variables. We believe that it may be useful in indicating patients at risk for RAS among patients referred for coronary or SA angiography, in which simultaneous renal angiography might be justified. Independent of the score index, it seems reasonable that patients with CAD, SA stenosis and elevated serum creatinine level should undergo renal artery evaluation with at least Doppler ultrasound.

Conflict of interest statement. None declared.



   References
Top
 Abstract
 Introduction
 Subjects and methods
 Statistical analysis
 Results
 Discussion
 References
 

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Received for publication: 13. 6.07
Accepted in revised form: 16. 8.07


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