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NDT Advance Access originally published online on October 18, 2006
Nephrology Dialysis Transplantation 2007 22(2):508-514; doi:10.1093/ndt/gfl609
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© The Author [2006]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Development of a cardiovascular calcification index using simple imaging tools in haemodialysis patients

Paul Muntner1,2, Emiliana Ferramosca3, Antonio Bellasi4, Geoffrey A. Block5 and Paolo Raggi6

1Department of Epidemiology, 2Department of Medicine, Tulane University, New Orleans, LA, USA, 3Department of Nephrology, Ospedale Malpighi and University of Bologna, Bologna, 4Ospedale San Paolo and University of Milan, Milan, Italy, 5Denver Nephrology, PC, Denver, CO, and 6Division of Cardiology, Emory University, Atlanta, GA, USA

Correspondence and offprint requests to: Paul Muntner, PhD, 1140 Canal Street, Suite 2032, New Orleans, LA 70112, USA. Email: pmuntner{at}tulane.edu



   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Acknowledgement
 References
 
Background. Coronary artery calcification (CAC) is highly prevalent in haemodialysis patients and is associated with cardiovascular outcomes. Though cardiac computed tomography (CCT) is accurate, it is not widely available.

Methods. We developed a cardiovascular calcification index (CCI) to predict the presence of CAC for haemodialysis patients using simple in-office techniques. Prevalent haemodialysis patients (n = 140) underwent CCT imaging for CAC, a lateral abdominal X-ray for calcification of the abdominal aorta, an echocardiogram for valvular calcification, and pulse pressure measurement. A CCI was derived by weighting the prevalence rate ratios of CAC ≥1000. Using bootstrap techniques, validation was performed using receiver operator characteristic curves and likelihood ratios.

Results. Points were assigned for patients’ age (60–69 and ≥70 years, 1 and 2 points, respectively), dialysis vintage ≥2 years (1 point), aortic and mitral valve calcification (3 and 1 points, respectively), and abdominal aorta X-ray scores of 1–6 and ≥7 (2 and 4 points, respectively). Race, sex and pulse pressure did not contribute to the CCI. The CCI ranged from 0 to 11 points. The likelihood ratio of CAC ≥1000 associated with CCI scores of 2–4, 5, 6–8 and 9–11 were 1.28, 2.03, 2.94 and 3.83, respectively. Given the prevalence of CAC ≥1000 of 21% in the current study, the probability of having CAC ≥1000 was 26%, 38%, 43% and 50% for participants with CCI scores of 2–4, 5, 6–8, and ≥9, respectively.

Conclusions. Although refinement is needed, the CCI developed in the current study provides an alternative for predicting CAC when CCT is not available.

Keywords: abdominal aorta calcification; arterial stiffness; cardiovascular calcification; haemodialysis; imaging; valvular calcification



   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Acknowledgement
 References
 
Haemodialysis patients experience an extraordinarily high rate of cardiovascular mortality [1]. Furthermore, vascular and valvular calcification is highly prevalent in this population [2–6] and has been associated with cardiovascular disease [7,8].

The National Kidney Foundation established the Global Bone and Mineral Initiative to address the clinical importance of calcification in the context of kidney disease [9]. The ‘gold standard’ for assessment of coronary artery calcium (CAC) is electron beam or multi-slice cardiac computed tomography (CCT) [10,11]. However, there exists a paucity of CCT machines. As part of a 2004 report, the National Kidney Foundation's Vascular Calcification Working Group proposed that a series of simple in-office measurements might be substituted for CCT to identify and semi-quantitatively assess the extent of CAC in patients with chronic kidney disease (CKD). A plain lateral X-ray of the lumbar spine to assess the presence of calcification of the abdominal aorta, pulse pressure as an indirect assessment of arterial stiffness and an echocardiogram to visualize calcification of the cardiac valves were suggested as alternatives that may provide equally useful information. The working group proposed these techniques could be used together to develop a cardiovascular calcification index (CCI) in place of CCT [9].

The aims of the current study were 2-fold. First, we used the imaging techniques proposed by the National Kidney Foundation's Vascular Calcification Working Group to develop a CCI for haemodialysis patients. Second, we validated the CCI by comparing this index to patients’ CAC score assessed by electron beam tomography (EBT).



   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Acknowledgement
 References
 
Patient selection
One hundred and forty-eight prevalent adult haemodialysis patients were recruited from two dialysis centers (Denver, Colorado and New Orleans, Louisiana). Patients were eligible if they were receiving maintenance haemodialysis treatment. Exclusion criteria included current or a planned pregnancy within the next 6 months due to radiation exposure from the imaging procedures, having undergone coronary artery bypass surgery or coronary artery stent placement to avoid an inaccurate assessment of CAC because of metal artifacts on EBT, active atrial fibrillation due to potential artifacts resulting from an irregular heart rhythm and a weight over 300 pounds due to the limit of the EBT radiological cradle. Of the 148 enrolled participants, four were excluded from the current analysis for not having undergone an echocardiogram and an additional four for not having undergone the lateral lumbar X-ray. The final sample size for the current study was 140 participants.

Pulse Pressure
Blood pressure was measured three times using a manual aneroid sphygmomanometer with a 30 s rest between each measurement. Each participant was asked to sit resting for 15–20 min after which blood pressure was measured in the supine position using the arm that did not contain an arterio-venous fistula or shunt. For each blood pressure measurement, pulse pressure was calculated as the difference between systolic and diastolic pressures. The mean of the three pulse pressures was used in the current analysis.

Abdominal Aorta Plain Roentgenography
A technique similar to that described by Kauppila and colleagues [12] was employed to obtain images of the lower abdominal aorta. In brief, a lateral plain radiograph of the abdomen was obtained that included the last two thoracic vertebrae and the first two sacral vertebrae. A previously published semi-quantitative scoring system was utilized to ascertain the degree of calcification present in the abdominal aorta [12,13]. Only the segments of abdominal aorta in front of the first to the fourth lumbar vertebra were considered. Points were assigned from 0 to 3 according to the length of each calcified plaque (0: none; 1: small; 2 moderate; 3: large) identified along the anterior and posterior profiles of the aorta in front of each of the lumbar vertebrae. Using this numerical grading, each patient was assigned a score between 0 and 24 with higher scores indicating a greater degree of calcification of the abdominal aorta. The abdominal aorta calcification score was divided into approximate tertiles (scores of 0, 1–6 and ≥7). Approximate tertiles were utilized because 44% of the study population had scores of 0. All abdominal X-ray scores were assessed by two experienced investigators (AB and PR) who reached a consensus reading in each patient.

Echocardiography
A two-dimensional echocardiograph study was performed on each patient utilizing a Sequoia 512 (Siemens, Erlangen, Germany) or a Vivid 7 (General Electric, Milwaukee, WI) system. Digital images were acquired in the long-axis and short-axis parasternal views and the apical four and two chamber views and 3-cycle clips were stored on magnetic optical disks for future review. The presence of aortic and mitral valve calcification, separately, was determined visually by two investigators (AB and PR) and assessed as being present or absent. The investigators reached a consensus interpretation for each participant.

Electron beam tomography imaging
All EBT scans were performed using a C-150 scanner (GE-Imatron, San Francisco, CA). A standard imaging protocol was used and 45–60 contiguous tomographic slices were obtained at end-expiration starting 0.5 cm above the aortic arch and extending to the diaphragm. The slice thickness was kept at 3 mm and the acquisition time at 100 ms per slice. A calcium score for each area of interest identified along the course of the coronary arteries was calculated as originally described by Agatson and colleagues [14]. The reported inter-scan variability for the Agatson score is about 8–10%. All scans were reviewed by a single experienced investigator (PR).

All tests (presence of valvular calcification, abdominal aorta scores and CAC) were evaluated and scored in a blinded fashion by observers who were unaware of the patients’ demographic information and other test results.

Statistical analysis
Demographic and medical characteristics of the study population were calculated overall, and by level of CAC, as means and standard errors for continuous variables and proportions for categorical variables. CAC thresholds (≥100, ≥400 and ≥1000) were identified on the basis of levels reported to be clinically useful and associated with increased morbidity and mortality in the general population [15–17]. Next, the age-adjusted prevalence rate ratio of CAC ≥1000 associated with predictor variables including age grouping (<50, 50–59, 60–69 and ≥70 years), sex, race, tertile of dialysis vintage (<2 years, 2–4 years and ≥5 years), aortic and mitral valve calcification, tertile of pulse pressure, and abdominal aorta calcification scores (0, 1–6, and ≥7) were calculated using a binomial regression model [18]. The use of alternate cut-points (e.g. dialysis vintage <2 years, 2–4 years, 5–9 years and ≥10 years) gave markedly similar results. Points were assigned to each predictor variable by rounding the corresponding prevalence rate ratio to the nearest integer and subtracting one for null associations (e.g. a prevalence rate ratio of 3.1 would be rounded to 3 and assigned 3 – 1 = 2 points). Points were summed across predictor variables to determine each participant's total point score which was used as the CCI. Using bootstrap techniques, performance of the CCI was characterized [19]. First, the ability of the CCI to predict the presence of CAC was assessed by plotting the receiver operating characteristic curves and calculating the area under the curve (AUC). AUC for the CCI comprised of demographic information, dialysis vintage, abdominal aorta calcification, and mitral and aortic valve calcification was compared to receiver operating characteristic curves for simpler models comprised of demographic information and dialysis vintage, alone and demographic information, dialysis vintage and each individual imaging procedure. Next, the likelihood ratio of CAC ≥100, ≥400, ≥1000, separately, associated with each quintile of the CCI was calculated. Finally, the incremental value of using the CCI was determined by calculating the post-test probability of having CAC ≥100, ≥400, ≥1000, separately, as the product of the pre-test probability of having CAC ≥100, ≥400, ≥1000 and the likelihood ratio associated with the CCI. In determining the post-test probability, the prevalence of CAC ≥100, ≥400, ≥1000 in our study population was used as the pre-test probability. All analyses were conducted using Stata 8.0 Statistical Software (College Station, TX).



   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Acknowledgement
 References
 
Study population characteristics
The mean age of study patients was 55.3 years (Table 1). There were a similar number of men and women, and Caucasians and African Americans were well represented. The mean dialysis vintage was 4.01 years and hypertension and diabetes mellitus were highly prevalent (95% and 50%, respectively). The mean pulse pressure was 68.0 mmHg, 44.3% and 37.9% of the population had aortic and mitral valve calcification, respectively, and the median abdominal aorta X-ray score was 1 (inter-quartile range: 0–7).


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  		Table 1. Clinical characteristics of the study population (N = 140)

 
The prevalence rate ratio of CAC ≥1000 associated with age 60–69 and ≥70 years was 2.08 (95% CI: 0.81, 5.32) and 2.72 (1.08, 6.83), respectively (Table 2). The age-adjusted prevalence rate ratio for CAC ≥1000 increased with longer dialysis vintage and was 1.63 (0.66, 4.02) and 2.36 (1.00, 5.60) for persons on dialysis for 2–4 and ≥5 years, respectively, compared to their counterparts on dialysis for less than 2 years. Additionally, the age-adjusted prevalence rate ratio was 3.63 (1.66, 7.96) and 2.20 (1.15, 4.21) for aortic and mitral valve calcification, respectively, and 3.16 (1.01, 9.84) and 5.42 (1.81, 16.2) for patients with an abdominal aorta X-ray score of 1–6 and ≥7, respectively, compared to their counterparts with an abdominal aorta X-ray score of 0. Race, sex and pulse pressure were not associated with a substantially increased prevalence rate ratio of CAC ≥1000. Using age grouping, dialysis vintage, aortic and mitral valve calcification and calcification of the abdominal aorta, CCI scores ranged from 0 to 11.


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  		Table 2. Age-specific and age-adjusted prevalence rate ratio of coronary artery calcium score ≥1000 and points assigned to the cardiovascular calcification index.

 
Using demographic information and dialysis vintage alone, the AUC for diagnosing CAC ≥1000 was 0.67 (Figure 1). This was improved by inclusion of abdominal aorta calcification score (AUC = 0.75; P = 0.079) or inclusion of aortic and mitral valve calcification (AUC = 0.76; P = 0.033). Including demographics, dialysis vintage, abdominal aorta calcification and aortic and mitral valve calcification increased the AUC to 0.80. While this model (i.e. the CCI) provided a significantly better discriminatory value than a model comprised of demographic information and dialysis vintage, the marginal improvement derived from including abdominal aorta calcification and aortic and mitral valve calcification simultaneously was not statistically significant (P = 0.08 and 0.21, respectively) compared to including either measure alone.


Figure 1
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  		Fig. 1. ROC curves for four models of coronary artery calcium score ≥1000. (Figure 1) Calcification index uses demographics and dialysis vintage (AUC = 0.67); (Figure 1) calcification index uses demographics, dialysis vintage and abdominal aorta clacification (AUC = 0.75); (Figure 1) calcification index uses demographics, dialysis vintage and mitral and aortic valve calcification (AUC = 0.76); (Figure 1) calcification index uses demographics, dialysis vintage, abdominal aorta calcification and aortic and mitral valve calcification (ROC = 0.80) P-value = 0.079, 0.033, and 0.009 comparing the area under the curve for the index comprised of demographics, dialysis vintage and abdominal aorta calcification (x), demographics, dialysis vintage and aortic and mitral valve calcification (triangle), and demographics, dialysis vintage, abdominal aorta calcification, mitral and aortic valve calcification (circle), respectively, to an index comprised of demographic information and dialysis vintage alone (square) P-value = 0.08 and 0.21 comparing index comprised of demographics, dialysis vintage and abdominal aorta calcification (triangle) and demographics, dialysis vintage and mitral and aortic valve calcification (x), respectively, with the index comprised of demographics, dialysis vintage, aortic and mitral valve calcification and abdominal aorta calcification (circle).

 
The likelihood ratios of CAC ≥100, ≥400 and ≥1000 increased at higher CCI quintile (Table 3). Applying these likelihood ratios to the pre-test probability of CAC ≥100, ≥400 and ≥1000 resulted in a substantial increase in the probability of having these outcomes (Figure 2).


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  		Table 3. Likelihood ratio of coronary artery calcium score ≥100, ≥400 and ≥1000 associated with quintile of cardiovascular calcification index in end-stage renal disease patients on haemodialysis.

 

Figure 2
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  		Fig. 2. Pre- and post-test probability of having coronary artery calcium score ≥100 (top left panel), ≥400 (top right panel) and ≥1000 (bottom left panel).

 


   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Acknowledgement
 References
 
CAC is an important prognostic variable in patients with CKD. Although detecting CAC is clinically important, the limited availability of CCT scanners and their high cost restrict their use as a screening modality. In the current study, we developed a CCI to estimate the presence of CAC in patients on maintenance haemodialysis. Using demographic information, dialysis vintage and three simple measures, the presence of CAC was predicted with very good accuracy. Specifically, the AUC for our model was 0.80. Additionally, the data we present show that the CCI provides an approach for the practicing nephrologist to identify haemodialysis patients who are more likely to have high levels of CAC. For example, the post-test probability of having CAC ≥100, ≥400 and ≥1000 for haemodialysis patients with CCI scores of 9 through 11 was 87%, 74% and 50%, respectively, representing substantial increases from the pre-test probability of 56%, 41% and 20%, respectively.

The current study highlights a simple approach to enhance the implementation of the Kidney Disease Outcomes Quality Initiative (K/DOQI) guidelines on the management of dialysis patients with cardiovascular calcification. The large burden of CAC in haemodialysis patients, compared to age–sex matched controls without renal disease, is well known [2,3]. Furthermore, the progression of CAC in the context of renal disease is known to be rapid [8,20]. The Global Bone and Mineral Initiative Working Group of the National Kidney Foundation recommended screening for the presence of cardiovascular calcification with simple office-based methods to make it accessible to a greater number of nephrologists.

The CCI developed in the current study was comprised of demographic information, dialysis vintage and simple imaging procedures that are widely available, less costly and mostly non-radiation-based or providing a small dose of radiation. The effectiveness of screening programs relies on the availability of technology. While CCT scanners are not widely available, ultrasound and X-ray machines are commonplace and well accepted by patients.

In patients undergoing haemodialysis, hyperphosphataemia has been shown to be an independent predictor of cardiovascular disease [21,22]. Alterations in mineral metabolism are linked to the development of vascular calcification as well as poor outcomes in dialysis patients. Strategies are therefore needed to prevent vascular calcification and limit the progression of CAC [23,24]. In the past, the limited availability of reliable and accurate imaging techniques has restricted the conduct of studies assessing treatments for CAC. The CCI developed in the current study provides a simple tool to use in clinical practice as a guide for the detection and prevention of CAC progression.

Data from the general population provide overwhelming evidence that CAC is associated with future cardiovascular events and mortality [25–29]. For example, after adjustment for age, cigarette smoking, high cholesterol, diabetes and hypertension, the relative risk of death or myocardial infarction associated with the presence of CAC of a cardiac event was 3.86 (95% CI: 1.17, 12.7) [29]. However, studies including patients with kidney disease are limited. In one study, Matsuoka and colleagues [30] reported a lower 5-year cumulative survival (67.9%) for haemodialysis patients with CAC scores above the median for that cohort (≥200) compared to their counterparts with CAC scores below the median (84.2%). Also, higher CAC scores remained associated with increased mortality after adjustment for potential confounders. Despite these early results, future studies are needed to better understand the prognostic importance of CAC scores, and potential interventions, in the context of patients with kidney failure. Also, given the high prevalence of left ventricular hypertrophy in the context of renal disease, aortic calcium may be a more relevant predictor of mortality than CAC. A direct comparison of aortic calcium and CAC on cardiovascular mortality will be important in understanding the pathology of cardiovascular disease in the context of renal disease.

Although strong associations were present between calcification of both valves and CAC ≥1000, this association was stronger for the aortic valve. Interestingly, this observation matches the purported similarity of aortic valve disease and atherosclerosis described in the general population, and supports the notion that calcification of vessels and valves follows similar mechanisms even in the context of end-stage renal disease [31,32]. On the contrary, no pattern was present between pulse pressure and CAC. While pulse pressure maintains a strong relationship with cardiovascular disease, its association with arterial stiffness and CAC is not well established [33]. Additional work is needed to better understand the relationship between components of the prediction equation developed in the current study, CAC and cardiovascular outcomes.

The AUC associated with the CCI comprised of age, dialysis vintage and abdominal aorta calcification was not significantly improved after further inclusion of aortic and mitral valve calcification. While we chose to include information on aortic and mitral valve calcification, consideration of a simpler derived CCI warrants consideration. Specifically, using demographic characteristics, dialysis vintage and calcification of the abdominal aorta (i.e. omitting the echocardiogram) would result in substantially reduced test costs and feasibility. Alternatively, to limit radiation exposure, one could perform a limited bi-dimensional echocardiogram to visualize valvular calcification and not perform an abdominal X-ray. Given our sample size of 140 patients, comparisons of CCIs developed using different criteria were beyond the scope of the current analysis.

The primary rationale for measuring CAC is to provide a risk assessment for individual patients. Current K/DOQI guidelines recommend against use of calcium based phosphate binders in the presence of extensive cardiovascular calcification. A coronary calcium score ≥1000 has been associated with a very high risk of events in the general population. Hence, higher CCI scores warrant clinical attention.

The current study needs to be considered in the context of its limitations. First, given the cross sectional nature of the study, the use of CCI to predict mortality was not feasible. Future endeavors should be undertaken to evaluate the use of CCI to predict the progression of CAC, and cardiovascular morbidity and mortality. Second, the study was limited to patients on haemodialysis. As such, the CCI may not be generalizable to patients with less severe renal disease (e.g. stage 3 and stage 4 CKD). Finally, given the expense involved in EBT, we relied on a relatively small study population. This forced us to examine a limited number of predictor variables and resulted in wide confidence intervals for the prevalence rate ratios. For this reason we did not rely on statistical significance when assigning points to components of the CCI. Also, the small data set required the use of bootstrapping techniques rather than a split data set design to develop and validate the CCI. However, the use of bootstrapping techniques has been previously shown to be reliable and should provide valid results [34].

The current study indicates that simple markers of CAC proposed by the National Kidney Foundation's Global Bone and Mineral Initiative Working Group can provide a sensitive, quantitative assessment of coronary calcification [9]. Screening haemodialysis patients for the presence of CAC using a CCI will allow nephrologists to identify, with acceptable sensitivity and specificity, patients at higher risk for cardiovascular events. Using this information and the National Kidney Foundation's guidelines, the management of bone and mineral metabolism and vascular disease can be carefully tailored. Further studies to refine and validate CCIs will assist nephrologists in identifying patients who may benefit from focused intervention.



   Acknowledgement
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgement
References
 
This study was supported in part by a grant from Genzyme Therapeutics, Cambridge, MA.

Conflict of interest statement. G.A.B. and P.R. have received research grants from Genzyme, and G.A.B. serves as an advisor Board member for AMGEN. None of the other authors declare any conflicts of interest.



   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgement
 References
 

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Received for publication: 8. 7.06
Accepted in revised form: 20. 9.06


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