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Multicenter Study
. 2007 Feb;22(2):508-14.
doi: 10.1093/ndt/gfl609. Epub 2006 Oct 18.

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

Affiliations
Multicenter Study

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

Paul Muntner et al. Nephrol Dial Transplant. 2007 Feb.

Abstract

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 > or =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 > or =70 years, 1 and 2 points, respectively), dialysis vintage > or =2 years (1 point), aortic and mitral valve calcification (3 and 1 points, respectively), and abdominal aorta X-ray scores of 1-6 and > or =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 > or =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 > or =1000 of 21% in the current study, the probability of having CAC > or =1000 was 26%, 38%, 43% and 50% for participants with CCI scores of 2-4, 5, 6-8, and > or =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.

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