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Open Access Highly Accessed Research article

What does my patient's coronary artery calcium score mean? Combining information from the coronary artery calcium score with information from conventional risk factors to estimate coronary heart disease risk

Mark J Pletcher12*, Jeffrey A Tice12, Michael Pignone3, Charles McCulloch1, Tracy Q Callister4 and Warren S Browner156

Author Affiliations

1 Department of Epidemiology and Biostatistics, University of California, San Francisco 500 Parnassus Ave, MU 420 West, Box 0560, San Francisco, CA 94143-0560, USA

2 Division of General Internal Medicine, University of California, San Francisco, CA, USA

3 Division of General Internal Medicine and Clinical Epidemiology, University of North Carolina – Chapel Hill School of Medicine, Chapel Hill, NC, USA

4 EBT Research Foundation, Nashville, TN, USA

5 Research Institute, California Pacific Medical Center, San Francisco, CA, USA

6 Department of Medicine, University of California, San Francisco, CA, USA

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BMC Medicine 2004, 2:31  doi:10.1186/1741-7015-2-31

Published: 24 August 2004

Abstract

Background

The coronary artery calcium (CAC) score is an independent predictor of coronary heart disease. We sought to combine information from the CAC score with information from conventional cardiac risk factors to produce post-test risk estimates, and to determine whether the score may add clinically useful information.

Methods

We measured the independent cross-sectional associations between conventional cardiac risk factors and the CAC score among asymptomatic persons referred for non-contrast electron beam computed tomography. Using the resulting multivariable models and published CAC score-specific relative risk estimates, we estimated post-test coronary heart disease risk in a number of different scenarios.

Results

Among 9341 asymptomatic study participants (age 35–88 years, 40% female), we found that conventional coronary heart disease risk factors including age, male sex, self-reported hypertension, diabetes and high cholesterol were independent predictors of the CAC score, and we used the resulting multivariable models for predicting post-test risk in a variety of scenarios. Our models predicted, for example, that a 60-year-old non-smoking non-diabetic women with hypertension and high cholesterol would have a 47% chance of having a CAC score of zero, reducing her 10-year risk estimate from 15% (per Framingham) to 6–9%; if her score were over 100, however (a 17% chance), her risk estimate would be markedly higher (25–51% in 10 years). In low risk scenarios, the CAC score is very likely to be zero or low, and unlikely to change management.

Conclusion

Combining information from the CAC score with information from conventional risk factors can change assessment of coronary heart disease risk to an extent that may be clinically important, especially when the pre-test 10-year risk estimate is intermediate. The attached spreadsheet makes these calculations easy.