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.
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.


CONCLUSIONS
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.
Pletcher
Mark J
MJ
Department of Epidemiology and Biostatistics, University of California, San Francisco 500 Parnassus Ave, MU 420 West, Box 0560, San Francisco, CA 94143-0560, USA. mpletcher@epi.ucsf.edu
Tice
Jeffrey A
JA
Pignone
Michael
M
McCulloch
Charles
C
Callister
Tracy Q
TQ
Browner
Warren S
WS
D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002114:Calcinosis / Q000000981:diagnostic imaging / Q000209:etiology*; D003327:Coronary Disease / Q000000981:diagnostic imaging / Q000209:etiology*; D003925:Diabetic Angiopathies / Q000209:etiology; D005260:Female; D006801:Humans; D006937:Hypercholesterolemia / Q000150:complications; D006973:Hypertension / Q000150:complications; D008297:Male; D008875:Middle Aged; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012737:Sex Factors; D014057:Tomography, X-Ray Computed