Quantification of coronary artery calcium by electron beam computed tomography for determination of severity of angiographic coronary artery disease in younger patients.

OBJECTIVES

This study attempted to 1) evaluate five quantitative measures of coronary artery calcium and determine which best agreed with coronary artery disease severity at angiography; and 2) determine optimal quantity cutpoints to distinguish among no, mild and significant disease.

BACKGROUND

Coronary artery calcium identified noninvasively by electron beam computed tomography is a sensitive marker for atherosclerosis. Quantitative assessments of calcium could distinguish among patients with no, mild and significant disease in clinical, screening and research settings.

METHODS

One hundred sixty patients, 23 to 59 years old, underwent coronary angiography and electron beam computed tomography. Coronary artery calcium was defined as dense (> 130 Hounsfield units) foci > or = 2 mm2 on the tomogram. Regression and receiver operating characteristic analyses were used to evaluate five quantitative measures of calcium as predictors of the largest stenosis in the coronary arteries and to identify optimal cutpoints for distinguishing among disease categories. No disease was defined as no stenosis, mild disease as 10% to 49% diameter stenosis in one or more major branches and significant disease as > or = 50% diameter stenosis in one or more major branches.

RESULTS

All measures evaluated performed well. With calcific area as the quantitative measure, the best cutpoint for discriminating between patients with and without disease was the presence of calcium: sensitivity 81%, specificity 86% and overall accuracy 83%. The best cutpoint for discriminating between patients with and without significant disease was 18 mm2: sensitivity 86%, specificity 81% and accuracy 83%.

CONCLUSIONS

Because the ranges of calcium quantity overlapped across disease categories, no cutpoints would distinguish among categories with absolute certainty. However, selected cutpoints could rule out disease in most healthy subjects and identify most patients with significant disease.