Continuous Probabilistic Prediction of Angiographically Significant Coronary Artery Disease Using Electron Beam Tomography

Abstract

Background — We sought to incorporate electron beam tomography–derived calcium scores in a model for prediction of angiographically significant coronary artery disease (CAD). Such a model could greatly facilitate clinical triage in symptomatic patients with no known CAD. Methods and Results — We examined 1851 patients with suspected CAD who underwent coronary angiography for clinical indications. An electron beam tomographic scan was performed in all patients. Total per-patient calcium scores and separate scores for the major coronary arteries were added to logistic regression models to calculate a posterior probability of the severity and extent of angiographic disease. These models were designed to be continuous, adjusted for age and sex, corrected for verification bias, and independently validated in terms of their incremental diagnostic accuracy. The overall sensitivity was 95%, and specificity was 66% for coronary calcium to predict obstructive disease on angiography. With calcium scores >20, >80, and >100, the sensitivity to predict stenosis decreased to 90%, 79%, and 76%, whereas the specificity increased to 58%, 72%, and 75%, respectively. The logistic regression model exhibited excellent discrimination (receiver operating characteristic curve area, 0.842±0.023) and calibration (χ 2 goodness of fit, 8.95; P =0.442). Conclusions — Electron beam tomographic calcium scanning provides incremental and independent power in predicting the severity and extent of angiographically significant CAD in symptomatic patients, in conjunction with pretest probability of disease. This algorithm is most useful when applied to an intermediate-risk population.