Electron-beam computed tomography: A Bayesian approach to risk assessment

The epidemic of coronary artery disease continues to affect a large number of individuals who often experience sudden and unexpected events. This unde rscores the need to develop more effective programs to detect silent athero sclerosis, with the ultimate goal of preventing coronary events. The use of conventional risk factors is helpful in assessing the median risk of a pop ulation, but it is often unsatisfactory in estimating the actual risk of an individual patient. As a consequence, newer imaging modalities are being d eveloped to detect atherosclerosis in its early developmental phases. Techn ologies such as electron-beam computed tomography (EBCT) may render risk st ratification more accurate if used in the appropriate patient populations a nd with the right diagnostic approach. Several studies have already demonst rated the power of coronary calcification as a strong predictor of future c ardiovascular events. Nonetheless, the medical literature is currently perv aded by an animated debate, as some investigators still have concerns about the effectiveness of a preventive approach driven by technology. The use o f Bayesian models to interpret data acquired with EBCT screening may provid e practitioners with valuable evidence to aid in their decision making. (C) 2001 by Excerpta Medico, Inc.