Use of a Bayesian statistical model for risk assessment in coronary artery surgery

Abstract

A computerized statistical model based on the theorem of Bayes was developed to predict mortality after coronary artery bypass grafting. From January, 1984, to April, 1987, at our hospital, 700 patients underwent isolated coronary artery bypass grafting. The presence or absence of 20 risk factors was determined for each patient. The first 300 patients formed the initial database of the Bayesian predictive model, and the remaining 400 patients were prospectively evaluated in four groups of 100 each. Each group was prospectively evaluated and then incorporated into the database to update the model. There was good agreement between predicted and observed results. Bayesian theory is particularly suited to this task because it (1) accommodates multiple risk factors, (2) is tailored to one's specific practice, (3) determines individual, rather than group, prognosis, and (4) can be updated with time to compensate for a changing patient population. These flexible attributes are especially valuable in light of recent changes in the coronary artery bypass graft patient profile.