A MODEL THAT PREDICTS MORBIDITY AND MORTALITY AFTER CORONARY-ARTERY BYPASS GRAFT-SURGERY

Objectives. This study was performed to develop a method for identifyi ng patients at increased risk for morbidity or mortality after coronar y artery bypass graft surgery. Background. Postoperative morbidity is more common than mortality and is important because of its relation to cost. Methods. Univariate and forward stepwise logistic regression an alysis was used to retrospectively analyze a group of 1,567 consecutiv e patients who underwent bypass surgery between July 1991 and December 1992. We developed a model that predicted postoperative morbidity or mortality, or both, which was then prospectively validated in a group of 1,235 consecutive patients operated on between January 1993 and Apr il 1994. A clinical risk score was derived from the model to simplify utilization of the data. Results. The following factors, listed in dec reasing order of significance, were found to be significant independen t predictors: cardiogenic shock, emergency operation, catheterization- induced coronary artery closure, severe left ventricular dysfunction, increasing age, cardiomegaly, peripheral vascular disease, chronic ren al insufficiency, diabetes mellitus, low body mass index, female gende r, reoperation, anemia, cerebrovascular disease, chronic obstructive p ulmonary disease, renal dysfunction, low albumin, elevated blood urea nitrogen, congestive heart failure and atrial arrhythmias. Observed mo rbidity and mortality for the validation group fell within the 95% con fidence interval of that predicted by the model. Costs were closely re lated to the incidence of postoperative morbidity. Conclusions. Analys is of preoperative patient variables can predict patients at increased risk for morbidity or mortality, or both, after bypass surgery. Incre ased morbidity results in higher costs. Different strategies for high and low risk patients should be used in cost reduction efforts.