A model that predicts morbidity and mortality after coronary artery bypass graft surgery.

Objectives: This study was performed to develop a method for identifying patients at increased risk for morbidity or mortality after coronary 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 analysis was used to retrospectively analyze a group of 1,567 consecutive 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 April 1994. A clinical risk score was derived from the model to simplify utilization of the data.

Results: The following factors, listed in decreasing order of significance, were found to be significant independent predictors: cardiogenic shock, emergency operation, catheterization-induced coronary artery closure, severe left ventricular dysfunction, increasing age, cardiomegaly, peripheral vascular disease, chronic renal insufficiency, diabetes mellitus, low body mass index, female gender, reoperation, anemia, cerebrovascular disease, chronic obstructive pulmonary disease, renal dysfunction, low albumin, elevated blood urea nitrogen, congestive heart failure and atrial arrhythmias. Observed morbidity and mortality for the validation group fell within the 95% confidence interval of that predicted by the model. Costs were closely related to the incidence of postoperative morbidity.

Conclusions: Analysis of preoperative patient variables can predict patients at increased risk for morbidity or mortality, or both, after bypass surgery. Increased morbidity results in higher costs. Different strategies for high and low risk patients should be used in cost reduction efforts.