Preoperative prediction of postoperative morbidity in coronary artery bypass grafting

The risk factors of patients selected for coronary artery bypass grafting have increased in recent years because of the aging population. Prediction of postoperative complications is essential for optimal use of the available resources. The aim of this study was to develop a scoring method for prediction of postoperative morbidity of individual patients undergoing bypass grafting. Data from 386 consecutive patients who underwent coronary artery bypass grafting in a single center were retrospectively collected. The relationship between the preoperative risk factors and the postoperative morbidity was analyzed by the Bayesian approach. Three risk indices (15-factor and seven-factor computed and seven-factor manual models) were developed for the prediction of morbidity. The criterion for morbidity was a prolonged hospital stay postoperatively (>12 days) because of adverse events. The best predictive preoperative factors for increased morbidity were emergency operation, diabetes, rhythm other than sinus rhythm on the electrocardiogram or recent myocardial infarction, low ejection fraction (