OBJECTIVE: Analysis of major perioperative morbidity has become an important factor in assessment of quality of patient care. We have conducted a prospective study of a large population of patients undergoing coronary artery bypass surgery (CABG), to identify preoperative risk factors and to develop and validate risk-prediction models for peri- and postoperative morbidity. METHODS: Data on 4567 patients who underwent isolated CABG surgery over a 10-year period were extracted from our clinical database. Five postoperative major morbidity complications (cerebrovascular accident, mediastinitis, acute renal failure, cardiovascular failure and respiratory failure) were analysed. A composite morbidity outcome (presence of two or more major morbidities) was also analysed. For each one of these endpoints a risk model was developed and validated by logistic regression and bootstrap analysis. Discrimination and calibration were assessed using the under the receiver operating characteristic (ROC) curve area and the Hosmer-Lemeshow (H-L) test, respectively. RESULTS: Hospital mortality and major composite morbidity were 1.0% and 9.0%, respectively. Specific major morbidity rates were: cerebrovascular accident (2.5%), mediastinitis (1.2%), acute renal failure (5.6%), cardiovascular failure (5.6%) and respiratory failure (0.9%). The risk models developed have acceptable discriminatory power (under the ROC curve area for cerebrovascular accident [0.715], mediastinitis [0.696], acute renal failure [0.778], cardiovascular failure [0.710], respiratory failure [0.787] and composite morbidity [0.701]). The results of the H-L test showed that these models predict accurately, both on average and across the ranges of patient deciles of risk. CONCLUSIONS: We developed a set of risk-prediction models that can be used as an instrument to provide information to clinicians and patients about the risk of postoperative major morbidity in our patient population undergoing isolated CABG
