Although risk factors for mortality after cardiac surgery have been identified, there is no widely applicable method for readily determining risk of postoperative morbidity based on preoperative severity of illness. The goal of this study was to develop a model for stratifying the risk of serious morbidity after adult cardiac surgery using readily available and objective clinical data. After univariate analysis of risk factors in 3,156 operations, 11 variables were identified as important predictors by logistic regression (LR) analysis and used to construct an additive model to calculate the probability of serious morbidity. Reliable correlation was found between a simplified additive model for clinical use and the LR model. The clinical and logistic models were then tested prospectively in 394 patients and demonstrated a pattern of increasing morbidity with ascending scores similar to that predicted by the reference group. Increasing clinical risk score was also associated with a greater frequency of individual complications as well as prolongation of ICU stay. This study demonstrates that it is feasible to design a simple method to stratify the risk of serious morbidity after adult cardiac surgery. With further prospective multicenter refinement and testing, such a model is likely to be useful for adjusting severity of illness when reporting outcome statistics as well as planning resource utilization.
ASJC Scopus subject areas
- Pulmonary and Respiratory Medicine
- Critical Care and Intensive Care Medicine
- Cardiology and Cardiovascular Medicine