Two new mathematical models for prediction of early mortality risk in coronary artery bypass graft surgery

The aim of this study was to develop new models for prediction of short-term mortality risk in on-pump coronary artery bypass grafting (CABG) surgery using decision tree (DT) methods.

Between September 2005 and April 2006, 948 consecutive patients underwent CABG surgery at Rajaie Heart Center. Potential risk factors were reviewed and univariate and multivariate analysis for short-term mortality were performed. The whole dataset was divided into mutually exclusive subsets. An entropy error fuzzy decision tree (EEFDT) and an entropy error crisp decision tree (EECDT) were implemented using 650 (68.6%) patient data and tested with 298 (31.4%) patient data. Ten times hold-out cross validation was done and the area under the receiver operative characteristic curve (AUC) was reported as model performance. The results were compared with the logistic regression (LR) model and EuroSCORE.

The overall short-term mortality rate was 3.8%, and was statistically higher in women than men (P<.001). The final EEFDT selected 19 variables and resulted in a tree with 39 nodes, 20 conditional rules, and AUC of 0.90±0.008. The final EECDT selected 15 variables and resulted in a tree with 35 nodes, 18 conditional rules, and AUC of 0.86±0.008. The LR model selected 10 variables and resulted in an AUC of 0.78±0.008; the AUC for EuroSCORE was 0.77±0.003. There were no differences in the discriminatory power of EEFDT and EECDT (P=.066) and their performance was superior to LR and EuroSCORE.

EEFDT, EECDT, LR, and EuroSCORE had clinical acceptance but the performance and accuracy of the DTs were superior to the other models.