Methods: We retrospectively evaluated 205 patients (143 males, 62 females; mean age 59.7±10.1 years; range, 28 to 83 years) undergoing CABG surgery between June 2001 and August 2008 at Jamaran Heart Hospital, Tehran, Iran. Baseline characteristics of the patients and postoperative bleeding status were recorded. For classifying the bleeders and non-bleeders, classic logistic regression and decision tree models were utilized.

Results: Logistic regression analysis showed that sex was significantly related to postoperative bleeding. Decision tree model revealed that age (score= 100), diabetes mellitus (score= 16.38), sex (score= 13.67), capital residency (score= 7.31) and dyslipidemia (score= 5.06) were found to have an impact on bleeding. We also observed that the decision tree model provided a better classification of the patients than logistic regression.

Conclusion: Surgeons should be aware of risk indicators of bleeding such as older age, male sex, absence of diabetes mellitus and presence of dyslipidemia in patients with three bypassed vessels before CABG. We also recommend statisticians to utilize the decision tree model instead of logistic regression analysis in classification of risk groups.