Table 5. Logistic regression model for short LOS (less than 6 days).

Variables	OR	P value	CI
Robotic CABG	3.31	< 0.001	2.00 - 5.50
NY State Complication Composite	0.13	< 0.001	0.09 - 0.19
Age	0.96	< 0.001	0.95 - 0.97
BMI more than 30.0	0.72	0.006	0.57 - 0.91
Race Not-White vs. White	0.62	< 0.001	0.48 - 0.81
Ethnicity Hispanic vs. Not-Hispanic	1.48	0.001	1.17 - 1.89
Dialysis	0.34	< 0.001	0.20 - 0.91
CHF	0.49	< 0.001	0.36 - 0.67
Cerebrovascular disease	0.72	0.04	0.93 - 0.98
LVEF	1.01	0.031	1.00 - 1.02
IABP	0.36	0.001	0.20 - 0.68

Robotic CABG - Robotic coronary artery bypass grafting versus conventional CABG, OR – odd ratio, CI – 95 % confidence interval, BMI – body mass index, CHF – congestive heart failure, LVEF – left ventricular ejection fraction, IABP – intra-aortic balloon pump. The variables included in the full models were age, gender, race (White or not-White), ethnicity (Hispanic vs. not-Hispanic), BMI, history of cerebro-vascular disease, CHF, diabetes, renal failure requiring dialysis, chronic obstructive lung disease (COPD), previous MI within 30 days, LVEF, urgency status (elective vs. urgent), number of diseased coronary arteries, left main coronary artery disease, and IABP. The variables insignificant by the Wald test (p > 0.05) were sequentially removed from the models if they were not confounders. Age (p = 0.753) and LVEF (p = 0.549) met assumption of linearity in fractional polynomials test and entered as continuous variables. BMI barely met assumption of linearity (p = 0.055) and was to 30 as criteria for obesity and entered to the model.