Continuous variables were presented as means with SD, while categorical variables were presented as frequencies. LOS at CICU and postoperative LOS were dichotomised to designate two groups (normal LOS and prolonged LOS). We defined prolonged LOS as ≥75th centile. In the absence of a prescribed LOS in the literature and to confirm the appropriateness of our selection, we consulted the surgeons for their judgement who agreed on this cut-off value. In general, there is a variability in medical research for defining the period at which a stay is considered as prolonged.14 (link) Moreover, the use of the LOS at the 75th centile is consistent with other studies. Postoperative LOS was defined as the time between the day of surgery and discharge from the hospital, while CICU LOS was defined as the time in days between the admission and discharge from CICU. For identifying difference between groups, we used t-tests for continuous variables, Mann-Whitney for non-normally distributed variables and χ2 for categorical variables.
For identifying predictors of postoperative LOS, a univariate logistic regression was first performed to select variables that are significantly related to the postoperative LOS. Factors with p<0.10 were then included in the multivariate analysis. These included sex, age, body mass index (BMI), history of diabetes, history of renal failure, history of cerebrovascular disease, history of respiratory disease, pulmonary hypertension, congestive heart failure (CHF) at current admission, preoperative arrhythmia, preoperative inotropic support, left ventricular ejection fraction (≥40% vs <40%), preoperative haematocrit (Hct) level, non-elective surgery, type of surgery, use of cardiopulmonary bypass (CPB) machine, inotropic support after operation, use of packed red blood cells (PRBC) and number of complications. A backward stepwise logistic regression was used to identify factors that had an independent effect on the postoperative LOS. A p value of 0.1 to enter a factor and 0.2 to remove it were used.
For identifying predictors of postoperative LOS, a univariate logistic regression was first performed to select variables that are significantly related to the postoperative LOS. Factors with p<0.10 were then included in the multivariate analysis. These included sex, age, body mass index (BMI), history of diabetes, history of renal failure, history of cerebrovascular disease, history of respiratory disease, pulmonary hypertension, congestive heart failure (CHF) at current admission, preoperative arrhythmia, preoperative inotropic support, left ventricular ejection fraction (≥40% vs <40%), preoperative haematocrit (Hct) level, non-elective surgery, type of surgery, use of cardiopulmonary bypass (CPB) machine, inotropic support after operation, use of packed red blood cells (PRBC) and number of complications. A backward stepwise logistic regression was used to identify factors that had an independent effect on the postoperative LOS. A p value of 0.1 to enter a factor and 0.2 to remove it were used.
