Statistical Analysis
Continuous data are presented as mean ± standard deviation for Gaussian variables or median [interquartile range (IQR)] for non-gaussian variables and all categorical data as number (percentage). Normality was assessed using the Kolmogorov-Smirnov test. Normally distributed continuous data and categorical data were compared with Pearson’s Chi-squared test or Fisher’s exact test when 25% of available data points had expected values <5. Non-Gaussian distributions were evaluated using Mann-Whitney U test.
Multivariable Cox Proportional Hazards modeling was used to identify predictors for mortality. Competing risk methods by Fine and Grey modeling was used to model all-cause readmission. Multivariable logistic regression was used to identify risk-adjusted predictors for GI complications. Co-variables were first assessed by univariable model with a threshold of P<0.2 for inclusion into the multivariable model.
Propensity score matching on a 1:1 basis using nearest neighbor matching without replacement and caliper setting of 0.2 of the standard deviation of the logit propensity score. Patients were matched on preoperative demographics, comorbidities, and operative characteristics. This resulted in 224 cases, 112 with and 112 without postoperative GI complications.