Statistical Analysis
Data are shown as mean ± standard deviation (SD) for continuous variables and n (%) for categorical variables. All relevant variables were compared between procedures with and without a 30-day all-cause readmission with Student’s T-test or Chi-square as appropriate. To derive independent predictors of readmission we followed the 10EPV guideline which allowed for 24 degrees of freedom for our adjusted model13. Based on univariate associations and clinical interest we used the following as potential predictors: elective status at index admission, age, sex, disposition, hospital bed size, cardiomyopathy, chronic pulmonary disease, congestive heart failure (CHF), ischemic heart disease (IHD), obesity, hypertension (HTN), peripheral vascular disease (PVD), diabetes mellitus (DM), renal disease including chronic kidney disease (CKD) and end stage renal disease (ESRD), fluid and electrolyte disorder, anemia, and peptic ulcer disease. These variables were entered into a hierarchical logistic regression model using a random intercept for site, with odds ratios and 95% confidence intervals. Next, we ran a selection
procedure using stepwise methods to obtain a parsimonious list of independent predictors of readmission in our cohort. We then used the beta estimates to create a simple integer scoring system to predict readmission14. We then used the applied the scoring system to our derivation cohort and tested the 30-day readmission rate by score. Then using data from the 2014-2015 NRD as a validation cohort, we applied the same integer scoring system to test discrimination with 30-day readmissions. Next, we determined the top causes of readmission using the primary diagnosis code at readmission.14 For general categorization we used the letter and first 2 numbers of the ICD 10 code. All analysis was done with SAS version 9.4 (Cary, NC) with a p- value of 0.05 marking statistical significance.