## 2 Patients, Materials, and Methods
### Statistical Analysis  
The normality of data distribution was analyzed by the Shapiro-Wilk test. The data for continuous variables are presented as mean \(\pm\) SD (standard deviation), when normally distributed, or median and interquartile range (IQR) in cases of skewed distribution, and as frequencies (percentages) for categorical variables. Differences in variables between groups were compared using a t-test for normally distributed or a Mann-Whitney U test for continuous variables with skewed distribution. In addition, the Pearson \(\chi^{2}\) test was used for group comparisons of categorical variables. The correlations between variables were analyzed by [PERSON]'s correlation analysis. To test the association between baseline depression (predictor) and postoperative hospital LOS (outcome variable), we analyzed the baseline BDI-II depression scores using the binary cut-off of 13 (\(\leq\)13 vs. \(>\)13) and modeled associations between baseline depression and postoperative hospital LOS using the binary logistic regressions analysis. Furthermore, a binary cut-off of 7 days was implemented for the postoperative hospital LOS, with prolonged hospitalization defined as LOS \(>\) 7 days [48; 53]. The binary logistic regression analysis results are presented as OR (odds ratios) with 95% CI (confidence interval). The covariates that might relate to the postoperative hospital LOS were included in the models of binary logistic regressions based on the statistical significance (\(p\)-value \(<\) 0.05) of [PERSON]'s correlation analysis and Mann-Whitney test for continuous data or the Pearson \(\chi^{2}\) test for categorical variables. Model 2 of adjusted binary logistic regression analysis included only age and sex as covariables. Model 3 further included other demographic and clinical variables (current smoking status, EuroSCORE-II, preoperative hemoglobin levels, the use of \(\beta\)-blockers, extubation/ventilation time, and postoperative atrial fibrillation) that could confound or mediate the association between depressive symptoms and postoperative hospital LOS. However, to avoid double adjustment, variables such as age, gender, or insulin-dependent diabetes mellitus that are already included in the EuroSCORE-II calculation were not included in Model 3 of adjusted binary logistic regression. The same approach for non-adjusted and adjusted binary logistic regression was used to analyze the association of preoperative and post-operative CRP values (\"early\" and \"persistent\" post-operative CRP response) with postoperative hospital LOS (outcome variable). In addition, baseline CRP values, as a separate variable, were also included in Models 2 and 3 of binary logistic regression to assess the relative contribution of both baseline and post-operative CRP values on postoperative hospital LOS as previously described [48]. The association between depression symptoms and preoperative and postoperative CRP plasma levels was also assessed by the linear regression model. Covariates included in the linear regression analysis for changes in preoperative CRP levels and postoperative CRP response were mainly the same as the variables included in Model 3 of binary logistic regression, using the same inclusion criteria as described above. The only difference was the introduction of infection as additional covariates to control for its possible contribution to the postoperative elevation of CRP levels. To examine the hypothesis in which the relationship between the independent (BDI-II depression score)and dependent (postoperative hospital LOS) variables is affected by a third variable (CRP response), that is, to assess the indirect relationship between the predictor and outcome variable, mediation analysis was performed. For that purpose, Hayes SPSS PROCESS macro v.4.1 was used [55; 56; 57; 58]. The 95% percentile bootstrap confidence intervals (CIs) based on 5000 samples were used to test the significance of indirect effects of depression in all analyses.