Data for continuous variables are expressed as mean and standard deviation or median and interquartile ranges (IQR), and data for categorical variables are expressed as frequency and percentage. Differences between continuous data were assessed by Student t test or by Mann-Whitney U test. Differences between categorical variables were assessed by χ2 test.

In-hospital mortality and discharge were evaluated by competing risks survival analysis, represented by cumulative incidence function (CIF) [3]. The Fine and Gray proportional sub-distribution hazard model was fitted in order to estimate the effect of covariates on CIFs in-hospital death and discharge [6]. Covariates used for multivariate analyses were chosen based on their significance in the univariate analysis (p<0.10). Covariates in the final model with a p-value <0.05 were considered statistically significant. The results are presented as adjusted hazard ratios (HR) and their 95% confidence intervals (CI). Competing risks analyses were performed in SAS version 9.4. Hotdeck missing imputation data and the assessment of discrimination and calibration and were performed in R Core Team (2019). We used hot.deck function from hot.deck library in R 3.8.0.

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