Statistical analysis:
Descriptive statistics are presented as frequencies with percentages for categorical variables and as means with standard deviations for continuous variables. Baseline characteristics were compared using a Pearson𝜒2 test and Fisher’s exact test for categorical variables and independent samples t-test for continuous variables. Trends analysis was performed using Analysis of variance (ANOVA). Linear regression was used to predict trends over calendar years. Logistic regression was performed to estimate odds ratios (ORs) with 95% confidence intervals (CIs) to determine predictors for mortality. Initially, a binomial logistic regression model was used to identify variables from demographic data (Table 1) that were significantly associated with patient mortality (P value ˂ 0.10). These variables were then subsequently utilized in a multivariable logistic regression model to identify statistically significant predictors of mortality. In the final model, P-value of <0.05 was used as cutoff for stepwise forward entry for logistic regression. A type I error rate of <0.05 was considered statistically significant. All statistical analyses were performed using statistical package for social science (SPSS) version 26 (IBM Corp). Discharge weights provided by NIS were used for computation of national estimates. All analyses were done on a weighted sample.