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 Ë‚ 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.