Statistical Analysis
We used descriptive statistics and bivariate tests to characterize
distributions of covariates across groups for the overall cohort and
among those hospitalized. Variables associated with the outcome at a
p-value <0.20 in bivariate tests were included in
multivariable analyses. We used multilevel logistic regression models
for all outcomes to account for clustering of patient outcomes among
individual hospitals in the health system. Models estimated odds ratios
and 95% confidence intervals (CI).
In sensitivity analyses, we analyzed all-cause mortality and
hospitalization using a multilevel Cox proportional hazards model
instead. Further, we analyzed AKI and ICU using a multilevel Cox
proportional hazards model with mortality as a competing risk. The Cox
proportional hazards assumption was verified graphically by generating a
plot of the Schoenfeld residuals. In all instances, our primary results
were robust and sensitivity analyses yielded similar inferences.
Analyses were conducted with R, Version 4.0.0, and used 2-sided
statistical tests with a p value < 0.05 as statistically
significant in final analyses.