Analysis
Statistical analysis was performed with SAS 9.2 software (SAS Institute,
Inc, Cary, NC). Categorical variables were expressed as percentages.
Continuous variables were expressed as means ± standard deviations. We
analyzed categorical variables with chi-square analysis, or if there
were few outcomes, Fisher’s exact test was used. Continuous variables
that had a normal distribution were analyzed using the Student’s t-test,
and variables that had non-normal distribution were analyzed using the
Wilcoxon rank-sum test. Statistical significance was based on
two-sided p-values less than 0.05.
Variables with a univariate p-value of less than 0.25 and those of known
biological importance were selected for inclusion in a multivariable
logistic regression model to identify independent predictors of hospital
mortality. Model discrimination and calibration were evaluated by the
area under the receiver operating characteristic curve (C statistic),
and the Hosmer–Lemeshow goodness-of-fit statistic, respectively. In the
current study, a model’s discrimination with an area under the ROC curve
greater than 0.7 was considered a good model, and Hosmer–Lemeshow test
with P > 0.05 indicates a well-calibrated model. To
validate the final predictive model, we did bootstrap replication with
1000 re-samples.