Categorical variables were described as the number and percentage, and their differences among groups were compared using Chi-squared test. Noncontinuous variables and continuous variables that didn’t follow normal distribution were descried as median and interquartile range (IQR), and were analyzed with non-parametric methods (Mann-Whitney-Wilcoxon for two groups, Kruskal-Wallis for multi-groups). Continuous variables that followed normal distribution were expressed as mean and standard deviations, and t-test (two groups) or Analysis of Variance (ANOVA) (multiple groups) was used for these variables. Kaplan-Meier curves were used to calculate the survive rate and the log-rank test was used for comparisons among the overall population and subgroups. We performed this study via available MIMIC database based on propensity score matching (PSM). The use of PSM aimed to reduce the imbalance between two matched cohorts. The factors included in the PSM were as follows: sex, ethnicity, ICU type, admission group, admission reasons, SOFA, SAPS II, APS III, LODS, OASIS, Elixhause comorbidity, RRT on first day, MV on first day, vasopressor administration on first day, sepsis, infection.

Standardized difference (SDD) before and after matching were plotted to show the effect of matching. In the cohort of propensity score-matched subjects, the SDD of all covariates between cancer group and noncancer group were < 10%, which suggested that the PSM appropriately adjusted for the initial selection bias. The bias in subgroups after successful PSM could also be considered as balanced [22]. A P value < 0.05 was considered statistically significant. Statistical analyses were performed using Stata, version 14.0 (Stata Corp).

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