R statistics software version 3

All statistical analyses were performed with R statistics software, version 3.5.0 (R Foundation for Statistical Computing, Vienna, Austria). Continuous parameters were presented as means and standard deviations (S) and compared using the Student’s t-test or the Mann–Whitney U test. Categorical parameters were expressed as a number and percentage and compared with chi-square tests. p values of < 0.05 were considered to be statistically significant.

All statistical analyses were performed with R statistics software, version 3.5.0 (R Foundation for Statistical Computing, Vienna, Austria). The association between ED LOS and IHCA was evaluated by descriptive statistics. The normality of distribution was examined using the Kolmogorov–Smirnov test. Continuous variables are presented as medians with interquartile ranges (IQR), and compared by Student’s t-test or the Mann–Whitney U-test, as appropriate. Categorical variables are expressed as number (N) and percentage (%), and compared by chi-square tests. The relationship between ED LOS and IHCA was assessed by Spearman rank correlation coefficient, with the cut-off value of LOS determined by the Youden-index method. Classical methods were utilized to calculate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each LOS cut-off. A multivariate regression model that included potential confounders, such as age, gender, cancer, and KTAS, was used to determine whether ED LOS was an independent risk factor for IHCA. P-values < 0.05 were considered statistically significant.

Statistical analyses were conducted with SPSS version 25.0 statistical software (SPSS, IBM SPSS Inc., Chicago, IL, USA) and R statistics software version 3.5.3 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were reported as mean ± standard deviation if normally distributed, and as median (25th–75th percentiles) if they were skewed. Categorical variables were described as counts and percentages. Logistic regression, Pearson’s Chi-square, Fischer’s exact test, Wilcoxon rank sum test and Kruskal–Wallis test were used for univariable analyses, as appropriate. Cochran Armitage trend-test was used to test trends between multiple groups. Bonferroni method and Steel’s nonparametric multiple comparisons with control were applied in post-hoc testing when applicable in Table 1, Figure 3 and the results chapter. Relationships between continuous variables were studied with Pearson’s correlation. A multivariable logistic regression model was created to study how, both, PIAB and AIAB predict ineffective CV compared to the normal P-wave. Backward stepwise selection was used with variables with a p-value <.10 in the univariable analysis. This model was further used to assess predictors of CV failure and AF recurrence. A p-value <.05 was considered statistically significant.