Prism 8

Statistical analyses were performed with GraphPad Prism 8.1.0 and RStudio version 4.0.2. Two-sided parametric (t and χ²) and nonparametric (Mann–Whitney) tests were used at α = 0.05 to test for group differences. The Z-score was calculated through Prism using the formula $z=\frac{x-\mu }{\sigma }$ where x is the raw score, μ is the population mean, and σ is the SD. Logistic regression was used to determine the relationship between rUTI and cytokine concentration with a standard cutoff probability of 0.5. McFadden’s pseudo-R² and its P-value were used to assess model fit. Leave-one-out cross-validation was used to evaluate the predictive power of each logistic model via AUC, sensitivity, specificity, misclassification rate, and F1-score. Receiver operating characteristic curves were generated by plotting the false-positive rate (1-specificity) against the true-positive rate (sensitivity).

All statistical analyses were performed in GraphPad Prism 8.1.0 and R Studio version 4.0.2 with an α of 0.05. Hypothesis testing was performed using classical methods. Pairwise associations between continuous variables were performed using Spearman’s rho correlation with P-values generated by permutation. Differences between continuous variables by group were analyzed using ANOVA or t test if normally distributed, or the Mann–Whitney U test, Wilcoxon matched pairs signed rank test, or Kruskal–Wallis test with multiple comparison post hoc if not normally distributed. Differences between continuous variable by group were analyzed using ANOVA, t test, or Kruskal–Wallis test with multiple comparison post hoc. Differences between categorical variables were analyzed using chi-square, Fisher’s exact test, or ordinal logistic regression. Various logistic regression models were constructed to predict rUTI using combinations of all significant covariates. Model fit was assessed using McFadden’s pseudo-R². The predictive power of each logistic regression model was assessed using F-scores and AUC attained through leave-one-out cross validation. Models were further compared using ANOVA.

The statistical analysis and mapping were performed using Prism8 and Rstudio based on R version 4.0.4. A comparison between the two groups was performed using the two-tailed Student’s t-test. Data were expressed as means ± SEM, and a p-value < 0.05 was considered significant.