Statistical analysis
Results are presented as summary statistics, namely median (interquartile range) or absolute frequency (percentage) unless specified otherwise. Comparisons between baseline groups were based on the Mann-Whitney test or the exact Fisher exact test, respectively. Univariable logistic regression models were used to identify factors associated with Intensive Care Unit (ICU) admission. The log-linearity of TTV load effect was checked using splines. The discriminatory performance of the TTV load was measured using the area under the curve (AUC) of the ROC curve, with the optimal cut-off point defined according to the Youden index. Variables associated with the outcome at the 0.05 level in univariable analyses were included in a multivariable model on complete cases, with variable selection based on the Akaike criterion (AIC). Final model was confirmed after multiple imputation by chained equations (MICE) of missing data, with a predictive mean matching method for quantitative variables and logistic regression models (binomial, ordinal, or multinomial) for categorical variables (12). Results are presented as pooled Odds Ratio (OR) from 30 imputed datasets, with 95% confidence intervals (CI). Statistical analyses were performed using R 4.1.1 (https://www.R-project.org/ ). All p-values were two-sided and values of 0.05 or less were considered statistical significant.