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.