## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.4 Comparisons Between Predictive Models  
Considering all cytokines included in reasonable models from Tables 4, 6 and 8, across the three comparisons, i.e., A vs. B, B vs. C, and A plus B vs. C, we attempted to establish cytokine sets exclusively associated with the outcomes \"need for IMV\" and \"mortality in the ICU\". Notably, CCL3 and IL-1\(\alpha\) were exclusive to models predicting the need for IMV (A vs. B). This is consistent with previous studies reporting a negative correlation between CCL3 levels and lung compliance [65], as well as elevated IL-1\(\alpha\) levels in patients undergoing IMV [66]. Regarding the best models for predicting mortality in the ICU (B vs. C and A plus B vs. C), the cytokines exclusively included in these models were IL-10, IL-2R, and VEGF. Unlike IL-10, the association of IL-2R [67] and VEGF with COVID-19 severity and other diseases [35; 36; 68] has already been highlighted in the univariate analysis section. According to other authors, IL-10 levels have shown a positive correlation with the lung-injury Murray score in patients with severe respiratory failure due to acute respiratory distress syndrome [69], as well as in-hospital mortality [70]. These findings further underscore the relevance of these cytokines in stratifying risks for critical COVID-19 outcomes. Across all three comparisons, CXCL9 and CXCL10 were consistently included in some of the best models. Both belong to the CXC chemokine family and are often described as interferon inducible chemokines. CXCL9 participates in leukocyte chemotaxis, promoting their differentiation and proliferation, and facilitating tissue extravasation. Meanwhile, CXCL10 acts as an inflammatory mediator, attracting immune cells such as T cells, eosinophils, natural killer cells, and monocytes/macrophages to sites of infection and injury [63; 71]. Different studies have demonstrated that both CXCL9 and CXCL10 are elevated in non-survivor COVID-19 patients, with their levels increasing with severity [63]. Notably, CXCL10 has been specifically associated with severe acute respiratory syndrome [63], which may explain why it was exclusively retained in the best models for predicting the need for IMV. Interestingly, most predictive models did not incorporate IL-6, a potent pro-inflammatory cytokine implicated in the cytokine storm and linked to unfavorable outcomes in severe COVID-19 cases [72]. One potential explanation is that our models focused exclusively on critically ill patients, all admitted to the ICU. In this population, cytokine profiles may be \begin{table}
\begin{tabular}{l c c c c} \hline \multirow{2}{*}{**Friedman Test**} & \multicolumn{3}{c}{_p_**-Values Adjusted by the Bonferroni Correction for Multiple Tests**} \\ \cline{2-5}  & **Overall \(p\)-Values from the Friedman Test** & \multicolumn{2}{c}{**Cytokines vs.**} & \multicolumn{2}{c}{**Cytokines vs.**} & **Routines Blood** \\  & **the Friedman Test** & \multicolumn{2}{c}{**Routine Blood**} & **Hybrid Data** & **Analyses vs.** \\ \cline{2-5}  & & **Analyses** & **Hybrid Data** & **Hybrid Data** \\ \hline Full datasets & 0.368 & 0.999 & 0.472 & 0.999 \\ \hline Subsets achieved by the information & & & & \\ gain algorithm & **<0.001** & **0.029** & **<0.001** & 0.716 \\ \hline \end{tabular}  
Statistically significant results at a 5% significance level are highlighted in bold.  
\end{table} 
Table 9: Comparisons between prediction models for mortality in the ICU (Groups A + B vs. Group C). influenced by a range of factors beyond just IL-6, potentially reducing its predictive value. Alternatively, other cytokines that remained in the best models may have shown stronger associations with the specific clinical endpoints considered for these models. Regarding model performance, Table 10 displays the best performing prediction models considering the three comparisons among groups and the three datasets.