## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.4 Comparisons Between Predictive Models
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Groups** & **Target** & **Best Serum Cytokines** & **Best Routine Blood Analyses** & **Best Hybrid Data** \\ \hline \multirow{3}{*}{**A vs. B**} & \multirow{3}{*}{Need for IMV} & IL-1 RN, CXCL10, CCL3, CXCL9, & IL-7, HGF & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & LDH, RBCs & LDH, RBCs, CRF, IL-1 RN, CXCL10, \\  & & IL-7, HGF & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } \\  & & (AUC = 0.856; R = Sp = 0.750) & & (AUC = 0.892; R = Sp = 0.850) \\ \hline \multirow{3}{*}{**B vs. C**} & Mortality in the ICU, among patients with IMV & IL-10, HGF & Lymphocytes & Lymphocytes, IL-2R, HGF \\  & & **(AUC = 0.772; R = Sp = 0.7759)** & & (AUC = 0.708; R = Sp = 0.725) \\ \hline \multirow{3}{*}{**A + B vs. C**} & Mortality in the ICU, among patients with/ without IMV & CXC10, JFN-\(\gamma\), VEGF, IL-2R & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & Lymphocytes, RBCs & Lymphocytes, CXCL9, RBCs \\  & & IL-1 RN & \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\  & & (AUC = 0.887; R = 0.820; Sp = 0.847) & & (AUC = 0.875; R = 0.780; Sp = 0.837) \\ \hline \end{tabular}  
* The table presents models built using the information gain algorithm. The best model for each comparison is highlighted in bold. R Recall; Sp Specificity.  
\end{table} 
Table 10: Variables used for the best Naïve Bayes models, considering the three comparisons among groups. validation of our results. This is a critical step in assessing the predictive performance of these models in different clinical settings and evaluating their adaptability to varying patient characteristics and disease severities. Due to our small sample size, to minimize confounding factors, we selected patients with no significant differences in demographics, comorbidities, or other relevant characteristics between groups. However, in future studies with larger cohorts, incorporating demographic and clinical variables, such as age, sex, and comorbidities, could further refine the models' predictive accuracy and enhance their applicability. Another key limitation was the lack of information on other treatments and concomitant infections, which restricted our ability to account for additional confounding variables. Moreover, a database of 2870 sequences covering 25 cytokines (including canonical and isoform variants) was used rather than the entire human proteome. While this approach maximized sensitivity for detecting clinically relevant inflammatory mediators, it did not explore additional proteins potentially involved in disease severity or biological processes underlying each patient's condition. Future work should adopt a broader proteomic approach to better characterize patients' immune responses and potentially uncover additional biomarkers. Furthermore, the depletion of high-abundance proteins such as albumin was not tested, which can overshadow low-abundance peptides. Although our FASP-based protocol, high-dynamic-range UHPLC-QTOF setup, and label-free match-between-runs MaxQuant processing partly mitigated this issue, some inflammatory mediators may still have been under-quantified. Lastly, it is also important to acknowledge the lack of validation of the obtained results considering other techniques. Using immunoassay assays and/or Enzyme-Linked Immunosorbent Assays (ELISA) could complement the results from UHPLC-HRMS by offering specific, sensitive, and quantifiable validation of the cytokines identified in the broader proteomic analysis. This approach could provide a more detailed and accurate understanding of the involved biomarkers. Despite the mentioned limitations, we believe our current analysis provides a solid framework and serves as a valuable starting point for further investigation. It holds particular relevance in the ICU context, where research specifically targeting this population remains limited.