## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.2 Comparisons Between Groups B and C  
When examining the t-SNE plots based on the three datasets (Figure 5a-c), some separation between the two groups was observed, especially when considering all 25 cytokines (Figure 5a). This highlights once again the relevance of cytokines in predicting patient outcomes, in this case, mortality. Despite this, and as observed in the previous section, all three Naive Bayes models based on entire datasets (25 cytokines, nine routine blood analyses results, and the 34 features together) showed very poor performance (AUC < 0.445; Table 6). In comparison to what was reported in the previous section, when resorting to the information gain algorithm to select the most informative features, much better predictive models were achieved. The best models reached AUCs of at least 0.708 (Table 6). A reasonable model was developed using two cytokines, HGF and IL-10 (AUC = 0.772; R = 0.775; Sp = 0.775), while a slightly less effective model was developed based on lymphocyte counts alone (AUC = 0.708; R = 0.725; Sp = 0.725). An even greater AUC was achieved with hybrid data, incorporating lymphocyte counts, IL-2R, and HGF (AUC = 0.774). While it may appear that models solely relying on lymphocyte counts and those incorporating cytokine profiles exhibited similar predictive performance, additional statistical analyses (Table 7) revealed that the achieved hybrid models demonstrated a significant improvement in performance compared to those including solely routine blood analyses (\(p\) < 0.001). This demonstrates that while lymphocyte counts are strong predictors of patient outcomes, as supported by other authors [57; 58; 43], incorporating serum cytokine profiles and routine blood analyses significantly improves both the accuracy and robustness of predictions. These hybrid models provide a better reflection of the underlying biological mechanisms influencing patients' conditions, which may not be fully captured by lymphocyte counts alone. Several studies have accounted for the limitations of using lymphocytes as the sole predictor of mortality in ICU COVID-19 patients, highlighting the importance of a more comprehensive, multifactorial approach [59; 60; 61]. Another interesting observation was that these predictive models identified lymphocyte counts as relevant, in accordance with the previously univariate data analysis, where lymphocytes were significantly lower for patients that died in the ICU (\(p\) = 0.015). In contrast, the cytokines included in the best predictive models were not highlighted as significantly different among groups in the univariate analysis. For example, the VEGF, that was actually significantly different between Groups B and C (\(p\) = 0.083), was not included in the best predictive models. In fact, the model obtained using exclusively this cytokineshowed poor performance (AUC = 0.541). Regarding the hybrid models including VEGF, the results improved, but most likely due to the relevance of lymphocytes (AUC = 0.636). Thus, tools like those employed in machine learning algorithms can be very useful in the selection of the best features to predict a certain phenotype.