## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.3 Comparisons Between the Combined Groups A and B and Group C  
The t-SNE plot using all features from each dataset did not reveal data separation according to patient groups (Figure 6a-c), in contrast to the previous sections (Figures 4a-c and 5a-c). This was most probably related to the high variability of the population, where the control group (A + B) included surviving patients with and without IMV, and Group C solely included patients under IMV. Despite that, reasonable to good Naive Bayes models were developed using each of the three datasets (AUCs > 0.717) (Table 8). The models built using feature subsets showed some improvement in predictive performance. For example, the subset including only five cytokines--CXCL9, IFN-\(\gamma\), VEGF, IL-2R, and IL-1 RN--achieved an AUC of 0.768, compared to 0.717 when using the entire dataset. Univariate data analysis had previously identified IFN-\(\gamma\), VEGF, and IL-2R as significantly different between the Groups A + B and C. However, models based solely on cytokines identified through univariate analysis demonstrated poorer results (AUCs < 0.675). CXCL9 and IL-1 RN were classified as important features through the information gain algorithm, not only within the cytokine dataset but also within the hybrid dataset. For instance, a hybrid model incorporating lymphocytes, CXCL9, RBCs, IFN-\(\gamma\), VEGF, IL-2R, and IL-1 RN achieved an AUC of 0.885. Interestingly, both CXCL9 and IL-1 RN had been previously included in the best prediction models when comparing Groups A and B (see Table 4). As mentioned in Section 3.1.1, IL-1 RN has been shown to be significantly elevated in COVID-19 patients with respiratory failure admitted to the ICU. A study by [PERSON] et al. explored this association using ROC analysis, reporting an AUC of 0.737 [37]. Although multivariate analysis was not performed, the authors concluded that the increase in IL-1 RN levels, along with elevations in other cytokines, may reflect systemic cytokine activation in patients with respiratory failure, culminating in pulmonary dysfunction and the need for IMV [37]. Supporting this observation, [PERSON] et al. identified IL-1 RN as one of the cytokines that is most strongly correlated with respiratory failure (r > 0.700, \(p<0.001\)) [62]. This finding highlights its role in the innate immune response, particularly in immune cell infiltration into infected tissues, further underscoring IL-1 RN's contribution to the immunopathology associated with severe COVID-19 and respiratory dysfunction [62]. When it comes to CXCL9, it has been associated with disease severity and mortality in COVID-19. [PERSON] et al. reported that CXCL9 was significantly increased in non-surviving patients at ICU admission and revealed adequate performance in predicting mortality (AUC: 0.737, 95%CI 0.609-0.865) [63]. Furthermore, a multivariate logistic regression model was developed that included age, weight, IL-10, CCL2, CCL5, CXCL10, and CXCL9, with internal validation yielding an AUC of 0.838 (\(p\) = 0.244) [63]. While the cited studies highlight the potential of IL-1 RN and CXCL9 in respectively predicting disease severity and the need for IMV and patient outcomes, their focus was predominantly on cytokine profiles, with some lacking multivariable models. Furthermore, few studies have examined an extensive panel of cytokines specifically in ICU patients or integrated routine blood analyses into their models. By employing hybrid models, our methodology provides a more comprehensive representation of the underlying biological mechanisms, offering insights that may not be fully captured through single-variable analyses--especially in the highly heterogeneous ICU population.