## 4 Conclusions  
Developing predictive models for patient severity and disease outcomes is crucial, particularly in highly dynamic and heterogeneous ICU populations. In this study, Naive Bayes models were developed to predict COVID-19 disease severity, associated with the need for IMV, and mortality at seven days. A total of 25 serum cytokines and nine routine blood analyses were considered, with models being developed using feature subsets selected through an information gain algorithm or univariate data analysis. Unsupervised t-SNE allowed us to conduct a preliminary analysis of the impact of the entire datasetand feature subsets in distinguishing patient groups. The resulting Naive Bayes models demonstrated good performance, predicting the need for IMV (AUC = 0.891) and mortality in both homogenous (AUC = 0.774) and more heterogenous (AUC = 0.887) populations using two to nine features. Interestingly, most of the best models included features that were not flagged as significant in the univariate analysis, pointing to the importance of multivariate approaches in capturing the complex interplay of diverse metabolic networks underlying ICU patents' conditions. Furthermore, our findings highlight the potential of hybrid predictive models that integrate cytokine profiling with routine blood tests to enhance risk stratification and patient management in ICUs. However, further research is needed to validate these findings and address key challenges mainly related to cytokine profiling, such as assay standardization and redefinition of thresholds for clinical interpretation. Overcoming these limitations could enable this approach to deliver timely and more precise healthcare interventions, ultimately improving patient outcomes. Nevertheless, the potential to formulate effective prediction models for disease severity and the outcome of critically ill patients using a limited number of variables, which could be applied in the ICU to promote faster decision-making, is promising. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15094823/s1](https://www.mdpi.com/article/10.3390/app15094823/s1)]([https://www.mdpi.com/article/10.3390/app15094823/s1](https://www.mdpi.com/article/10.3390/app15094823/s1)). Figure S1: Quality control of the proteomics analyses performed. Quality control of the proteomics analyses was performed by evaluating the retention time (Figure S1A), chromatographic peak width (Figure S1B), precursor charge (Figure S1C), mass precision (Figure S1D), abundance (Figure S1E), missed cleavages (Figure S1F), and peptide identification per group (Figure S1F), across all identified peptides. The number of peptides identified per sample coming from the target proteome, per sample group, was also analyzed (Figure S1G). A comprehensive file containing the search results for all identified protein groups is also provided (see Supplementary Table S1). Conceptualization, C.P.V.R., T.A.H.F., A.M. and C.R.C.C. ; Methodology, All authors; Software, C.P.V.R., T.A.H.F., R.A. and G.C.J. ; Validation, C.P.V.R., T.A.H.F., R.A., A.M. and I.P. ; Formal Analysis, C.P.V.R., A.M., I.P. and C.R.C.C. ; Investigation, All authors; Resources, M.C.O., L.B. and C.R.C.C. ; Data Curation, C.P.V.R., T.A.H.F., R.A. and G.C.J. ; Writing--Original Draft Preparation, C.P.V.R. and C.R.C.C. ; Writing--Review & Editing, All authors; Visualization, C.P.V.R. and T.A.H.F. ; Supervision, L.B. and C.R.C.C. ; Project Administration, L.B. and C.R.C.C. ; Funding Acquisition, L.B. and C.R.C.C. All authors have read and agreed to the published version of the manuscript. This research was supported by Fundacao para a Ciencia e a Tecnologia (FCT), grant numbers DSAIPA/DS/0117/2020 and RNEM-LISBOA-01-0145-FEDER-022125 (Portuguese Mass Spectrometry Network). Centro de Quimica Estrutural is a Research Unit funded by FCT through projects UIDB/00100/2020 and UIDP/00100/2020. Institute of Molecular Sciences is an Associate Laboratory funded by FCT through project LA/P/0056/2020.