## 1 Introduction  
Improving the management of critically ill patients, particularly in intensive care units (ICUs), has become crucial, given the growing prevalence of chronic diseases, the emergence of new infectious diseases such as the coronavirus disease 2019 (COVID-19), and the concomitant rise in healthcare expenses over the past few years. To improve patient management within the dynamic and heterogeneous populations at ICUs, it is fundamental to create models for the early stratification of patients, considering illness severity, potential complications and mortality risk. Within this context, diverse ICU severity scores have been developed, including the Acute Physiology and Chronic Health Evaluation System (APACHE) and the Simplified Acute Physiology Score (SAPS), as well as organ dysfunction scores like the Sequential Organ Failure Assessment (SOFA) [1,2]. However, these scores often require a substantial amount of demographic and clinical data and present a significant margin of error, hence being primarily used for risk stratification, inter-unit comparisons, and evaluating care quality for patients with similar risk profiles [1,2]. Therefore, it remains relevant to develop new models that, relying on a limited number of variables that may be easily collected throughout hospitalization, can accurately and precisely predict disease severity and patient outcomes in the ICU. Such a small number of analytes could, in the future, be detected using biosensors with minimally invasive methods like a drop of blood, providing essential predictive information and enabling rapid decision-making in ICUs. A common factor among critically ill patients in ICUs is their complex inflammatory processes, which must be well understood for effective management. Serum cytokines could overcome the limitations of the aforementioned scoring systems, as they are individual quantifiable and objective indicators of physiological and pathological processes. Furthermore, they can be measured repeatedly, offering real-time insights into patients' conditions. By developing predictive models that integrate cytokines and other biomarkers, healthcare professionals could achieve more accurate, reliable, and comprehensive assessments of patients' conditions in the ICU, enhancing decision-making and improving patient outcomes. For example, [PERSON] et al. developed several models to predict in-hospital mortality in COVID-19 patients using clinical characteristics and inflammatory cytokines levels from the first day of admission [3]. All models achieved accuracies greater than 80%, with IFN-\(\alpha\), IL-8, and IL-6 being identified as key biomarkers for the prognosis of COVID-19 patients [3]. [PERSON] et al. demonstrated that COVID-19 mortality could be predicted using a combination of IL-8 and lymphocyte subsets, achieving an AUC of 0.956 (95% CI, 0.928 to 0.984), with sensitivity and specificity exciting 90% [4]. Recently, [PERSON] et al. also developed another significant prediction model for COVID-19 mortality risk, incorporating 20 biomarkers, with an AUC of 0.98 (95% CI, 0.96 to 0.98) [5]. Moreover, [PERSON] et al. determined that D-dimer, ferritin, and IL-6 could predict the severity of COVID-19 with 97% accuracy; this model even achieved 92% accuracy when IL-6 was not considered, making it useful for hospitals with limitations in testing for this biomarker [6]. Methodologies like these that use patients' clinical information and laboratory analyses have also been applied in other areas, enabling the prediction of, e.g., pulmonary infection in stroke patients [7], sepsis in the ICU [8], and acute kidney injury in ICU patients with cerebrovascular disease [9]. All of these findings underscore the critical importance of comprehending the intricate immunopathological mechanisms underlying inflammatory responses to optimize patient care. Despite their usefulness, most of these models primarily focus on hospitalized patients, with relatively few studies specifically addressing ICU care--particularly in the context of COVID-19 populations.