Introduction. Mortality among critically ill COVID‐19 patients remains relatively high despite different potential therapeutic modalities being introduced recently. The treatment of critically ill patients is a challenging task, without identified credible predictors of mortality. Methods. We performed an analysis of 160 consecutive patients with confirmed COVID‐19 infection admitted to the Respiratory Intensive Care Unit between June 23, 2020, and October 2, 2020, in University Hospital Center Bezanijska kosa, Belgrade, Serbia. Patients on invasive, noninvasive ventilation and high flow oxygen therapy with moderate to severe ARDS, according to the Berlin definition of ARDS, were selected for the study. Demographic data, past medical history, laboratory values, and CT severity score were analyzed to identify predictors of mortality. Univariate and multivariate logistic regression models were used to assess potential predictors of mortality in critically ill COVID‐19 patients. Results. The mean patient age was 65.6 years (range, 29–92 years), predominantly men, 68.8%. 107 (66.9%) patients were on invasive mechanical ventilation, 31 (19.3%) on noninvasive, and 22 (13.8%) on high flow oxygen therapy machine. The median total number of ICU days was 10 (25^th to 75^th percentile: 6–18), while the median total number of hospital stay was 18 (25^th to 75^th percentile: 12–28). The mortality rate was 60% (96/160). Univariate logistic regression analysis confirmed the significance of age, CRP, and lymphocytes at admission to hospital, serum albumin, D‐dimer, and IL‐6 at admission to ICU, and CT score. Serum albumin, D‐dimer, and IL‐6 at admission to ICU were independently associated with mortality in the final multivariate analysis. Conclusion. In the present study of 160 consecutive critically ill COVID‐19 patients with moderate to severe ARDS, IL‐6, serum albumin, and D‐dimer at admission to ICU, accompanied by chest CT severity score, were marked as independent predictors of mortality.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma, characterized by significant variability in clinical outcomes. Emerging evidence highlights the pivotal role of inflammation in the pathogenesis and prognosis of DLBCL. This narrative review explores the interplay between the tumor microenvironment, inflammatory processes, and prognostic indices used in DLBCL, focusing on biomarkers, immune responses, and systemic inflammation. These indices show promise as predictive and prognostic tools comparable to molecular markers, such as gene expression profiling, which are currently considered gold standards in prognosis but are often costly and technically demanding. By synthesizing findings from the current literature, this article highlights the potential of inflammatory indices as accessible and cost-effective prognostic alternatives to molecular markers in DLBCL, while also underscoring the need for further research to validate their clinical utility.