Prognostic significance of cytokine dysregulation in critically ill COVID-19 patients

Abstract

Background

Understanding the immunopathogenesis of COVID-19 has yielded valuable insights into predicting adverse outcomes—particularly mortality. However, significant gaps persist in our comprehension of the complex interplay among the proposed pathophysiological mechanisms. Here, we aim to investigate the immunological factors associated with mortality in critically ill, unvaccinated COVID-19 patients admitted to the intensive care unit (ICU).

Methods

We conducted a single-center, prospective study involving 56 unvaccinated COVID-19 patients admitted to the ICU. Plasma cytokine levels at admission were quantified using enzyme-linked immunosorbent assay (ELISA). Continuous variables were presented as median (IQR), and categorical variables as frequencies and percentages. Non-parametric tests assessed group differences. Logistic regression and receiver operating characteristic (ROC) curve analyses identified predictors of mortality, with bootstrapping (1000 re-samplings; 95 % BCa CI) applied for model validation.

Results

Deceased patients exhibited significantly higher levels of interleukin (IL)-1β, IL-2, IL-6, transforming growth factor (TGF)-β, and interferon (IFN)-γ compared to survivors. Conversely, IL-10 and IL-27 were associated with favorable outcomes. Logistic regression modeling identified elevated IL-2 and IFN-γ levels as significant predictors of mortality. Notably, individual ROC curve analyses demonstrated that IL-1β and TGF-β had excellent discriminatory ability for mortality, while IFN-γ, IL-2, and IL-27 showed very good to excellent discriminatory capacity.

Conclusion

Our results indicate that distinct cytokine profiles differentiate survivors from non-survivors in critically ill, unvaccinated COVID-19 patients. These findings highlight the importance of cytokine dysregulation in severe COVID-19 cases and suggest potential targets for prognostic approaches. Further research is warranted to validate these results and translate them into effective clinical management strategies.