Bayesian Modeling of the Impact of HBOT on the Reduction in Cytokine Storms

Since the initial identification of SARS-CoV-2 infections, numerous clinical challenges have arisen, revealing both acute and long-term effects associated with COVID-19. These effects impact various systems within the body, including the respiratory, cardiovascular, and nervous systems. Background/Objectives: This study aimed to investigate the immunological and inflammatory parameters in patients with severe COVID-19 and evaluate the effects of hyperbaric oxygen therapy (HBOT) on these parameters. Methods: This study enrolled thirty patients from the Military Medical Institute-National Research Institute in Warsaw, who were hospitalized for SARS-CoV-2 infection. Patients were screened for eligibility based on pre-defined inclusion criteria. The subjects were randomly assigned to one of two groups: hyperbaric oxygen therapy (HBOT) or a control group. Immune profiling was performed, measuring cytokine concentrations and leukocyte subpopulations in serum samples. Outcomes were assessed using Bayesian modeling. Results: Bayesian regression analysis confirmed previous findings, indicating that HBOT may reduce inflammatory cytokine levels while improving oxygen saturation (SpO₂) in patients with moderate and severe COVID-19. Moreover, the analysis suggested a higher probability of HBOT success in modulating the immune response and reducing inflammatory parameters, particularly in T lymphocyte subpopulations. Conclusions: Hyperbaric oxygen therapy (HBOT) may serve as an effective adjunctive treatment for patients with COVID-19 by enhancing oxygen saturation and modulating the immune response. Further studies are needed to elucidate the underlying mechanisms of HBOT on inflammatory and immunological parameters in COVID-19 patients.