Predictors of Unfavorable Outcomes in COVID-19-Related Sepsis: A Prospective Cohort Study
Year: 2025
Type: article
Study design: Observational Study
Abstract: Sepsis is a leading cause of mortality in critically ill patients, arising from a dysregulated immune response to infection. While traditionally associated with bacterial pathogens, severe COVID-19 can induce a sepsis-like syndrome, characterized by systemic inflammation, endothelial dysfunction, and coagulation abnormalities. This study aimed to assess the prognostic value of age, inflammatory markers, coagulation dysfunction, comorbidity burden, and lung involvement on computer tomography (CT) scans in predicting poor outcomes. We conducted a prospective cohort study including 163 patients diagnosed with COVID-19-related sepsis. Univariate and multivariable logistic regression analyses were performed to identify the independent predictors of unfavorable outcomes. Higher D-dimer (OR: 1.417, p = 0.020) and C-reactive protein (CRP) levels (OR: 1.010, p = 0.027) were independently associated with poor outcomes. A greater than 50% lung involvement on CT (OR: 1.774, p = 0.025) was also a significant predictor. The Charleson Comorbidity Index (CCI) showed a strong trend toward significance (p = 0.065), while age lost statistical significance after adjusting for comorbidities. Our findings suggest that D-dimers, CRP, and lung involvement on CT are key independent predictors of poor outcomes in COVID-19-related sepsis. These results emphasize the importance of inflammatory and coagulation markers, alongside comorbidity burden, in early risk assessment. Further prospective studies are warranted to refine predictive models for severe COVID-19 cases complicated by sepsis.
Source: Viruses
DOI: 10.3390/v17040455
Authors: Diana-Maria Mateescu, Ioana Cotet, Cristina Guse, Catalin Prodan-Barbulescu, Norberth-Istvan Varga, Stela Iurciuc, Maria-Laura Craciun, Adrian-Cosmin Ilie, Alexandra Enache
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4408791663
