Full publication: https://journals.lww.com/ccejournal/Fulltext/2022/12000/Validating_a_Proteomic_Signature_of_Severe.6.aspx
Background: COVID-19 is a heterogenous disease. Biomarker based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies.
Methods: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19. We classified patients as non-severe versus severe COVID-19 at study entry and in 7-day intervals thereafter defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the signature modified the effect of corticosteroids on estimated mortality risk.
Results: We identified 194 proteins associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 28-day severity in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI 0.88, 0.95). Classifying patients based on the predicted risk from the signature identified a heterogenous response to treatment with corticosteroids (p=0.006).
Conclusions: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.