ArticleCritical care explorations2022
Validating a Proteomic Signature of Severe COVID-19.
Article in Critical care explorations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Plasma Levels of Soluble ST2 Reflect Extrapulmonary Organ Dysfunction and Predict Outcomes in Acute Respiratory Failure.Critical care medicine · 2025Observational
- Longitudinal plasma proteomic analysis of 1117 hospitalized patients with COVID-19 identifies features associated with severity and outcomes.Science advances · 2024Article
- Vascular endothelial-derived SPARCL1 exacerbates viral pneumonia through pro-inflammatory macrophage activation.Nature communications · 2024Article
- Simple and Scalable Algorithms for Cluster-Aware Precision Medicine.Proceedings of machine learning research · 2024Article
- Plasma Proteomic Variables Related to COVID-19 Severity: An Untargeted nLC-MS/MS Investigation.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
25 authors at 2 institutions in 1 country.
Funding
Abstract
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. Our objective was to identify and validate a plasma protein signature for severe COVID-19.
designProspective observational cohort study.
settingTwo hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19.
interventionNone. MEASUREMENTS AND MAIN
resultsWe measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, 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 association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were 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 90-day mortality 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 heterogeneous response to treatment with corticosteroids (
conclusionsInpatients 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.
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