Dataset Information

Cytokine ranking via mutual information algorithm correlates cytokine profiles with presenting disease severity in patients with COVID-19.

ABSTRACT: Although the range of immune responses to COVID-19 infection is variable, cytokine storm is observed in many affected individuals. To further understand the disease pathogenesis and, consequently, to develop an additional tool for clinicians to evaluate patients for presumptive intervention we sought to compare plasma cytokine levels between a range of donor and patient samples grouped by a COVID-19 Severity Score (CSS) based on need for hospitalization and oxygen requirement. Here we utilize a mutual information algorithm that classifies the information gain for CSS prediction provided by cytokine expression levels and clinical variables. Using this methodology, we found that a small number of clinical and cytokine expression variables are predictive of presenting COVID-19 disease severity, raising questions about the mechanism by which COVID-19 creates severe illness. The variables that were the most predictive of CSS included clinical variables such as age and abnormal chest x-ray as well as cytokines such as macrophage colony-stimulating factor (M-CSF), interferon-inducible protein 10 (IP-10) and Interleukin-1 Receptor Antagonist (IL-1RA). Our results suggest that SARS-CoV-2 infection causes a plethora of changes in cytokine profiles and that particularly in severely ill patients, these changes are consistent with the presence of Macrophage Activation Syndrome and could furthermore be used as a biomarker to predict disease severity.

SUBMITTER: Huntington KE

PROVIDER: S-EPMC7709181 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cytokine ranking via mutual information algorithm correlates cytokine profiles with presenting disease severity in patients with COVID-19.

Huntington Kelsey E KE Louie Anna D AD Lee Chun Geun CG Elias Jack A JA Ross Eric A EA El-Deiry Wafik S WS

medRxiv : the preprint server for health sciences 20201127

Although the range of immune responses to COVID-19 infection is variable, cytokine storm is observed in many affected individuals. To further understand the disease pathogenesis and, consequently, to develop an additional tool for clinicians to evaluate patients for presumptive intervention we sought to compare plasma cytokine levels between a range of donor and patient samples grouped by a COVID-19 Severity Score (CSS) based on need for hospitalization and oxygen requirement. Here we utilize a ...[more]

PMID: 33269361

Similar Datasets

Project description:BackgroundMarkedly elevated levels of proinflammatory cytokines and defective type-I interferon responses were reported in patients with coronavirus disease 2019 (COVID-19).ObjectiveWe sought to determine whether particular cytokine profiles are associated with COVID-19 severity and mortality.MethodsCytokine concentrations and severe acute respiratory syndrome coronavirus 2 antigen were measured at hospital admission in serum of symptomatic patients with COVID-19 (N = 115), classified at hospitalization into 3 respiratory severity groups: no need for mechanical ventilatory support (No-MVS), intermediate severity requiring mechanical ventilatory support (MVS), and critical severity requiring extracorporeal membrane oxygenation (ECMO). Principal-component analysis was used to characterize cytokine profiles associated with severity and mortality. The results were thereafter confirmed in an independent validation cohort (N = 86).ResultsAt time of hospitalization, ECMO patients presented a dominant proinflammatory response with elevated levels of TNF-α, IL-6, IL-8, and IL-10. In contrast, an elevated type-I interferon response involving IFN-α and IFN-β was characteristic of No-MVS patients, whereas MVS patients exhibited both profiles. Mortality at 1 month was associated with higher levels of proinflammatory cytokines in ECMO patients, higher levels of type-I interferons in No-MVS patients, and their combination in MVS patients, resulting in a combined mortality prediction accuracy of 88.5% (risk ratio, 24.3; P < .0001). Severe acute respiratory syndrome coronavirus 2 antigen levels correlated with type-I interferon levels and were associated with mortality, but not with proinflammatory response or severity.ConclusionsDistinct cytokine profiles are observed in association with COVID-19 severity and are differentially predictive of mortality according to oxygen support modalities. These results warrant personalized treatment of COVID-19 patients based on cytokine profiling.

Dataset Information

Cytokine ranking via mutual information algorithm correlates cytokine profiles with presenting disease severity in patients with COVID-19.

Publications

Cytokine ranking via mutual information algorithm correlates cytokine profiles with presenting disease severity in patients with COVID-19.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets