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05.10.2020 | COVID-19 | Research | Ausgabe 1/2020 Open Access

Respiratory Research 1/2020

Inflammatory phenotyping predicts clinical outcome in COVID-19

Zeitschrift:
Respiratory Research > Ausgabe 1/2020
Autoren:
H. Burke, A. Freeman, D. C. Cellura, B. L. Stuart, N. J. Brendish, S. Poole, F. Borca, H. T. T. Phan, N. Sheard, S. Williams, C. M. Spalluto, K. J. Staples, T. W. Clark, T. M. A. Wilkinson, on behalf of the REACT COVID investigators
Wichtige Hinweise
H. Burke, A. Freeman, T. W. Clark and T. M. A. Wilkinson contributed equally to this work.

Supplementary information

Supplementary information accompanies this paper at https://​doi.​org/​10.​1186/​s12931-020-01511-z.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Background

The COVID-19 pandemic has led to more than 760,000 deaths worldwide (correct as of 16th August 2020). Studies suggest a hyperinflammatory response is a major cause of disease severity and death. Identitfying COVID-19 patients with hyperinflammation may identify subgroups who could benefit from targeted immunomodulatory treatments. Analysis of cytokine levels at the point of diagnosis of SARS-CoV-2 infection can identify patients at risk of deterioration.

Methods

We used a multiplex cytokine assay to measure serum IL-6, IL-8, TNF, IL-1β, GM-CSF, IL-10, IL-33 and IFN-γ in 100 hospitalised patients with confirmed COVID-19 at admission to University Hospital Southampton (UK). Demographic, clinical and outcome data were collected for analysis.

Results

Age > 70 years was the strongest predictor of death (OR 28, 95% CI 5.94, 139.45). IL-6, IL-8, TNF, IL-1β and IL-33 were significantly associated with adverse outcome. Clinical parameters were predictive of poor outcome (AUROC 0.71), addition of a combined cytokine panel significantly improved the predictability (AUROC 0.85). In those ≤70 years, IL-33 and TNF were predictive of poor outcome (AUROC 0.83 and 0.84), addition of a combined cytokine panel demonstrated greater predictability of poor outcome than clinical parameters alone (AUROC 0.92 vs 0.77).

Conclusions

A combined cytokine panel improves the accuracy of the predictive value for adverse outcome beyond standard clinical data alone. Identification of specific cytokines may help to stratify patients towards trials of specific immunomodulatory treatments to improve outcomes in COVID-19.
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