The University of Southampton
University of Southampton Institutional Repository

Inflammatory phenotyping predicts clinical outcome in COVID-19

Inflammatory phenotyping predicts clinical outcome in COVID-19
Inflammatory phenotyping predicts clinical outcome in COVID-19

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.

Age Factors, Analysis of Variance, Area Under Curve, COVID-19, COVID-19 Testing, Clinical Laboratory Techniques/methods, Cohort Studies, Coronavirus Infections/blood, Cytokines/analysis, Female, Hospital Mortality, Hospitalization/statistics & numerical data, Hospitals, University, Humans, Incidence, Inflammation Mediators/blood, Male, Pandemics/prevention & control, Phenotype, Pneumonia, Viral/blood, Predictive Value of Tests, ROC Curve, Retrospective Studies, Severity of Illness Index, Sex Factors, United Kingdom
1465-9921
245
Burke, H
a9bb9391-4704-4584-aeb7-e69fe0acbdb8
Freeman, A
3d83f907-e7ce-4649-a018-a7a31b19f934
Cellura, D C
e4cffc4c-0e12-40e7-ad13-e90e3fb55332
Stuart, B L
626862fc-892b-4f6d-9cbb-7a8d7172b209
Brendish, N J
a8a4189e-01eb-4ab3-933e-a24cd188a4d7
Poole, S
440d7904-ab72-469c-892b-c910cd1cb19b
Borca, F
31fc3965-6bcf-4fd6-85bc-8b0f99f62473
Phan, H T T
2811b94c-62b7-459d-9cc1-c88057008e3b
Sheard, N
02303085-17a5-43dc-9ae8-c76b97bd86b1
Williams, S
2259988e-3bd9-4481-9053-46494a9f6874
Spalluto, C M
6802ad50-bc38-404f-9a19-40916425183b
Staples, K J
e0e9d80f-0aed-435f-bd75-0c8818491fee
Clark, T W
f6d288f1-2911-454a-8b0a-3f07a3b08ec0
Wilkinson, T M A
8c55ebbb-e547-445c-95a1-c8bed02dd652
REACT COVID Investigators
Burke, H
a9bb9391-4704-4584-aeb7-e69fe0acbdb8
Freeman, A
3d83f907-e7ce-4649-a018-a7a31b19f934
Cellura, D C
e4cffc4c-0e12-40e7-ad13-e90e3fb55332
Stuart, B L
626862fc-892b-4f6d-9cbb-7a8d7172b209
Brendish, N J
a8a4189e-01eb-4ab3-933e-a24cd188a4d7
Poole, S
440d7904-ab72-469c-892b-c910cd1cb19b
Borca, F
31fc3965-6bcf-4fd6-85bc-8b0f99f62473
Phan, H T T
2811b94c-62b7-459d-9cc1-c88057008e3b
Sheard, N
02303085-17a5-43dc-9ae8-c76b97bd86b1
Williams, S
2259988e-3bd9-4481-9053-46494a9f6874
Spalluto, C M
6802ad50-bc38-404f-9a19-40916425183b
Staples, K J
e0e9d80f-0aed-435f-bd75-0c8818491fee
Clark, T W
f6d288f1-2911-454a-8b0a-3f07a3b08ec0
Wilkinson, T M A
8c55ebbb-e547-445c-95a1-c8bed02dd652

REACT COVID Investigators (2020) Inflammatory phenotyping predicts clinical outcome in COVID-19. Respiratory Research, 21 (1), 245, [245]. (doi:10.1186/s12931-020-01511-z).

Record type: Article

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.

Text
Ella manuscript Resp Med R 10th Sept - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (118kB)

More information

Accepted/In Press date: 14 September 2020
Published date: 22 September 2020
Additional Information: Copyright © 2020, The Author(s)
Keywords: Age Factors, Analysis of Variance, Area Under Curve, COVID-19, COVID-19 Testing, Clinical Laboratory Techniques/methods, Cohort Studies, Coronavirus Infections/blood, Cytokines/analysis, Female, Hospital Mortality, Hospitalization/statistics & numerical data, Hospitals, University, Humans, Incidence, Inflammation Mediators/blood, Male, Pandemics/prevention & control, Phenotype, Pneumonia, Viral/blood, Predictive Value of Tests, ROC Curve, Retrospective Studies, Severity of Illness Index, Sex Factors, United Kingdom

Identifiers

Local EPrints ID: 443925
URI: http://eprints.soton.ac.uk/id/eprint/443925
ISSN: 1465-9921
PURE UUID: c8634eab-2ff9-44fb-a676-0b02fc8852bf
ORCID for B L Stuart: ORCID iD orcid.org/0000-0001-5432-7437
ORCID for N J Brendish: ORCID iD orcid.org/0000-0002-9589-4937
ORCID for K J Staples: ORCID iD orcid.org/0000-0003-3844-6457

Catalogue record

Date deposited: 16 Sep 2020 16:40
Last modified: 29 Jul 2022 04:03

Export record

Altmetrics

Contributors

Author: H Burke
Author: A Freeman
Author: D C Cellura
Author: B L Stuart ORCID iD
Author: N J Brendish ORCID iD
Author: S Poole
Author: F Borca
Author: H T T Phan
Author: N Sheard
Author: S Williams
Author: C M Spalluto
Author: K J Staples ORCID iD
Author: T W Clark
Author: T M A Wilkinson
Corporate Author: REACT COVID Investigators

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×