The University of Southampton
University of Southampton Institutional Repository

Point-of-care inflammatory phenotyping predicts clinical outcome in COVID-19

Point-of-care inflammatory phenotyping predicts clinical outcome in COVID-19
Point-of-care inflammatory phenotyping predicts clinical outcome in COVID-19
Rationale: The COVID-19 pandemic has led to more than 445,000 deaths worldwide. There is an urgent need for effective treatment. Studies suggest that a hyper-inflammatory response is a major cause of disease severity and death. Using precision medicine to rapidly identify patients with COVID-19 with hyper-inflammation may be key for identify subgroups who may benefit from targeted immunomodulatory treatments.

Methods: In combination with a diagnostic point-of-care test (POCT), we used a rapid multiplex cytokine assay to measure serum IL-6, IL-8, TNF-α, IL-1β, GM-CSF, IL-10, IL-33 and IFN-γ in 101 hospitalised patients with confirmed COVID-19 at admission to University Hospital Southampton. Demographic, clinical, laboratory and outcome data were collected for all patients.

Findings: Age over 70 years was the strongest predictor of death (OR 28, 95% CI 5.94, 139.45). Cytokines IL-6, IL-8, TNF-α, IL-1β and IL-33 were significantly associated with adverse outcome in COVID-19. Clinical parameters at admission were predictive of poor outcome (AUROC 0.71), with addition of a combined cytokine panel significantly improving the predictability (AUROC 0.85). In those < 70 years, IL-33 and TNF-α were predictive of poor outcome (AUROC 0.84 and 0.83), and addition of a combined cytokine panel demonstrated greater predictability of poor outcome than clinical parameters alone (AUROC 0.92 vs 0.77).

Interpretation: A combined cytokine panel improves the accuracy of the predictive value for adverse outcome beyond standard clinical data alone. Rapid identification of specific cytokines may help to stratify patients towards trials of specific immunomodulatory treatments to improve outcomes in COVID-19.
Burke, H.
4c1f67c5-5087-429b-98d1-45dfe0e0adb2
Freeman, A.
b5f45a0d-f9e4-4a91-9af0-40efb6730787
Cellura, D.C.
5edb82b2-f50c-4c76-bd10-8bf687a91b4d
Stuart, B.L.
a51c80d3-5855-4672-b24f-8c65fd2e1444
Brendish, N.J.
d07b588f-4919-456d-8604-04efde7711fd
Poole, S.
440d7904-ab72-469c-892b-c910cd1cb19b
Borca, F.
118f85ef-3a02-47f5-9e2a-a7128f72bd1e
Sheard, N.
02303085-17a5-43dc-9ae8-c76b97bd86b1
Williams, S.
f67a3e2b-3287-4040-9ea9-499e0b269fd3
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.
8c55ebbb-e547-445c-95a1-c8bed02dd652
Burke, H.
4c1f67c5-5087-429b-98d1-45dfe0e0adb2
Freeman, A.
b5f45a0d-f9e4-4a91-9af0-40efb6730787
Cellura, D.C.
5edb82b2-f50c-4c76-bd10-8bf687a91b4d
Stuart, B.L.
a51c80d3-5855-4672-b24f-8c65fd2e1444
Brendish, N.J.
d07b588f-4919-456d-8604-04efde7711fd
Poole, S.
440d7904-ab72-469c-892b-c910cd1cb19b
Borca, F.
118f85ef-3a02-47f5-9e2a-a7128f72bd1e
Sheard, N.
02303085-17a5-43dc-9ae8-c76b97bd86b1
Williams, S.
f67a3e2b-3287-4040-9ea9-499e0b269fd3
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.
8c55ebbb-e547-445c-95a1-c8bed02dd652

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

Rationale: The COVID-19 pandemic has led to more than 445,000 deaths worldwide. There is an urgent need for effective treatment. Studies suggest that a hyper-inflammatory response is a major cause of disease severity and death. Using precision medicine to rapidly identify patients with COVID-19 with hyper-inflammation may be key for identify subgroups who may benefit from targeted immunomodulatory treatments.

Methods: In combination with a diagnostic point-of-care test (POCT), we used a rapid multiplex cytokine assay to measure serum IL-6, IL-8, TNF-α, IL-1β, GM-CSF, IL-10, IL-33 and IFN-γ in 101 hospitalised patients with confirmed COVID-19 at admission to University Hospital Southampton. Demographic, clinical, laboratory and outcome data were collected for all patients.

Findings: Age over 70 years was the strongest predictor of death (OR 28, 95% CI 5.94, 139.45). Cytokines IL-6, IL-8, TNF-α, IL-1β and IL-33 were significantly associated with adverse outcome in COVID-19. Clinical parameters at admission were predictive of poor outcome (AUROC 0.71), with addition of a combined cytokine panel significantly improving the predictability (AUROC 0.85). In those < 70 years, IL-33 and TNF-α were predictive of poor outcome (AUROC 0.84 and 0.83), and addition of a combined cytokine panel demonstrated greater predictability of poor outcome than clinical parameters alone (AUROC 0.92 vs 0.77).

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

This record has no associated files available for download.

More information

Published date: 30 September 2020

Identifiers

Local EPrints ID: 479389
URI: http://eprints.soton.ac.uk/id/eprint/479389
PURE UUID: bf01208e-817c-4a02-b5f2-90c5989a4cb8
ORCID for A. Freeman: ORCID iD orcid.org/0000-0003-3495-2520
ORCID for N.J. Brendish: ORCID iD orcid.org/0000-0002-9589-4937
ORCID for C.M. Spalluto: ORCID iD orcid.org/0000-0001-7273-0844
ORCID for K.J. Staples: ORCID iD orcid.org/0000-0003-3844-6457

Catalogue record

Date deposited: 20 Jul 2023 17:41
Last modified: 01 Oct 2024 02:04

Export record

Altmetrics

Contributors

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

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.

×