Toggle Main Menu Toggle Search

Open Access padlockePrints

The Newcastle University research output collection, currently available on ePrints, will shortly be moving to a new open repository platform, Figshare. To prepare for the data migration we have paused adding new content to ePrints, and will resume once the new repository is launched. During this time you will continue to have access to ePrints (but no new content will appear). We will share updates here when available.

Simple hyperinflammation scores predict mortality in hospitalized patients with COVID-19 and offer a personalized medicine approach to dexamethasone intervention

Lookup NU author(s): Trevor Liddle, Professor Matthew CollinORCiD

Downloads


Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2025 The Authors.Background Dexamethasone is recommended for use in all patients with COVID-19 requiring supplemental oxygen, however, only some patients develop hyperinflammation (COV-HI) potentially influencing their response to corticosteroids. This study tested the ability of criteria defining COV-HI to predict response to dexamethasone. Methods A retrospective, multicentre, observational cohort study of 1313-patients with PCR-confirmed COVID-19 during first and second waves of community-acquired infection including 212 patients who received dexamethasone monotherapy. Demographic data, laboratory tests and clinical status were recorded from admission until death or discharge, with minimum 28-days follow-up. Patients were stratified at admission as COV-HI-YES/COV-HI-NO based on three published COV-HI definitions. Results Patients with COV-HI shared a biological phenotype of hypoalbuminemia/anemia, and elevated D -dimer/lactate dehydrogenase/alanine transaminase/respiratory rates. Combining these features predicted 28-day mortality and stratified COV-HI-YES from COV-HI-NO more effectively compared to individual markers/demographic features alone. In COV-HI-YES patients, dexamethasone treatment halved mortality-risk (relative risk = 0.50) compared to untreated patients. However, in COV-HI-NO patients mortality-risk was 3.03x higher (CI = 1.3-7.0) in treated versus untreated patients during a 28-day admission period. Conclusions We present a framework for a new machine-learning based scoring system for COV-HI combining clinical assessment with laboratory markers for prediction of mortality and targeting glucocorticoids in hospitalized COVID-19 patients.


Publication metadata

Author(s): Oppong AE, Coelewij L, Hutchinson M, Carpenter B, Robinson GA, Liddle T, Hawkins E, Cox MF, Ciurtin C, Venkatachalam S, Collin M, Tattersall RS, Ardern-Jones M, Duncombe AS, Jury EC, Manson JJ

Publication type: Article

Publication status: Published

Journal: International Journal of Infectious Diseases

Year: 2025

Volume: 161

Print publication date: 05/11/2025

Online publication date: 13/10/2025

Acceptance date: 09/10/2025

Date deposited: 27/11/2025

ISSN (print): 1201-9712

ISSN (electronic): 1878-3511

Publisher: Elsevier

URL: https://doi.org/10.1016/j.ijid.2025.108119

DOI: 10.1016/j.ijid.2025.108119

PubMed id: 41093003


Altmetrics

Altmetrics provided by Altmetric


Funding

Funder referenceFunder name
Biomedical Research Centre grant (BRC815/HI/JM/101,440)
BRC grant (BRC4/III/CC)
MR/N013867/1

Share