1. Inflammatory phenotyping predicts clinical outcome in COVID-19.
- Author
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Burke H, Freeman A, Cellura DC, Stuart BL, Brendish NJ, Poole S, Borca F, Phan HTT, Sheard N, Williams S, Spalluto CM, Staples KJ, Clark TW, and Wilkinson TMA
- Subjects
- Age Factors, Analysis of Variance, Area Under Curve, COVID-19, COVID-19 Testing, Clinical Laboratory Techniques methods, Cohort Studies, Coronavirus Infections diagnosis, Coronavirus Infections physiopathology, Female, Hospitalization statistics & numerical data, Hospitals, University, Humans, Incidence, Male, Pandemics prevention & control, Phenotype, Pneumonia, Viral physiopathology, Predictive Value of Tests, ROC Curve, Retrospective Studies, Severity of Illness Index, Sex Factors, United Kingdom, Coronavirus Infections blood, Coronavirus Infections epidemiology, Cytokines analysis, Hospital Mortality, Inflammation Mediators blood, Pandemics statistics & numerical data, Pneumonia, Viral blood, Pneumonia, Viral epidemiology
- 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.
- Published
- 2020
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