Journal Article

Inflammatory phenotyping predicts clinical outcome in COVID-19

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

2020

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Nature Publishing Group,BMC
• Subject: Age Factors / Analysis of Variance / Area Under Curve / Clinical Laboratory Techniques - methods / Clinical outcomes / Clinical trials / Cohort Studies / Coronavirus Infections - blood / Coronavirus Infections - diagnosis / Coronavirus Infections - epidemiology / Coronavirus Infections - physiopathology / Coronaviruses / COVID-19 / COVID-19 Testing / Cytokines / Cytokines - analysis / Diabetes / Diagnosis / Disease / Female / Granulocyte-macrophage colony-stimulating factor / Health aspects / Hospital Mortality / Hospitalization - statistics & numerical data / Hospitals / Hospitals, University / Humans / Hypertension / IL-1β / IL-33 / Immunomodulation / Immunomodulators / Incidence / Infections / Inflammation / Inflammation Mediators - blood / Interleukin 10 / Interleukin 6 / Interleukin 8 / Laboratories / Male / Medicine / Medicine & Public Health / Mortality / Neutrophils / Pandemics / Pandemics - prevention & control / Pandemics - statistics & numerical data / Parameters / Patients / Phenotype / Phenotyping / Pneumology/Respiratory System / Pneumonia, Viral - blood / Pneumonia, Viral - epidemiology / Pneumonia, Viral - physiopathology / Point-of-care testing / Precision medicine / Predictive Value of Tests / Prognosis / Retrospective Studies / ROC Curve / Sample size / SARS-CoV-2 / Severe acute respiratory syndrome coronavirus 2 / Severity of Illness Index / Sex Factors / Subgroups / TNF-α / Tumor necrosis factor / Tumor necrosis factor-TNF / United Kingdom / Variables / Viral diseases / γ-Interferon