Although the range of immune responses to COVID-19 infection is variable, cytokine storm is observed in many affected individuals. Here we utilize a mutual information algorithm that classifies the information gain for COVID-19 Severity Score (CSS) prediction provided by cytokine expression levels and clinical variables. We found a small number of clinical and cytokine expression variables are predictive of presenting COVID-19 disease severity, raising questions about the mechanism of COVID-19 severe illness. Variables that were most predictive of CSS included clinical variables such as age, abnormal chest x-ray, and cytokines such as macrophage colony-stimulating factor (M-CSF), interferon-inducible protein 10 (IP-10) and Interleukin-1 Receptor Antagonist (IL-1RA). Our results suggest that SARS-CoV-2 infection causes a plethora of changes in cytokine profiles and that particularly in severely ill patients, these changes are consistent with the presence of Macrophage Activation Syndrome and could furthermore be used as a biomarker to predict disease severity.
Collection : COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv
Disease X-19 Medical Review from Michael_Novakhov (2 sites)