A-229 Immune Biomarkers Associated with COVID-19 Disease Severity in an Urban, Hospitalized Population

Abstract

Abstract Background We sought to identify immune biomarkers associated with severe Coronavirus disease 2019 (COVID-19) in patients admitted to a large public county hospital during the early phase of the SARS-CoV-2 pandemic. We hypothesized that we could identify clinically relevant immune markers at the time of initial hospital admission that could be used to predict the course of COVID-19 illness. Methods The study population consisted of SARS-CoV-2 positive patients admitted for COVID-19 (n = 58) or controls (n = 14) at the Los Angeles County University of Southern California Medical Center between April-December 2020. Immunologic markers including chemokine/cytokines (IL-6, IL-8, IL-10, IP-10, MCP-1, TNFα) and serologic markers against SARS-CoV-2 antigens (including spike subunits S1 and S2, receptor binding domain (RBD), and nucleocapsid (N)) were assessed in serum collected on the day of admission using custom MILLIPLEX® immunoassay panels. Result values were computed using mean fluorescent intensity in individual samples fit to a standard curve using a 5PL logistic formula with power law variance. Comparison of patient demographic, clinical, cytokines and immunoglobulins characteristics between mild vs moderate/severe COVID-19 groups were conducted using Wilcoxon tests for continuous variables and Chi-square tests for categorical variables. Linear support vector machine models were fitted to perform the binary classification task of predicting mild vs moderate/severe COVID-19 using the python library scikit-learn. Results SARS-CoV-2 antibody levels were significantly elevated in patients with the highest COVID-19 disease severity, with IgM S1, IgG N, IgG RBD, IgG S1, and IgG S2 showing statistical significance between mild vs moderate/severe disease group medians (P = 0.037, 0.032, 0.007, 0.003, and 0.015, respectively). Of the chemokines/cytokines tested, only IP-10 showed significance across the disease groups (medians 640.8 pg/mL in mild, 493.3 pg/mL in moderate/severe, and 259.9 pg/mL in control, overall P = 0.005). The linear support vector machine model achieved an accuracy of 64% and an AUROC of 0.81 in predicting COVID-19 severity status. The most important clinical variables for predicting disease severity were white blood cell count, diastolic blood pressure, and platelet count, while the most important serologic markers were IgG anti-SARS-CoV-2 N, S1, S2, TNF-α, IP-10, and IL-10. Conclusion Our results suggest that IP-10 and anti-SARS-CoV-2 antibody measurements could be useful to identify patients most likely to experience the most severe forms of the disease. Strengths of this study include a focus on a racially and ethnically diverse patient population and a combined analysis of both cytokine/chemokine and immune response (antibody) biomarkers. However, we emphasize that our subjects were enrolled at a time before widespread vaccination against SARS-CoV-2.