Biomarker Levels Concur with a Risk Stratification Criteria in Covid-19 Pneumonia, a Role for a Prognostic-Tool for Identification of Clinical Deteriorators

ABSTRACT

Rationale COVID-19 patients present with a number of clinical symptoms ranging from mild, moderate to severe, while only a subgroup of patients, who requires high-dependency critical care resources, accounts for most of the COVID-19 associated health care expenditure and death. A reliable prognostic tool is therefore required to identify patients at risk of developing severe COVID-19 pneumonia. To address this unmet need, we tested a wide range of potentially important peripheral blood biomarkers in a group of clinically risk-stratified COVID-19 patients in order to identify most relevant candidate biomarker(s) predictive of disease progression. Methods: Patients and healthy controls recruited to this study are summarised in Figure 1. Biomarkers levels were analysed using ANOVA across the severity groups. Spearman-correlation coefficients against pairs of average levels from each biomarker within severity-group and healthy controls were assembled into a 76x76 matrix and agglomerative hierarchical clustering was applied to generate the final heatmaps. Linear-discriminant analysis (LDA) was carried out on a reduced optimised set of biomarkers to explore the boundaries between the clinical severity groups.Results: Degree of lymphopaenia, neutrophil levels, TNF-α, INR-levels, and pro-inflammatory cytokines;IL6, IL8, CXCL9 and D-dimers were significantly increased in COVD-19 patients compared to healthy controls (p<0.05, 95% C.I.). C3a and C5 was significantly elevated in all categories of severity compared to healthy controls (p<0.05), C5a levels were significantly different between “moderate” and “severe” categories (p<0.01). sC5b-9 was significantly elevated in the “moderate” and “severe” category of patients compared to healthy controls (p<0.001).Heatmap analysis demonstrated distinct visual differences of biomarker profiles between the clinical severity groups. LDA on the deteriorators, non-deteriorators and healthy volunteers as a combined function of the predictor variables C3, eosinophil-counts, granulocyte colony-stimulating factor (G-CSF), fractalkine, IL10, IL27, LTB4, lymphocyte count, MIG/CXCL9, M-CSF, platelet count and sC5b-9 showed clear separation between the groups based on biomarker/blood-count levels.Conclusions: Diagnostic and clinical assessments followed by robust statistical and machine learning approaches could identify peripheral blood biomarkers for prognostic stratification of patients in COVID-19. Our results would be helpful for clinicians and supports the use of point of care devices that can quantify multiple analytes. (Lui G, et al., Pointof- care detection of cytokines in cytokine storm management and beyond Significance and challenges. VIEW. 2021;2 1-20.). Such would allow for more efficient management and resource allocation. 1 (Figure Presented).