ABSTRACT
Introduction: Despite the growing number of studies on COVID-19, interpreting the data and extracting meaning from these data is challenging. Method(s): We used a multivariate patient similarity network (PSN) approach to analyse ~85 immune (cellular and humoral) and ~70 clinical features in 250 prospectively recruited patients 4-8 weeks after a SARS-CoV-2 diagnostic PCR. Result(s): Our multivariate analysis identified a minimal immune signature (CD69+CD4+ T-lymphocytes, CTLA-4+ Tlymphocytes and immature B-lymphocytes) that was strongly associated with disease severity and manifested even 4-8 weeks after COVID-19. Visualisation of clinical and laboratory parameters in patient clusters detected by PSN showed that a history of severe COVID-19 was associated with less frequent anosmia/ageusia and higher levels of IgM and IgG 4-8 weeks after COVID-19 compared with mild disease. Conclusion(s): Ours study reveals the relationship between immune profiles and severity of COVID-19, showing that more severe COVID-19 is typical lower proportion on immature B cells and cytotoxic T- lymphocytes. Our study demonstrates the benefits of implementing multivariate data-mining approaches for interpreting complex datasets, not only relating to COVID-19.