Immunophenotyping and machine learning identify distinct immunotypes that predict COVID-19 clinical severity

Mueller, Y. M.; Schrama, T. J.; Ruijten, R.; Schreurs, M. W. J.; Grashof, D. G. B.; van de Werken, H. J. G.; Alvarez de la Sierra, D.; Kiernan, C. H.; Castro Eiro, M. D.; van Meurs, M.; Brouwers-Haspels, I.; Zhao, M.; Li, L.; de Wit, H.; Ouzounis, C. A.; Wilmsen, M. E. P.; Alofs, T.; Laport, D. A.; van Wees, T.; Kraker, G.; Jaimes, M. C.; Van Bockstael, S.; Hernandez-Gonzalez, M.; Rokx, C.; Rijnders, B. J. A.; Pujol-Borrell, R.; Katsikis, P. D.

Quantitative or qualitative differences in immunity may drive and predict clinical severity in COVID-19. We therefore measured modules of serum pro-inflammatory, anti-inflammatory and anti-viral cytokines in combination with the anti-SARS-CoV-2 antibody response in COVID-19 patients admitted to tertiary care. Using machine learning and employing unsupervised hierarchical clustering, agnostic to severity, we identified three distinct immunotypes that were shown post-clustering to predict very different clinical courses such as clinical improvement or clinical deterioration. Immunotypes did not associate chronologically with disease duration but rather reflect variations in the nature and kinetics of individual patient's immune response. Here we demonstrate that immunophenotyping can stratify patients to high and low risk clinical subtypes, with distinct cytokine and antibody profiles, that can predict severity progression and guide personalized therapy.