Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
Yvonne M. Mueller, Thijs J. Schrama, Rik Ruijten, Marco W.J. Schreurs, Dwin G.B. Grashof, Harmen J.G. van de Werken, Giovanna Jona Lasinio, Daniel Álvarez‐Sierra, Caoimhe H. Kiernan, Melisa D. Castro Eiro, Marjan van Meurs, Inge Brouwers-Haspels, Manzhi Zhao, Ling Li, Harm de Wit, Christos Ouzounis, Merel E. P. Wilmsen, Tessa M. Alofs, Danique A. Laport, Tamara van Wees, Geoffrey Kraker, María C. Jaimes, Sebastiaan Van Bockstael, M. Hernández, Casper Rokx, Bart Rijnders, Ricardo Pujol‐Borrell, Peter D. Katsikis
Abstract
Quantitative or qualitative differences in immunity may drive clinical severity in COVID-19. Although longitudinal studies to record the course of immunological changes are ample, they do not necessarily predict clinical progression at the time of hospital admission. Here we show, by a machine learning approach using serum pro-inflammatory, anti-inflammatory and anti-viral cytokine and anti-SARS-CoV-2 antibody measurements as input data, that COVID-19 patients cluster into three distinct immune phenotype groups. These immune-types, determined by unsupervised hierarchical clustering that is agnostic to severity, predict clinical course. The identified immune-types do not associate with disease duration at hospital admittance, but rather reflect variations in the nature and kinetics of individual patient's immune response. Thus, our work provides an immune-type based scheme to stratify COVID-19 patients at hospital admittance into high and low risk clinical categories with distinct cytokine and antibody profiles that may guide personalized therapy.