Primate Monocytes - CD14, CD16 - Ziegler-Heitbrock


Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning.


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.

Authors: Mueller YM, Schrama TJ, Ruijten R, Schreurs MWJ, Grashof DGB, van de Werken HJG, Lasinio GJ, Álvarez-Sierra D, Kiernan CH, Castro Eiro MD, van Meurs M, Brouwers-Haspels I, Zhao M, Li L, de Wit H, Ouzounis CA, Wilmsen MEP, Alofs TM, Laport DA, van Wees T,
Journal: Nat Commun;20220217; 13 (1) 915. doi:10.1038/s41467-022-28621-0
Year: 2022
PubMed: PMID: 35177626 (Go to PubMed)