Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
Mueller, Yvonne M. (Erasmus University Medical Center)
Schrama, Thijs J. (Erasmus University Medical Center)
Ruijten, Rik (Erasmus University Medical Center)
Schreurs, Marco W. J. (Erasmus University Medical Center)
Grashof, Dwin G. B. (Erasmus University Medical Center)
van de Werken, Harmen J. G. (Erasmus University Medical Center)
Lasinio, Giovanna Jona (University of Rome "La Sapienza")
Álvarez-Sierra, Daniel (Hospital Universitari Vall d'Hebron)
Kiernan, Caoimhe H. (Erasmus University Medical Center)
Castro Eiro, Melisa D. (Erasmus University Medical Center)
van Meurs, Marjan (Erasmus University Medical Center)
Brouwers-Haspels, Inge (Erasmus University Medical Center)
Zhao, Manzhi (Erasmus University Medical Center)
Li, Ling (Erasmus University Medical Center)
de Wit, Harm (Erasmus University Medical Center)
Ouzounis, Christos A. (Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thermi)
Wilmsen, Merel E. P. (Erasmus University Medical Center)
Alofs, Tessa M. (Erasmus University Medical Center)
Laport, Danique A. (Erasmus University Medical Center)
van Wees, Tamara (Erasmus University Medical Center)
Kraker, Geoffrey (Cytek Biosciences, Fremont, CA USA)
Jaimes, Maria C. (Cytek Biosciences, Fremont, CA USA)
Van Bockstael, Sebastiaan (Cytek Biosciences, Fremont, CA USA)
Hernández-González, Manuel (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Rokx, Casper (Erasmus University Medical Center)
Rijnders, Bart J. A. (Erasmus University Medical Center)
Pujol-Borrell, Ricardo (Vall d'Hebron Institut d'Oncologia)
Katsikis, Peter D. (Erasmus University Medical Center)
Universitat Autònoma de Barcelona

Data:	2022
Resum:	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. Developing predictive methods to identify patients with high risk of severe COVID-19 disease is of crucial importance. Authors show here that by measuring anti-SARS-CoV-2 antibody and cytokine levels at the time of hospital admission and integrating the data by unsupervised hierarchical clustering/machine learning, it is possible to predict unfavourable outcome.
Ajuts:	European Commission. Horizon 2020 779295 Instituto de Salud Carlos III PI20/00416
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Viral infection ; Applied immunology ; Computer modelling ; Predictive markers
Publicat a:	Nature communications, Vol. 13 (february 2022) , ISSN 2041-1723

DOI: 10.1038/s41467-022-28621-0
PMID: 35177626

13 p, 7.0 MB

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