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Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning.
Mueller, Yvonne M; Schrama, Thijs J; Ruijten, Rik; Schreurs, Marco W J; Grashof, Dwin G B; van de Werken, Harmen J G; Lasinio, Giovanna Jona; Álvarez-Sierra, Daniel; Kiernan, Caoimhe H; Castro Eiro, Melisa D; van Meurs, Marjan; Brouwers-Haspels, Inge; Zhao, Manzhi; Li, Ling; de Wit, Harm; Ouzounis, Christos A; Wilmsen, Merel E P; Alofs, Tessa M; Laport, Danique A; van Wees, Tamara; Kraker, Geoffrey; Jaimes, Maria C; Van Bockstael, Sebastiaan; Hernández-González, Manuel; Rokx, Casper; Rijnders, Bart J A; Pujol-Borrell, Ricardo; Katsikis, Peter D.
  • Mueller YM; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Schrama TJ; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Ruijten R; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Schreurs MWJ; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Grashof DGB; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • van de Werken HJG; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Lasinio GJ; Cancer Computational Biology Center, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Álvarez-Sierra D; Department of Statistical Sciences, University of Rome "La Sapienza", Roma, Italy.
  • Kiernan CH; Immunology Division, Hospital Universitari Vall d'Hebron, Campus Vall d'Hebron, Barcelona, Spain.
  • Castro Eiro MD; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • van Meurs M; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Brouwers-Haspels I; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Zhao M; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Li L; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • de Wit H; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Ouzounis CA; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Wilmsen MEP; School of Informatics, Faculty of Sciences, Aristotle University of Thessaloniki, Thessalonica, Greece.
  • Alofs TM; Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thermi, Thessalonica, Greece.
  • Laport DA; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • van Wees T; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Kraker G; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Jaimes MC; Department of Immunology, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Van Bockstael S; Cytek Biosciences, Fremont, CA, USA.
  • Hernández-González M; Cytek Biosciences, Fremont, CA, USA.
  • Rokx C; Cytek Biosciences, Fremont, CA, USA.
  • Rijnders BJA; Immunology Division, Hospital Universitari Vall d'Hebron, Campus Vall d'Hebron, Barcelona, Spain.
  • Pujol-Borrell R; Cell Biology, Physiology and Immunology Department, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Katsikis PD; Translational Immunology Research Group, Vall d'Hebron Institut de Recerca (VHIR), Campus Vall d'Hebron, Barcelona, Spain.
Nat Commun ; 13(1): 915, 2022 02 17.
Article Dans Anglais | MEDLINE | ID: covidwho-1703249
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.
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Texte intégral: Disponible Collection: Bases de données internationales Base de données: MEDLINE Sujet Principal: Indice de gravité de la maladie / Cytokines / SARS-CoV-2 / COVID-19 / Anticorps antiviraux Type d'étude: Étude de cohorte / Études expérimentales / Étude observationnelle / Étude pronostique / Recherche qualitative / Essai contrôlé randomisé Limites du sujet: Adulte très âgé / Femelle / Humains / Mâle / Adulte d'âge moyen langue: Anglais Revue: Nat Commun Thème du journal: Biologie / Science Année: 2022 Type de document: Article Pays d'affiliation: S41467-022-28621-0

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Texte intégral: Disponible Collection: Bases de données internationales Base de données: MEDLINE Sujet Principal: Indice de gravité de la maladie / Cytokines / SARS-CoV-2 / COVID-19 / Anticorps antiviraux Type d'étude: Étude de cohorte / Études expérimentales / Étude observationnelle / Étude pronostique / Recherche qualitative / Essai contrôlé randomisé Limites du sujet: Adulte très âgé / Femelle / Humains / Mâle / Adulte d'âge moyen langue: Anglais Revue: Nat Commun Thème du journal: Biologie / Science Année: 2022 Type de document: Article Pays d'affiliation: S41467-022-28621-0