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

Author

Listed:
  • Yvonne M. Mueller

    (Erasmus University Medical Center)

  • Thijs J. Schrama

    (Erasmus University Medical Center)

  • Rik Ruijten

    (Erasmus University Medical Center)

  • Marco W. J. Schreurs

    (Erasmus University Medical Center)

  • Dwin G. B. Grashof

    (Erasmus University Medical Center)

  • Harmen J. G. van de Werken

    (Erasmus University Medical Center
    Erasmus University Medical Center)

  • Giovanna Jona Lasinio

    (University of Rome “La Sapienza”)

  • Daniel Álvarez-Sierra

    (Hospital Universitari Vall d’Hebron, Campus Vall d’Hebron)

  • Caoimhe H. Kiernan

    (Erasmus University Medical Center)

  • Melisa D. Castro Eiro

    (Erasmus University Medical Center)

  • Marjan van Meurs

    (Erasmus University Medical Center)

  • Inge Brouwers-Haspels

    (Erasmus University Medical Center)

  • Manzhi Zhao

    (Erasmus University Medical Center)

  • Ling Li

    (Erasmus University Medical Center)

  • Harm de Wit

    (Erasmus University Medical Center)

  • Christos A. Ouzounis

    (Aristotle University of Thessaloniki
    Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thermi)

  • Merel E. P. Wilmsen

    (Erasmus University Medical Center)

  • Tessa M. Alofs

    (Erasmus University Medical Center)

  • Danique A. Laport

    (Erasmus University Medical Center)

  • Tamara van Wees

    (Erasmus University Medical Center)

  • Geoffrey Kraker

    (Cytek Biosciences)

  • Maria C. Jaimes

    (Cytek Biosciences)

  • Sebastiaan Van Bockstael

    (Cytek Biosciences)

  • Manuel Hernández-González

    (Hospital Universitari Vall d’Hebron, Campus Vall d’Hebron
    Universitat Autònoma de Barcelona
    Translational Immunology Research Group, Vall d’Hebron Institut de Recerca (VHIR), Campus Vall d’Hebron)

  • Casper Rokx

    (Erasmus University Medical Center)

  • Bart J. A. Rijnders

    (Erasmus University Medical Center)

  • Ricardo Pujol-Borrell

    (Hospital Universitari Vall d’Hebron, Campus Vall d’Hebron
    Universitat Autònoma de Barcelona
    Translational Immunology Research Group, Vall d’Hebron Institut de Recerca (VHIR), Campus Vall d’Hebron
    Vall d’Hebron Institute of Oncology (VHIO))

  • Peter D. Katsikis

    (Erasmus University Medical Center)

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.

Suggested Citation

  • 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 & Melis, 2022. "Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28621-0
    DOI: 10.1038/s41467-022-28621-0
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    Cited by:

    1. Tao Qi & Fangzhao Wu & Chuhan Wu & Liang He & Yongfeng Huang & Xing Xie, 2023. "Differentially private knowledge transfer for federated learning," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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