Nature Communications (Feb 2022)
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 A. Ouzounis,
- Merel E. P. Wilmsen,
- Tessa M. Alofs,
- Danique A. Laport,
- Tamara van Wees,
- Geoffrey Kraker,
- Maria C. Jaimes,
- Sebastiaan Van Bockstael,
- Manuel Hernández-González,
- Casper Rokx,
- Bart J. A. Rijnders,
- Ricardo Pujol-Borrell,
- Peter D. Katsikis
Affiliations
- Yvonne M. Mueller
- Department of Immunology, Erasmus University Medical Center
- Thijs J. Schrama
- Department of Immunology, Erasmus University Medical Center
- Rik Ruijten
- Department of Immunology, Erasmus University Medical Center
- Marco W. J. Schreurs
- Department of Immunology, Erasmus University Medical Center
- Dwin G. B. Grashof
- Department of Immunology, Erasmus University Medical Center
- Harmen J. G. van de Werken
- Department of Immunology, Erasmus University Medical Center
- Giovanna Jona Lasinio
- Department of Statistical Sciences, University of Rome “La Sapienza”
- Daniel Álvarez-Sierra
- Immunology Division, Hospital Universitari Vall d’Hebron, Campus Vall d’Hebron
- Caoimhe H. Kiernan
- Department of Immunology, Erasmus University Medical Center
- Melisa D. Castro Eiro
- Department of Immunology, Erasmus University Medical Center
- Marjan van Meurs
- Department of Immunology, Erasmus University Medical Center
- Inge Brouwers-Haspels
- Department of Immunology, Erasmus University Medical Center
- Manzhi Zhao
- Department of Immunology, Erasmus University Medical Center
- Ling Li
- Department of Immunology, Erasmus University Medical Center
- Harm de Wit
- Department of Immunology, Erasmus University Medical Center
- Christos A. Ouzounis
- School of Informatics, Faculty of Sciences, Aristotle University of Thessaloniki
- Merel E. P. Wilmsen
- Department of Immunology, Erasmus University Medical Center
- Tessa M. Alofs
- Department of Immunology, Erasmus University Medical Center
- Danique A. Laport
- Department of Immunology, Erasmus University Medical Center
- Tamara van Wees
- Department of Immunology, Erasmus University Medical Center
- Geoffrey Kraker
- Cytek Biosciences
- Maria C. Jaimes
- Cytek Biosciences
- Sebastiaan Van Bockstael
- Cytek Biosciences
- Manuel Hernández-González
- Immunology Division, Hospital Universitari Vall d’Hebron, Campus Vall d’Hebron
- Casper Rokx
- Department of Internal Medicine, Section Infectious Diseases, and Department of Medical Microbiology and Infectious Diseases, Erasmus University Medical Center
- Bart J. A. Rijnders
- Department of Internal Medicine, Section Infectious Diseases, and Department of Medical Microbiology and Infectious Diseases, Erasmus University Medical Center
- Ricardo Pujol-Borrell
- Immunology Division, Hospital Universitari Vall d’Hebron, Campus Vall d’Hebron
- Peter D. Katsikis
- Department of Immunology, Erasmus University Medical Center
- DOI
- https://doi.org/10.1038/s41467-022-28621-0
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 13
Abstract
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.
