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

Applied immunology; Predictive markers; Viral infection

Detalhes bibliográficos
Autores: Mueller, Yvonne, Schrama, Thijs, Ruijten, Rik, Schreurs, Marco WJ, Grashof, Dwin, van de Werken, Harmen J. G., Álvarez de la Sierra, Daniel, Hernández González, Manuel, Pujol Borrell, Ricardo
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Recursos:Departament de Salut de la Generalitat de Catalunya (DS)
Repositorio:Scientia. Dipòsit d'Informació Digital del Departament de Salut
OAI Identifier:oai:scientiasalut.gencat.cat:11351/7860
Acesso em linha:https://hdl.handle.net/11351/7860
Access Level:acceso abierto
Palavra-chave:Hospitals - Pacients
COVID-19 (Malaltia) - Prognosi
DISEASES::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections
ENFERMEDADES::virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus
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spelling Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learningMueller, YvonneSchrama, ThijsRuijten, RikSchreurs, Marco WJGrashof, Dwinvan de Werken, Harmen J. G.Álvarez de la Sierra, DanielHernández González, ManuelPujol Borrell, RicardoHospitals - PacientsCOVID-19 (Malaltia) - PrognosiDISEASES::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus InfectionsENFERMEDADES::virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por CoronavirusApplied immunology; Predictive markers; Viral infectionImmunologia aplicada; Marcadors predictius; Infecció viralInmunología aplicada; Marcadores predictivos; Infección viralQuantitative 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.This work was supported by Health Holland LSHM20056 grant (PDK), in part from the European Union’s Horizon 2020 research and innovation program under grant agreement No 779295 (PDK), in part supported by the Erasmus foundation (BJAR), grant PI20/00416 from the Instituto de Salud Carlos III (RPB) and the European Regional Development Fund (ERDF) (RPB).Nature ResearchInstitut Català de la Salut[Mueller YM, Schrama TJ, Ruijten R, Schreurs MWJ, 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. Cancer Computational Biology Center, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands. [Álvarez-Sierra D] Servei d’Immunologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. [Hernández-González M] Servei d’Immunologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Departament de Biologia Cel•lular, Fisiologia i Immunologia, Universitat Autònoma de Barcelona, Bellaterra, Spain. Grup de Recerca en Immunologia Translacional, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. [Pujol-Borrell R] Servei d’Immunologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Departament de Biologia Cel•lular, Fisiologia i Immunologia, Universitat Autònoma de Barcelona, Bellaterra, Spain. Grup de Recerca en Immunologia Translacional, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, SpainVall d'Hebron Barcelona Hospital Campus202220222022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/11351/7860Scientiareponame:Scientia. Dipòsit d'Informació Digital del Departament de Salutinstname:Departament de Salut de la Generalitat de Catalunya (DS)InglésNature Communications;13https://doi.org/10.1038/s41467-022-28621-0info:eu-repo/grantAgreement/EC/H2020/779295info:eu-repo/grantAgreement/ES/PE2017-2020/PI20%2F00416Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:scientiasalut.gencat.cat:11351/78602026-06-12T09:38:37Z
dc.title.none.fl_str_mv Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
title Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
spellingShingle Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
Mueller, Yvonne
Hospitals - Pacients
COVID-19 (Malaltia) - Prognosi
DISEASES::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections
ENFERMEDADES::virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus
title_short Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
title_full Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
title_fullStr Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
title_full_unstemmed Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
title_sort Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
dc.creator.none.fl_str_mv Mueller, Yvonne
Schrama, Thijs
Ruijten, Rik
Schreurs, Marco WJ
Grashof, Dwin
van de Werken, Harmen J. G.
Álvarez de la Sierra, Daniel
Hernández González, Manuel
Pujol Borrell, Ricardo
author Mueller, Yvonne
author_facet Mueller, Yvonne
Schrama, Thijs
Ruijten, Rik
Schreurs, Marco WJ
Grashof, Dwin
van de Werken, Harmen J. G.
Álvarez de la Sierra, Daniel
Hernández González, Manuel
Pujol Borrell, Ricardo
author_role author
author2 Schrama, Thijs
Ruijten, Rik
Schreurs, Marco WJ
Grashof, Dwin
van de Werken, Harmen J. G.
Álvarez de la Sierra, Daniel
Hernández González, Manuel
Pujol Borrell, Ricardo
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Institut Català de la Salut
[Mueller YM, Schrama TJ, Ruijten R, Schreurs MWJ, 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. Cancer Computational Biology Center, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands. [Álvarez-Sierra D] Servei d’Immunologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. [Hernández-González M] Servei d’Immunologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Departament de Biologia Cel•lular, Fisiologia i Immunologia, Universitat Autònoma de Barcelona, Bellaterra, Spain. Grup de Recerca en Immunologia Translacional, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. [Pujol-Borrell R] Servei d’Immunologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Departament de Biologia Cel•lular, Fisiologia i Immunologia, Universitat Autònoma de Barcelona, Bellaterra, Spain. Grup de Recerca en Immunologia Translacional, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain
Vall d'Hebron Barcelona Hospital Campus
dc.subject.none.fl_str_mv Hospitals - Pacients
COVID-19 (Malaltia) - Prognosi
DISEASES::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections
ENFERMEDADES::virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus
topic Hospitals - Pacients
COVID-19 (Malaltia) - Prognosi
DISEASES::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections
ENFERMEDADES::virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus
description Applied immunology; Predictive markers; Viral infection
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11351/7860
url https://hdl.handle.net/11351/7860
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Nature Communications;13
https://doi.org/10.1038/s41467-022-28621-0
info:eu-repo/grantAgreement/EC/H2020/779295
info:eu-repo/grantAgreement/ES/PE2017-2020/PI20%2F00416
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Nature Research
publisher.none.fl_str_mv Nature Research
dc.source.none.fl_str_mv Scientia
reponame:Scientia. Dipòsit d'Informació Digital del Departament de Salut
instname:Departament de Salut de la Generalitat de Catalunya (DS)
instname_str Departament de Salut de la Generalitat de Catalunya (DS)
reponame_str Scientia. Dipòsit d'Informació Digital del Departament de Salut
collection Scientia. Dipòsit d'Informació Digital del Departament de Salut
repository.name.fl_str_mv
repository.mail.fl_str_mv
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