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

被引：35

作者：

Mueller, Yvonne M. ^{[1
]}

Schrama, Thijs J. ^{[1
]}

Ruijten, Rik ^{[1
]}

Schreurs, Marco W. J. ^{[1
]}

Grashof, Dwin G. B. ^{[1
]}

van de Werken, Harmen J. G. ^{[1
,2
]}

Lasinio, Giovanna Jona ^{[3
]}

Alvarez-Sierra, Daniel ^{[4
]}

Kiernan, Caoimhe H. ^{[1
]}

Eiro, Melisa D. Castro ^{[1
]}

van Meurs, Marjan ^{[1
]}

Brouwers-Haspels, Inge ^{[1
]}

Zhao, Manzhi ^{[1
]}

Li, Ling ^{[1
]}

de Wit, Harm ^{[1
]}

Ouzounis, Christos A. ^{[5
,6
]}

Wilmsen, Merel E. P. ^{[1
]}

Alofs, Tessa M. ^{[1
]}

Laport, Danique A. ^{[1
]}

van Wees, Tamara ^{[1
]}

Kraker, Geoffrey ^{[7
]}

Jaimes, Maria C. ^{[7
]}

Van Bockstael, Sebastiaan ^{[7
]}

Hernandez-Gonzalez, Manuel ^{[4
,8
,9
]}

Rokx, Casper ^{[10
,11
]}

Rijnders, Bart J. A. ^{[10
,11
]}

Pujol-Borrell, Ricardo ^{[4
,8
,9
,12
]}

Katsikis, Peter D. ^{[1
]}

机构：

[1] Erasmus MC, Dept Immunol, Rotterdam, Netherlands

[2] Erasmus MC, Canc Computat Biol Ctr, Erasmus MC Canc Inst, Rotterdam, Netherlands

[3] Univ Roma La Sapienza, Dept Stat Sci, Rome, Italy

[4] Hosp Univ Vall dHebron, Immunol Div, Campus Vall dHebron, Barcelona, Spain

[5] Aristotle Univ Thessaloniki, Fac Sci, Sch Informat, Thessaloniki, Greece

[6] Ctr Res & Technol Hellas, Chem Proc & Energy Resources Inst, Thessaloniki, Greece

[7] Cytek Biosci, Fremont, CA USA

[8] Univ Autonoma Barcelona, Cell Biol Physiol & Immunol Dept, Barcelona, Spain

[9] Vall dHebron Inst Recerca VHIR, Translat Immunol Res Grp, Campus Vall dHebron, Barcelona, Spain

[10] Erasmus MC, Dept Internal Med, Sect Infect Dis, Rotterdam, Netherlands

[11] Erasmus MC, Dept Med Microbiol & Infect Dis, Rotterdam, Netherlands

[12] Vall dHebron Inst Oncol VHIO, Barcelona, Spain

来源：

NATURE COMMUNICATIONS | 2022年 / 13卷 / 01期

基金：

欧盟地平线“2020”;

关键词：

HEALTH; ANTIBODIES;

D O I：

10.1038/s41467-022-28621-0

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：13