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