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
in English
| 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Severity of Illness Index
/
Cytokines
/
SARS-CoV-2
/
COVID-19
/
Antibodies, Viral
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Qualitative research
/
Randomized controlled trials
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Nat Commun
Journal subject:
Biology
/
Science
Year:
2022
Document Type:
Article
Affiliation country:
S41467-022-28621-0
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