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

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 indiv

SUBMITTER: Mueller YM 

PROVIDER: S-EPMC8854670 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature