ArticleNature communications2022
Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- Randomized phase II clinical trial of ruxolitinib plus simvastatin in COVID19 clinical outcome and cytokine evolution.Frontiers in immunology · 2023Trial
- Fine specificity of antibodies to SARS-CoV-2 nucleoprotein in patients with COVID-19.Immunologic research · 2026Article
- Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches.Frontiers in cellular and infection microbiology · 2025Article
- Identifying and Validating Prognostic Hyper-Inflammatory and Hypo-Inflammatory COVID-19 Clinical Phenotypes Using Machine Learning Methods.Journal of inflammation research · 2025Article
- Disease progression associated cytokines in COVID-19 patients with deteriorating and recovering health conditions.Scientific reports · 2024Article
- Early inflammatory profiles predict maximal disease severity in COVID-19: An unsupervised cluster analysis.Heliyon · 2024Article
- Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes.Biomedicines · 2024Article
- Modulation of local immunity by the vaginal microbiome is associated with triggering spontaneous preterm birth.Frontiers in immunology · 2024Article
- Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York.International journal of medical informatics · 2024Article
- Machine learning-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study.Frontiers in immunology · 2024Article
- Repeated mRNA vaccination sequentially boosts SARS-CoV-2-specific CD8Nature immunology · 2024Article
- Peripheral PD-1Frontiers in immunology · 2024Observational
- A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning.Frontiers in immunology · 2024Article
- Wireless, battery-free, multifunctional integrated bioelectronics for respiratory pathogens monitoring and severity evaluation.Nature communications · 2023Article
- Rare Variants in Primary Immunodeficiency Genes and Their Functional Partners in Severe COVID-19.Biomolecules · 2023Article
- An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model.Human genomics · 2023Article
- Immune biomarkers associated with COVID-19 disease severity in an urban, hospitalized population.Practical laboratory medicine · 2023Article
- Differentially private knowledge transfer for federated learning.Nature communications · 2023Article
- Microfluidic Organ-Chips and Stem Cell Models in the Fight Against COVID-19.Circulation research · 2023Review
Corrections and comments
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Authors and funding
28 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.