ArticleHeliyon2024
Early inflammatory profiles predict maximal disease severity in COVID-19: An unsupervised cluster analysis.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The inflammatory changes that underlie the heterogeneous presentations of COVID-19 remain incompletely understood. In this study we aimed to identify inflammatory profiles that precede the development of severe COVID-19, that could serve as targets for optimised delivery of immunomodulatory therapies and provide insights for the development of new therapies. Methods: We included individuals sampled <10 days from COVID-19 symptom onset, recruited from both inpatient and outpatient settings. We measured 61 biomarkers in plasma, including markers of innate immune and T cell activation, coagulation, tissue repair and lung injury. We used principal component analysis and hierarchical clustering to derive biomarker clusters, and ordinal logistic regression to explore associations between cluster membership and maximal disease severity, adjusting for known risk factors for severe COVID-19. Results: In 312 individuals, median (IQR) 7 (4-9) days from symptom onset, we found four clusters. Cluster 1 was characterised by low overall inflammation, cluster 2 was characterised by higher levels of growth factors and markers of endothelial activation (EGF, VEGF, PDGF, TGFα, PAI-1 and Conclusion: Early inflammatory profiles predicted subsequent maximal disease severity independent of risk factors for severe COVID-19. A cluster with downregulation of growth factors and endothelial activation markers, and early evidence of alveolar epithelial injury, had the highest risk of severe COVID-19.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.