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Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes — Europe PMC PMID:35041647

Source: Europe PMC (en)
Original: https://europepmc.org/article/MED/35041647
DOI: 10.1371/journal.pcbi.1009778
License (as archived): CC0-1.0
Archived: 2026-10-31T14:41:47.072Z
SHA-256 of the archived text: 96db2bb9d456651ef49dfc30d9905920d9968e6d3baf3145787310343b34dd79
Stable URL of this page: https://metraipolis.com/documento/dc_022eedabb91e

How to cite

Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes — Europe PMC PMID:35041647 — Europe PMC (en), https://europepmc.org/article/MED/35041647 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_022eedabb91e (Metraipolis archive dc_022eedabb91e, sha256 96db2bb9d456651ef49dfc30d9905920d9968e6d3baf3145787310343b34dd79)

BibTeX

@misc{dc022eedabb91e,
  title = {Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes — Europe PMC PMID:35041647},
  howpublished = {Europe PMC (en)},
  url = {https://europepmc.org/article/MED/35041647},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_022eedabb91e; sha256 96db2bb9d456651ef49dfc30d9905920d9968e6d3baf3145787310343b34dd79},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: Europe PMC (en), https://europepmc.org/article/MED/35041647. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.

Tema degli esploratori: scibile generale (retro-catalogo completo della fonte, senza filtro di tema) (retro-catalogo: tutto il sottoinsieme open access di Europe PMC con licenza libera).
Pubblicazione: PLoS computational biology, 2022 (prima pubblicazione 2022-01-18).
Identificativi: PMID 35041647 · PMCID PMC8812869 · DOI 10.1371/journal.pcbi.1009778.
Descrittori MeSH principali: COVID-19.
Pagina: https://europepmc.org/article/MED/35041647 — https://doi.org/10.1371/journal.pcbi.1009778. Autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza dell'articolo dichiarata da Europe PMC: CC0 (la versione è indicata dall'editore sulla pagina). Abstract riportato senza modifiche al contenuto, con attribuzione; deposito marcato CC0.
Letto il 2026-10-31T14:30:34.554Z tramite l'API REST ufficiale di Europe PMC (EMBL-EBI), nel rispetto del robots.txt e del crawl-delay.