A Digital Protein Microarray for COVID-19 Cytokine Storm Monitoring.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
17 Jun 2020
Historique:
entrez: 27 6 2020
pubmed: 27 6 2020
medline: 27 6 2020
Statut: epublish

Résumé

Despite widespread concern for cytokine storms leading to severe morbidity in COVID-19, rapid cytokine assays are not routinely available for monitoring critically ill patients. We report the clinical application of a machine learning-based digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit (ICU) at the University of Michigan Hospital. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing/mixing module that can be operated inside a biosafety cabinet to minimize the exposure of technician to the virus infection and (ii) a 12-12-15 inch compact fluorescence optical scanner for the potential near-bedside readout. The platform enabled daily cytokine analysis in clinical practice with high sensitivity (<0.4pg/mL), inter-assay repeatability (~10% CV), and near-real-time operation with a 10 min assay incubation. A cytokine profiling test with the platform allowed us to observe clear interleukin-6 (IL-6) elevations after receiving tocilizumab (IL-6 inhibitor) while significant cytokine profile variability exists across all critically ill COVID-19 patients and to discover a weak correlation between IL-6 to clinical biomarkers, such as Ferritin and CRP. Our data revealed large subject-to-subject variability in a patient's response to anti-inflammatory treatment for COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays.

Identifiants

pubmed: 32587979
doi: 10.1101/2020.06.15.20131870
pmc: PMC7310633
pii:
doi:

Types de publication

Preprint

Langues

eng

Commentaires et corrections

Type : UpdateIn

Auteurs

Classifications MeSH