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High levels of von Willebrand factor with reduced specific activities in hospitalized patients with or without COVID-19.

Monica BrayMelda A GuzelFong Wilson LamAndrew YeeMiguel A CruzRolando E Rumbaut
Published in: Journal of thrombosis and thrombolysis (2022)
The COVID-19 pandemic is often accompanied by severe respiratory illness and thrombotic complications. Von Willebrand Factor (VWF) levels are highly elevated in this condition. However, limited data are available on the qualitative activity of VWF in COVID-19. We measured plasma VWF levels quantitatively (VWF antigen) and qualitatively (ristocetin-induced platelet agglutination, glycoprotein IbM (GPIbM) binding, and collagen binding). Consistent with prior reports, VWF antigen levels were significantly elevated in hospitalized patients with or without COVID-19. The GPIbM and collagen binding activity-to-antigen ratios were significantly reduced, consistent with qualitative changes in VWF in COVID-19. Of note, critically ill hospitalized patients without COVID-19 had similar reductions in VWF activity-to-antigen ratios as patients with COVID-19. Our data suggest that qualitative changes in VWF in COVID-19 may not be specific to COVID-19. Future studies are warranted to determine the mechanisms responsible for qualitative changes in VWF in COVID-19 and other critical illnesses.• VWF levels were increased in COVID-19 compared to healthy controls.• VWF activity-to-antigen ratios were decreased in COVID-19 compared to healthy controls.• There were no differences in VWF activity-to-antigen ratios between hospitalized patients with or without COVID-19.• These findings are consistent with qualitative changes in VWF in systemic inflammation which are not specific to COVID-19.• Future studies are needed to define possible roles of changes in conformation or multimer length in the qualitative changes in VWF in systemic inflammation.
Keyphrases
  • coronavirus disease
  • sars cov
  • respiratory syndrome coronavirus
  • systematic review
  • emergency department
  • oxidative stress
  • machine learning
  • electronic health record
  • big data