PB0604 The VWF-ADAMTS13 Axis as Biomarkers of Disease Progression in Severe COVID-19
Bibliographic record
Abstract
Background: Von Willebrand factor (VWF), when secreted from activated endothelial cells (EC), regulates platelet recruitment and protects coagulation factor VIII (FVIII).As such, VWF is an indicator of acute EC activation and is elevated in severe COVID-19.ADAMTS13, the protease that processes VWF, is also reduced in severe COVID-19.These observations explain how EC injury may result in COVID-19-associated coagulopathy.However, the role of these biomarkers throughout disease progression and after acute SARS-CoV-2 infection requires further investigation.Aims: To assess biomarkers of the VWF-ADAMTS13 axis throughout disease progression in severe COVID-19 and further validate their association with clinical outcomes.Methods: Plasma samples were collected from 51 COVID-19 patients admitted to ICU.Two samples were analyzed, one at admission and another between days 5 and 15.An additional sample was collected at day 21/22 for patients confirmed COVID-19 negative via qRT-PCR.Clinical outcomes related to coagulopathy were recorded.VWF antigen (VWF:Ag), activity (VWF:GPIbM), propeptide (VWFpp), FVIII, ADAMTS13 activity (ADAMTS13:Ac) and antigen (ADAMTS13:Ag) were measured in all samples.Results: Clinical outcomes included deep vein thrombosis, pulmonary embolism, stroke, and death (Table 1).VWF:Ag and VWF:GPIbM were markedly elevated with mean values peaking at days 11 to 15 (Figure 1A, 1B).FVIII and VWFpp values were moderately elevated and displayed similar patterns throughout disease progression.Mean ADAMTS13:Ac and ADAMTS13:Ag values were below normal range, with some patients experiencing an ADAMTS13:Ac reduction to ≤10% (Figure 1C).There was significant association (P ≤ 0.05) between VWF:Ag, VWF:GPIbM, VWFpp, and ADAMTS13:Ag levels at days 5 to 15 and death.VWF:GPIbM and VWFpp levels during days 5 to 15 were also associated with stroke.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".