Pathogenetic and prognostic significance of inflammation and altered ADAMTS-13/vWF axis in patients with severe COVID-19
Bibliographic record
Abstract
Introduction. Currently, endothelial dysfunction caused by inflammation and immunothrombosisis considered as one of the crucial mechanisms in developing the SARS-CoV-2 virus-mediated coronavirus disease 2019 (COVID-19). A mass endothelial damage followed by release of untypical large quantity of von Willebrand factor (vWF) multimers and subsequent consumption of metalloproteinase ADAMTS-13 (a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13) is described during severe COVID-19. The activation of innate immune cells including neutrophils results in formation of neutrophil extracellular traps (NETs) and myeloperoxidase (MPO) release that, in turn, contributes to spread of inflammation and microvascular thrombosis. Aim: to evaluate a pathogenetic role and predictive significance for serum markers of inflammation, endothelial dysfunction and hemostatis activation such as vWF, ADAMTS-13 and MPO for in-hospital mortality in severe COVID-19 patients requiring mechanical lung ventilation. Materials and Methods. There was performed a single-center observational study with 129 severe COVID-19 patients on mechanical lung ventilation at the intensive care unit, by assessing serum in all subjects vWF, ADAMTS-13 as well as in 79 patients MPO level along with other potential predictors for in-hospital mortality. Results. A multivariate analysis revealed that increased serum level for vWF antigen (vWF:Ag) and MPO antigen (MPO:Ag) were significantly and independently related to high mortality probability: vWF:Ag (IU/ml) – adjusted odds ratio (OR) = 3.360; 95 % confidence interval (95 % Cl) = 1.562–7,228 (р = 0,0019); MPO:Ag (ng/ml) – adjusted OR = 1.062; 95 % = 1.024–1.101 (p = 0.0011). Such data allowed to obtained a simplified mortality score for categorizing patients as those having a higher or lower score compared with the median score level: a high score was associated with lower cumulative survival rate (p < 0.0001), with 50 % of the cases linked to lethal outcome on day 13 post-hospital admission. Conclusion. Severe COVID-19 patients requiring mechanical lung ventilation were found to have elevated level of serum MPO activity and vWF correlating with poor survival.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".