Endothelial Activation and Permeability in Patients on VV-ECMO Support: An Exploratory Study
Bibliographic record
Abstract
Background Veno-venous extracorporeal membrane oxygenation (VV-ECMO) supports critically ill patients with respiratory failure. However, ECMO may induce systemic inflammation, hemolysis, and hemodilution, potentially resulting in endothelial activation and damage. Therefore, this study explored the longitudinal changes in circulating markers of inflammation, hemolysis, and endothelial activation and damage in patients with COVID-19 on VV-ECMO. Methods Plasma was obtained before, within 48 h as well as on day 4, week 1, and week 2 of ECMO support and after decannulation. Circulating markers were measured using Luminex, ELISA, and spectrophotometry. Human pulmonary endothelial cells were exposed to patient plasma, and in vitro endothelial permeability was assessed using electric cell-substrate impedance sensing. Results From April 2020 to January 2022, plasma was collected from 14 patients (71.4% male; age 54 (45–61) years). IL-6 levels decreased (1.238 vs. 0.614 ng/mL, p = 0.039) while ICAM-1 increased (667 vs. 884 ng/mL, p = 0.003) over time when compared to pre-ECMO. Angiopoietin-1 decreased after ECMO initiation (7.57 vs. 3.58 ng/mL, p = 0.030), whereas angiopoietin-2 increased (5.20 vs. 10.19 ng/mL, p = 0.017), particularly in non-survivors of ECMO. Cell-free hemoglobin decreased directly after VV-ECMO initiation but remained stable thereafter (55.29 vs. 9.19 mg/dL, p = 0.017). Moreover, the plasma obtained at several time points during the ECMO run induced in vitro pulmonary endothelial hyperpermeability. Conclusions This exploratory study shows that patients on VV-ECMO support due to COVID-ARDS exhibit progressive endothelial activation and damage but not inflammation and hemolysis. Larger prospective studies are necessary to elucidate pathophysiological pathways leading to endothelial activation and damage, thereby reducing organ failure in these critically ill patients.
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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 | 0.001 | 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".