Association between arterial carbon dioxide, brain biomarkers and central nervous system injury during veno-venous extracorporeal membrane oxygenation: A Prospective Cohort Study.
Bibliographic record
Abstract
Abstract Background Central nervous system (CNS) injury following initiation of veno-venous extracorporeal membrane oxygenation (VV-ECMO) is common. An acute decrease in PaCO2 following VV-ECMO initiation has been suggested as an etiological factor, but the challenges of diagnosing CNS injuries has made discerning a relationship between PaCO2 and CNS injury difficult.Methods We conducted a prospective cohort study of adult patients undergoing VV-ECMO for acute respiratory failure. We collected blood biospecimens to measure brain biomarkers (neurofilament light [NF-L]; glial fibrillary acidic protein [GFAP]; and phosphorylated-tau 181 [p-tau 181]) in the first seven days following initiation of VV-ECMO. We assessed the relationship between both PaCO2 over the first 24-hours and brain biomarkers with CNS injury using mixed methods linear regression.Results In our cohort twelve of 59 (20%) patients had overt CNS injury identified on head CT. The PaCO2 decrease with VV-ECMO initiation was steeper in patients who developed a CNS injury (-0.32%, 95%CI: -0.25 to -0.39) compared to those without (-0.18%, 95%CI: -0.14 to -0.21, P-interaction < 0.001). The mean concentration of NF-L increased over time and was higher in those with a CNS injury (464 [739]) compared to those without (127 [257])(P = 0.001). GFAP was higher in those with a CNS injury (4278 [11653] pg/ml) compared to those without (116 [108] pg/ml) (P < 0.001).Conclusions Although rapid decreases in PaCO2 following initiation of VV-ECMO were slightly greater in patients that had CNS injuries vs. those without, data overlap and absence of relationships between PaCO2 and brain biomarkers suggests other pathophysiologic variables are likely at play.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".