Control of respiratory drive by extracorporeal CO2 removal in acute exacerbation of COPD breathing on non-invasive NAVA
Bibliographic record
Abstract
Veno-venous extracorporeal CO 2 removal (vv-ECCO 2 R) and non-invasive neurally adjusted ventilator assist (NIV-NAVA) are two promising techniques which may prevent complications related to prolonged invasive mechanical ventilation in patients with acute exacerbation of COPD. A physiological study of the electrical activity of the diaphragm (Edi) response was conducted with varying degrees of extracorporeal CO 2 removal to control the respiratory drive in patients with severe acute exacerbation of COPD breathing on NIV-NAVA. Twenty COPD patients (SAPS II 37 ± 5.6, age 57 ± 9 years) treated with vv-ECCO 2 R and supported by NIV-NAVA were studied during stepwise weaning of vv-ECCO 2 R. Based on dyspnea, tolerance, and blood gases, weaning from vv-ECCO 2 R was successful in 12 and failed in eight patients. Respiratory drive (measured via the Edi) increased to 19 ± 10 μV vs. 56 ± 20 μV in the successful and unsuccessful weaning groups, respectively, resulting in all patients keeping their CO 2 and pH values stable. Edi was the best predictor for vv-ECCO 2 R weaning failure (ROC analysis AUC 0.95), whereas respiratory rate, rapid shallow breathing index, and tidal volume had lower predictive values. Eventually, 19 patients were discharged home, while one patient died. Mortality at 90 days and 180 days was 15 and 25%, respectively. This study demonstrates for the first time the usefulness of the Edi signal to monitor and guide patients with severe acute exacerbation of COPD on vv-ECCO 2 R and NIV-NAVA. The Edi during vv-ECCO 2 R weaning was found to be the best predictor of tolerance to removing vv-ECCO 2 R.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".