Noninvasive Ventilation Before Intubation and Mortality in Patients Receiving Extracorporeal Membrane Oxygenation for COVID-19: An Analysis of the Extracorporeal Life Support Organization Registry
Bibliographic record
Abstract
Bilevel-positive airway pressure (BiPAP) is a noninvasive respiratory support modality which reduces effort in patients with respiratory failure. However, it may increase tidal ventilation and transpulmonary pressure, potentially aggravating lung injury. We aimed to assess if the use of BiPAP before intubation was associated with increased mortality in adult patients with coronavirus disease 2019 (COVID-19) who received venovenous extracorporeal membrane oxygenation (ECMO). We used the Extracorporeal Life Support Organization Registry to analyze adult patients with COVID-19 supported with venovenous ECMO from January 1, 2020, to December 31, 2021. Patients treated with BiPAP were compared with patients who received other modalities of respiratory support or no respiratory support. A total of 9,819 patients from 421 centers were included. A total of 3,882 of them (39.5%) were treated with BiPAP before endotracheal intubation. Patients supported with BiPAP were intubated later (4.3 vs . 3.3 days, p < 0.001) and showed higher unadjusted hospital mortality (51.7% vs. 44.9%, p < 0.001). The use of BiPAP before intubation and time from hospital admission to intubation resulted as independently associated with increased hospital mortality (odds ratio [OR], 1.32 [95% confidence interval {CI}, 1.08-1.61] and 1.03 [1-1.06] per day increase). In ECMO patients with severe acute respiratory failure due to COVID-19, the extended use of BiPAP before intubation should be regarded as a risk factor for mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".