Repeated endo-tracheal tube disconnection generates pulmonary edema in a model of volume overload: an experimental study
Bibliographic record
Abstract
Abstract Background An abrupt lung deflation in rodents results in lung injury through vascular mechanisms. Ventilator disconnections during endo-tracheal suctioning in humans often cause cardio-respiratory instability. Whether repeated disconnections or lung deflations cause lung injury or oedema is not known and was tested here in a porcine large animal model. Methods Yorkshire pigs (~ 12 weeks) were studied in three series. First, we compared PEEP abruptly deflated from 26 cmH2O or from PEEP 5 cmH2O to zero. Second, pigs were randomly crossed over to receive rapid versus gradual PEEP removal from 20 cmH2O. Third, pigs with relative volume overload, were ventilated with PEEP 15 cmH2O and randomized to repeated ETT disconnections (15 s every 15 min) or no disconnection for 3 h. Hemodynamics, pulmonary variables were monitored, and lung histology and bronchoalveolar lavage studied. Results As compared to PEEP 5 cmH2O, abrupt deflation from PEEP 26 cmH2O increased PVR, lowered oxygenation, and increased lung wet-to-dry ratio. From PEEP 20 cmH2O, gradual versus abrupt deflation mitigated the changes in oxygenation and vascular resistance. From PEEP 15, repeated disconnections in presence of fluid loading led to reduced compliance, lower oxygenation, higher pulmonary artery pressure, higher lung wet-to-dry ratio, higher lung injury score and increased oedema on morphometry, compared to no disconnects. Conclusion Single abrupt deflation from high PEEP, and repeated short deflations from moderate PEEP cause pulmonary oedema, impaired oxygenation, and increased PVR, in this large animal model, thus replicating our previous finding from rodents. Rapid deflation may thus be a clinically relevant cause of impaired lung function, which may be attenuated by gradual pressure release.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".