Safety of flow-controlled ventilation with positive and negative end-expiratory pressure in a swine model of intracranial hypertension
Bibliographic record
Abstract
Abstract Background Patients with brain damage often require mechanical ventilation. Although lung-protective ventilation is recommended, the application of increased positive end-expiratory pressure (PEEP) has been associated with elevated intracranial pressure (ICP) due to altered cerebral venous return. This study investigates the effects of flow-controlled ventilation (FCV) using negative end-expiratory pressures (NEEP), on cerebral hemodynamics in a swine model of intracranial hypertension. Methods A model of intracranial hypertension involving bilateral trepan bolt holes was performed in 14 pigs. Pressure-controlled volume-guaranteed ventilation (PCV-VG) with PEEP and FCV using PEEP and then NEEP were applied. Intracranial pressure and oxygenation, as well as systemic hemodynamics and gas exchange parameters, were continuously monitored. Data were collected at baseline and at varying PEEP levels for both PCV-VG and FCV ventilation modalities. Following this, FCV ventilation and NEEP levels of -3, -6 and -9 cmH2O were applied. Results ICP remained stable with low PEEP levels, but significantly decreased with NEEP. Lower ICP following NEEP improved cerebral perfusion pressure and cerebral tissue oxygenation (p < 0.05 for all). FCV with NEEP at EEP-6 and EEP-9 significantly improved cardiac output and mean arterial pressure (MAP), compared to PCV-VG and FCV using PEEP (p < 0.05, respectively). There were no significant differences in gas exchange parameters between modalities (PCV-VG vs FCV), and between the application of PEEP or NEEP. No significant correlations were observed between ΔICP and ΔMAP. Conclusion The application of FCV with NEEP appears to be a safe ventilation mode and offers an additional tool for controlling severe intracranial pressure episodes. These findings warrant validation in future studies and may lead to important potential applications in clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".