Positive End-Expiratory Pressure, Pleural Pressure, and Regional Compliance during Pronation. An Experimental Study
Bibliographic record
Abstract
Abstract Rationale The physiological basis of lung protection and the impact of positive end-expiratory pressure (PEEP) during pronation in acute respiratory distress syndrome are not fully elucidated. Objectives To compare pleural pressure (Ppl) gradient, ventilation distribution, and regional compliance between dependent and nondependent lungs, and investigate the effect of PEEP during supination and pronation. Methods We used a two-hit model of lung injury (saline lavage and high-volume ventilation) in 14 mechanically ventilated pigs and studied supine and prone positions. Global and regional lung mechanics including Ppl and distribution of ventilation (electrical impedance tomography) were analyzed across PEEP steps from 20 to 3 cm H2O. Two pigs underwent computed tomography scans: tidal recruitment and hyperinflation were calculated. Measurements and Main Results Pronation improved oxygenation, increased Ppl, thus decreasing transpulmonary pressure for any PEEP, and reduced the dorsal–ventral pleural pressure gradient at PEEP < 10 cm H2O. The distribution of ventilation was homogenized between dependent and nondependent while prone and was less dependent on the PEEP level than while supine. The highest regional compliance was achieved at different PEEP levels in dependent and nondependent regions in supine position (15 and 8 cm H2O), but for similar values in prone position (13 and 12 cm H2O). Tidal recruitment was more evenly distributed (dependent and nondependent), hyperinflation lower, and lungs cephalocaudally longer in the prone position. Conclusions In this lung injury model, pronation reduces the vertical pleural pressure gradient and homogenizes regional ventilation and compliance between the dependent and nondependent regions. Homogenization is much less dependent on the PEEP level in prone than in supine positon.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".