Individualized PEEP can improve both pulmonary hemodynamics and lung function in acute lung injury
Bibliographic record
Abstract
Abstract Rationale There are several approaches to select the optimal positive end-expiratory pressure (PEEP), resulting in different PEEP levels. The impact of different PEEP settings may extend beyond respiratory mechanics, affecting pulmonary hemodynamics. Objectives To compare PEEP levels obtained with three titration strategies—(i) highest respiratory system compliance (C RS ), (ii) electrical impedance tomography (EIT) crossing point; (iii) positive end-expiratory transpulmonary pressure (P L )—in terms of regional respiratory mechanics and pulmonary hemodynamics. Methods Experimental studies in two porcine models of acute lung injury: (I) bilateral injury induced in both lungs, generating a highly recruitable model (n = 37); (II) asymmetrical injury, generating a poorly recruitable model (n = 13). In all experiments, a decremental PEEP titration was performed monitoring P L , EIT (collapse, overdistention, and regional ventilation), respiratory mechanics, and pulmonary and systemic hemodynamics. Measurements and main results PEEP titration methods resulted in different levels of median optimal PEEP in bilateral lung injury: 14(12–14) cmH 2 O for C RS , 11(10–12) cmH 2 O for EIT, and 8(8–10) cmH 2 O for P L , p < 0.001. Differences were less pronounced in asymmetrical lung injury. PEEP had a quadratic U-shape relationship with pulmonary artery pressure (R 2 = 0.94, p < 0.001), right-ventricular systolic transmural pressure, and pulmonary vascular resistance. Minimum values of pulmonary vascular resistance were found around individualized PEEP, when ventilation distribution and pulmonary circulation were simultaneously optimized. Conclusions In porcine models of acute lung injury with variable lung recruitability, both low and high levels of PEEP can impair pulmonary hemodynamics. Optimized ventilation and hemodynamics can be obtained simultaneously at PEEP levels individualized based on respiratory mechanics, especially by EIT and esophageal pressure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".