Impact of airway closure and lung collapse on inhaled nitric oxide effect in acute lung injury: an experimental study
Bibliographic record
Abstract
BACKGROUND: Efficacy of inhaled therapy such as Nitric Oxide (iNO) during mechanical ventilation may depend on airway patency. We hypothesized that airway closure and lung collapse, countered by positive end-expiratory pressure (PEEP), influence iNO efficacy. This could support the role of an adequate PEEP titration for inhalation therapy. The main aim of this study was to assess the effect of iNO with PEEP set above or below the airway opening pressure (AOP) generated by airway closure, on hemodynamics and gas exchange in swine models of acute respiratory distress syndrome. Fourteen pigs randomly underwent either bilateral or asymmetrical two-hit model of lung injury. Airway closure and lung collapse were measured with electrical impedance tomography as well as ventilation/perfusion ratio (V/Q). After AOP detection, the effect of iNO (10ppm) was studied with PEEP set randomly above or below regional AOP. Respiratory mechanics, hemodynamics, and gas-exchange were recorded. RESULTS: O) after injury. In bilateral injury, iNO was associated with an improved mean pulmonary pressure from 49 ± 8 to 42 ± 7mmHg; (p = 0.003), and ventilation/perfusion matching, caused by a reduction in pixels with low V/Q and shunt from 16%[IQR:13-19] to 9%[IQR:4-12] (p = 0.03) only at PEEP set above AOP. iNO had no effect on hemodynamics or gas exchange for PEEP below AOP (low V/Q 25%[IQR:16-30] to 23%[IQR:14-27]; p = 0.68). In asymmetrical injury, iNO improved pulmonary hemodynamics and ventilation/perfusion matching independently from the PEEP set. iNO was associated with improved oxygenation in all cases. CONCLUSIONS: In an animal model of bilateral lung injury, PEEP level relative to AOP markedly influences iNO efficacy on pulmonary hemodynamics and ventilation/perfusion match, independently of oxygenation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".