Inter-lung asymmetrical airway closure cause insufflation delay between lungs in acute hypoxemic respiratory failure
Bibliographic record
Abstract
Abstract Background Electrical Impedance Tomography (EIT) can quantify ventilation in the two lungs and be used to measure the airway opening pressure (AOP) of each lung. Asymmetrical AOPs can cause inter-lung insufflation delay. Objectives To assess the relation between AOP asymmetry and inter-lung insufflation delay at different PEEP levels. Methods Patients with acute hypoxemic respiratory failure and airway closure were included. Low-flow pressure-volume curves and EIT signal were recorded during controlled ventilation and for some patients in pressure support ventilation. Results 23 patients were studied, 22 patients had ARDS, 9 patients had asymmetrical airway closure with an AOP of 10 [6–13] cmH20 in the sicker lung (AOPsicker) vs. 5 [3–9, ] cmH20 in the healthier lung. During a low flow inflation, the inter-lung inflation delay was 0 [0-112]ms vs. 1450 [375–2400]ms in patients without or with asymmetrical AOPs, p < 0.0001. This delay was correlated to the difference of AOP between the 2 lungs, Spearman R2 = 0.800, p < 0.0001. During tidal ventilation, median delay was 0 [0–62] ms vs. 150 [50–355] ms in patients without vs. with asymmetry, p = 0.019. Setting PEEP at the crossing point of a decremental EIT-based PEEP trial decreased the inter-lung insufflation delay. During pressure support insufflation delay could still be measured and was reduced by increasing PEEP from 5 to 10 cmH2O in patient with asymmetrical lung injury. Conclusion In asymmetrical airway closure, titrating PEEP can minimize inter-lung insufflation delay and can be monitored by EIT. Reducing the delay and reducing ventilation asymmetry is also feasible during pressure support ventilation when low flow inflation curves cannot be performed.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".