A novel non-invasive method to detect excessively high respiratory effort and dynamic transpulmonary driving pressure during mechanical ventilation
Bibliographic record
Abstract
Abstract Background Excessive respiratory muscle effort during mechanical ventilation may cause patient self-inflicted lung injury and load-induced diaphragm myotrauma, but there are no non-invasive methods to reliably detect elevated transpulmonary driving pressure and elevated respiratory muscle effort during assisted ventilation. We hypothesized that the swing in airway pressure generated by respiratory muscle effort under assisted ventilation when the airway is briefly occluded (Δ P occ ) could be used as a highly feasible non-invasive technique to screen for these conditions. Methods Respiratory muscle pressure ( P mus ), dynamic transpulmonary driving pressure (Δ P L,dyn , the difference between peak and end-expiratory transpulmonary pressure), and Δ P occ were measured daily in mechanically ventilated patients in two ICUs in Toronto, Canada. A conversion factor to predict Δ P L,dyn and P mus from Δ P occ was derived and validated using cross-validation. External validity was assessed in an independent cohort (Nanjing, China). Results Fifty-two daily recordings were collected in 16 patients. In this sample, P mus and Δ P L were frequently excessively high: P mus exceeded 10 cm H 2 O on 84% of study days and Δ P L,dyn exceeded 15 cm H 2 O on 53% of study days. Δ P occ measurements accurately detected P mus > 10 cm H 2 O (AUROC 0.92, 95% CI 0.83–0.97) and Δ P L,dyn > 15 cm H 2 O (AUROC 0.93, 95% CI 0.86–0.99). In the external validation cohort ( n = 12), estimating P mus and Δ P L,dyn from Δ P occ measurements detected excessively high P mus and Δ P L,dyn with similar accuracy (AUROC ≥ 0.94). Conclusions Measuring Δ P occ enables accurate non-invasive detection of elevated respiratory muscle pressure and transpulmonary driving pressure. Excessive respiratory effort and transpulmonary driving pressure may be frequent in spontaneously breathing ventilated patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".