Estimation of transpulmonary driving pressure using a lower assist maneuver (LAM) during synchronized ventilation in patients with acute respiratory failure: a physiological study
Bibliographic record
Abstract
Abstract Background We previously showed in animals that transpulmonary driving pressure (PL) can be estimated during Neurally Adjusted Ventilatory Assist (NAVA) and Neural Pressure Support (NPS) using a single lower assist maneuver (LAM). The aim of this study was to test the LAM-based estimate of PL (PL_LAM) in patients with acute respiratory failure. Methods This was a prospective, physiological, and interventional study in intubated patients with acute respiratory failure. During both NAVA and simulated NPS (high and low levels of assist), a LAM was performed every 3 min by manually reducing the assist to zero for one single breath (by default, ventilator still provides 2 cmH2O). Following NAVA and NPSSIM periods, patients were sedated and passively ventilated in volume control and pressure control ventilation, to obtain PL during controlled mechanical ventilation (PL_CMV). PL using an esophageal balloon (PL_Pes) was also compared to PL_LAM and PL_CMV. We measured diaphragm electrical activity (Edi), ventilator pressure (PVent), esophageal pressure (Pes) and tidal volume. PL_LAM and PL_Pes were compared to themselves, and to PL_CMV for matching flows and volumes. Results Ten patients were included in the study. For the group, PL_LAM was closely similar to PL_CMV, with a high correlation (R2 = 0.88). Bland–Altman analysis revealed a low Bias of 0.28 cmH2O, and 1.96SD of 5.26 cmH2O. PL_LAM vs PL_Pes were also tightly related (R2 = 0.77). Conclusion This physiological study in patients confirms our previous pre-clinical data that PL_LAM is as good an estimate as PL_Pes to determine PL, in spontaneously breathing patients on assisted mechanical ventilation. Trial registration The study was registered at clinicaltrials.gov (ID NCT05378802) on November 6, 2021
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".