Estimation of transpulmonary driving pressure during synchronized mechanical ventilation using a single lower assist maneuver (LAM) in rabbits: a comparison to measurements made with an esophageal balloon
Bibliographic record
Abstract
Abstract Background Mechanical ventilation is applied to unload the respiratory muscles, but knowledge about transpulmonary driving pressure (ΔPL) is important to minimize lung injury. We propose a method to estimate ΔPL during neurally synchronized assisted ventilation, with a simple intervention of lowering the assist for one breath (“lower assist maneuver”, LAM). Methods In 24 rabbits breathing spontaneously with imposed loads, titrations of increasing assist were performed, with two neurally synchronized modes: neurally adjusted ventilatory assist (NAVA) and neurally triggered pressure support (NPS). Two single LAM breaths (not sequentially, but independently) were performed at each level of assist by acutely setting the assist to zero cm H2O (NPS) or NAVA level 0 cm H2O/uV (NAVA) for one breath. NPS and NAVA titrations were followed by titrations in controlled-modes (volume control, VC and pressure control, PC), under neuro-muscular blockade. Breaths from the NAVA/NPS titrations were matched (for flow and volume) to VC or PC. Throughout all runs, we measured diaphragm electrical activity (Edi) and esophageal pressure (PES). We measured ΔPL during the spontaneous modes (PL_PES) and controlled mechanical ventilation (CMV) modes (PL_CMV) with the esophageal balloon. From the LAMs, we derived an estimation of ΔPL (“PL_LAM”) using a correction factor (ratio of volume during the LAM and volume during assist) and compared it to measured ΔPL during passive (VC or PC) and spontaneous breathing (NAVA or NPS). A requirement for the LAM was similar Edi to the assisted breath. Results All animals successfully underwent titrations and LAMs for NPS/NAVA. One thousand seven-hundred ninety-two (1792) breaths were matched to passive ventilation titrations (matched Vt, r = 0.99). PL_LAM demonstrated strong correlation with PL_CMV (r = 0.83), and PL_PES (r = 0.77). Bland–Altman analysis revealed little difference between the predicted PL_LAM and measured PL_CMV (Bias = 0.49 cm H2O and 1.96SD = 3.09 cm H2O). For PL_PES, the bias was 2.2 cm H2O and 1.96SD was 3.4 cm H2O. Analysis of Edi and PES at peak Edi showed progressively increasing uncoupling with increasing assist. Conclusion During synchronized mechanical ventilation, a LAM breath allows for estimations of transpulmonary driving pressure, without measuring PES, and follows a mathematical transfer function to describe respiratory muscle unloading during synchronized assist.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".