Accuracy of delivered airway pressure and work of breathing estimation during proportional assist ventilation: a bench study
Bibliographic record
Abstract
BACKGROUND: Proportional assist ventilation+ (PAV+) delivers airway pressure (P aw) in proportion to patient effort (P mus) by using the equation of motion of the respiratory system. PAV+ calculates automatically respiratory mechanics (elastance and resistance); the work of breathing (WOB) is estimated by the ventilator. The accuracy of P mus estimation and hence accuracy of the delivered P aw and WOB calculation have not been assessed. This study aimed at assessing the accuracy of delivered P aw and calculated WOB by PAV+ and examining the factors influencing this accuracy. METHODS: Using an active lung model with different respiratory mechanics, we compared (1) the actual delivered P aw by the ventilator to the theoretical P aw as defined by the equation of motion and (2) the WOB value displayed by the ventilator to the WOB measured from a Campbell diagram. RESULTS: Irrespective of respiratory mechanics and gain, the ventilator provided a P aw approximately 25 % lower than expected. This underassistance was greatest at the beginning of the inspiration. Intrinsic PEEP (PEEPi), associated with an increase in trigger delay, was a major factor affecting PAV+ accuracy. The absolute value of total WOB displayed by the ventilator was underestimated, but the changes in WOB were accurately detected by the ventilator. CONCLUSION: The assistance provided by PAV+ well follows P mus but with a constant underassistance. This is associated with an underestimation by the ventilator of the WOB. PEEPi can be a major factor contributing to PAV+ inaccuracy. Clinical recommendations should include using a high trigger sensitivity and a careful PEEP titration.
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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 | 0.002 | 0.011 |
| 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 | 0.001 | 0.001 |
| 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".