Individual response in patient’s effort and driving pressure to variations in assistance during pressure support ventilation
Bibliographic record
Abstract
During Pressure Support Ventilation (PSV) an inspiratory hold allows to measure plateau pressure (Pplat), driving pressure (∆P), respiratory system compliance (Crs) and pressure-muscle-index (PMI), an index of inspiratory effort. This study aims [1] to assess systematically how patient’s effort (estimated with PMI), ∆P and tidal volume (Vt) change in response to variations in PSV and [2] to confirm the robustness of Crs measurement during PSV. 18 patients recovering from acute respiratory failure and ventilated by PSV were cross-randomized to four steps of assistance above (+ 3 and + 6 cmH 2 O) and below (-3 and -6 cmH 2 O) clinically set PS. Inspiratory and expiratory holds were performed to measure Pplat, PMI, ∆P, Vt, Crs, P0.1 and occluded inspiratory airway pressure (Pocc). Electromyography of respiratory muscles was monitored noninvasively from body surface (sEMG). As PSV was decreased, Pplat (from 20.5 ± 3.3 cmH 2 O to 16.7 ± 2.9, P < 0.001) and ∆P (from 12.5 ± 2.3 to 8.6 ± 2.3 cmH 2 O, P < 0.001) decreased much less than peak airway pressure did (from 21.7 ± 3.8 to 9.7 ± 3.8 cmH2O, P < 0.001), given the progressive increase of patient’s effort (PMI from -1.2 ± 2.3 to 6.4 ± 3.2 cmH 2 O) in line with sEMG of the diaphragm ( r = 0.614; P < 0.001). As ∆P increased linearly with Vt, Crs did not change through steps ( P = 0.119). Patients react to a decrease in PSV by increasing inspiratory effort—as estimated by PMI—keeping Vt and ∆P on a desired value, therefore, limiting the clinician’s ability to modulate them. PMI appears a valuable index to assess the point of ventilatory overassistance when patients lose control over Vt like in a pressure-control mode. The measurement of Crs in PSV is constant—likely suggesting reliability—independently from the level of assistance and patient’s effort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".