Monitoring Inspiratory Effort during Synchronous and Dyssynchronous Mechanical Ventilation
Bibliographic record
Abstract
BackgroundThe precise incidence, magnitude, and consequences of synchronous and dyssynchronous breathing effort during acute hypoxemic respiratory failure remain unknown because of a lack of automated detection and quantification tools. Hypotheses Magnitude of synchronous and dyssynchronous effort and impact on stress and strain and can be quantified using bedside techniques and continuous automated algorithms; there are important interactions between the magnitude and timing of effort, mode of ventilation, and their physiological consequences. Synthesis First, we defined a reference range for synchronous effort and thresholds for injurious effort based on a prospective physiological study and a meta-analysis of studies during spontaneous breathing trials. Second, we studied healthy subjects, patients in physiological studies, and a bench simulation to validate the use of airway occlusion pressure (P0.1) displayed on ventilators to measure respiratory drive (excellent correlation with alternative measures), diagnose low and excessive effort using previous thresholds (AUROC > 0.9), and established the accuracy and precision of P0.1 from different ventilators. Third, we developed two algorithms, one for detection of reverse triggering based on flow and airway pressure waveforms, and another for quantification of the magnitude and consequences of synchronous and dyssynchronous efforts on stress, strain, and alveolar pressure based on muscular pressure. These were developed and validated using physiological studies (accuracy > 95%). Finally, we found in patients with acute hypoxemic respiratory failure (BEARDS cohort study NCT03447288, N=60 patients and 451 recordings) that magnitude of effort during reverse triggering without breath-stacking was similar to patient triggering on pressure support, patient triggering on assist-control had stronger efforts, and effort during breath-stacking was the strongest. We also found that consequences on stress, strain, and alveolar pressure depend primarily on the magnitude of effort but differs according to the mode of ventilation. Conclusions P0.1 accurately measure respiratory drive and diagnoses extremes of effort. Automated tools for quantification of synchronous and dyssynchronous efforts enable analyses of large databases. In acute hypoxemic respiratory failure magnitude of effort varies according to synchrony and physiological consequences of effort are highly influenced by the magnitude of effort and mode of ventilation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".