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Lung-Distending Pressure During Spontaneous Breathing Under Mechanical Ventilation Assessed Using Diaphragm Electrical Activity

2025· letter· en· W4410276529 on OpenAlexaff
Nicholas C. K. Lam, Benjamin Coiffard, José Dianti, Christer A. Sinderby, Jennifer Beck, Niall D. Ferguson, Ewan C. Goligher

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typeletter
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSt. Michael's HospitalToronto General Hospital
Fundersnot available
KeywordsMedicineDiaphragm (acoustics)Mechanical ventilationLungAnesthesiaBreathingVentilation (architecture)Internal medicineMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Introduction Ventilator-delivered pressures and patient efforts during assisted mechanical ventilation leave patients at risk of lung injury. Electrical diaphragm activity can provide continuous diaphragm monitoring because it provides an electromyographic measure of diaphragmatic effort. However, it is uncertain whether this can be used to assess respiratory effort of lung distending pressure during spontaneous breathing. Objectives Our primary objective was to establish the validity of using electrical diaphragm activity (Edi) to total lung-distending pressure (ΔPL,dyn). We then applied this method to a longitudinal cohort study to describe the distribution and magnitude of ΔPL,dyn during assisted mechanical ventilation. Methods We performed a secondary analysis based on two published studies. Pocc and Edi were measured in 16 mechanically ventilated patients. ΔPes was measured and observed ΔPL,dyn was computed. Similarly, ΔPes, ΔPL,dyn, Pocc, Edi,occ, and Edi,open were measured in an additional 12 mechanically ventilated patients.Estimated ΔPL,dyn for non-occluded breaths was computed as ΔPaw – Pocc/Edi,occ [asterisk] 2/3 [asterisk] Edi,open. The estimated ΔPL,dyn was validated against observed ΔPL,dyn via Pearson's correlation and Bland-Altman analysis. This method was then applied to 45 patients where ΔPaw, Edi,open, and Pocc were measured. Results The validation analysis was performed using 98 recordings obtained in 28 subjects. We found that the median ΔPL,dyn was 19cm H2O (15-23 cm H2O) and estimated ΔPL,dyn had a median of 20 cm H2O (IQR 16-25 cm H2O). The measured and predicted ΔPL,dyn, showed a moderate correlation (R2=0.70, p<0.0001) with a bias of 6% and limits of agreement approximating 28% – 40%. In the longitudinal cohort with 45 patients and 4508 study-hours, we computed the distribution of estimated ΔPL,dyn during assisted ventilation. Estimated ΔPL,dyn was elevated ( >25 cm H2O) in 21% of hours with spontaneous breathing and 35 patients had ≥1 hour of ΔPL,dyn >25 cm H2O. When estimated ΔPL,dyn exceeded 25 cm H2O, the contribution of respiratory effort to total lung-distending pressure was a median of 71% (IQR 58-82%). Conclusion Dynamic transpulmonary driving pressure, a measure of lung stress during spontaneous breathing, can be estimated by monitoring Edi and Pocc. Excessive respiratory effort may be an important and frequent contributor to excess lung stress and strain during mechanical ventilation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.328
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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