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Record W2985798828 · doi:10.1186/s13054-019-2617-0

A novel non-invasive method to detect excessively high respiratory effort and dynamic transpulmonary driving pressure during mechanical ventilation

2019· article· en· W2985798828 on OpenAlexafffundabout
Michele Bertoni, Irene Telías, Martin Urner, Michael Long, Lorenzo Del Sorbo, Eddy Fan, Christer A. Sinderby, Jennifer Beck, Ling Liu, Haibo Qiu, Jenna Wong, Arthur S. Slutsky, Niall D. Ferguson, Laurent Brochard, Ewan C. Goligher

Bibliographic record

VenueCritical Care · 2019
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity Health NetworkUniversity of TorontoToronto General HospitalSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchPhysicians' Services Incorporated Foundation
KeywordsTranspulmonary pressureMedicineMechanical ventilationPeak inspiratory pressureVentilation (architecture)Respiratory systemDiaphragm (acoustics)Respiratory physiologyAnesthesiaPressure support ventilationAirwayCohortMean airway pressureCardiologyInternal medicineLungLung volumesTidal volume

Abstract

fetched live from OpenAlex

Abstract Background Excessive respiratory muscle effort during mechanical ventilation may cause patient self-inflicted lung injury and load-induced diaphragm myotrauma, but there are no non-invasive methods to reliably detect elevated transpulmonary driving pressure and elevated respiratory muscle effort during assisted ventilation. We hypothesized that the swing in airway pressure generated by respiratory muscle effort under assisted ventilation when the airway is briefly occluded (Δ P occ ) could be used as a highly feasible non-invasive technique to screen for these conditions. Methods Respiratory muscle pressure ( P mus ), dynamic transpulmonary driving pressure (Δ P L,dyn , the difference between peak and end-expiratory transpulmonary pressure), and Δ P occ were measured daily in mechanically ventilated patients in two ICUs in Toronto, Canada. A conversion factor to predict Δ P L,dyn and P mus from Δ P occ was derived and validated using cross-validation. External validity was assessed in an independent cohort (Nanjing, China). Results Fifty-two daily recordings were collected in 16 patients. In this sample, P mus and Δ P L were frequently excessively high: P mus exceeded 10 cm H 2 O on 84% of study days and Δ P L,dyn exceeded 15 cm H 2 O on 53% of study days. Δ P occ measurements accurately detected P mus > 10 cm H 2 O (AUROC 0.92, 95% CI 0.83–0.97) and Δ P L,dyn > 15 cm H 2 O (AUROC 0.93, 95% CI 0.86–0.99). In the external validation cohort ( n = 12), estimating P mus and Δ P L,dyn from Δ P occ measurements detected excessively high P mus and Δ P L,dyn with similar accuracy (AUROC ≥ 0.94). Conclusions Measuring Δ P occ enables accurate non-invasive detection of elevated respiratory muscle pressure and transpulmonary driving pressure. Excessive respiratory effort and transpulmonary driving pressure may be frequent in spontaneously breathing ventilated patients.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.309
Teacher spread0.295 · 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 designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations257
Published2019
Admission routes3
Has abstractyes

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