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Record W1981435384 · doi:10.1097/ccm.0b013e3181cb0d7b

Patient-ventilator interaction during pressure support ventilation and neurally adjusted ventilatory assist*

2010· article· en· W1981435384 on OpenAlexaff
Jadranka Spahija, Michel de Marchie, Martin Albert, Patrick Bellemare, Stéphane Delisle, Jennifer Beck, Christer Sinderby

Bibliographic record

VenueCritical Care Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsPressure support ventilationMedicineVentilation (architecture)Tidal volumeAnesthesiaMechanical ventilationSpontaneous breathing trialRespiratory minute volumeDiaphragm (acoustics)Respiratory rateControl of respirationCrossover studyRespiratory systemInternal medicineBlood pressureHeart rate

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the effect of pressure support ventilation and neurally adjusted ventilatory assist on breathing pattern, patient-ventilator synchrony, diaphragm unloading, and gas exchange. Increasing the level of pressure support ventilation can increase tidal volume, reduce respiratory rate, and lead to delayed ventilator triggering and cycling. Neurally adjusted ventilatory assist uses diaphragm electrical activity to control the timing and pressure of assist delivery and is expected to enhance patient-ventilator synchrony. DESIGN: Prospective, comparative, crossover study. SETTING: Adult critical care unit in a tertiary university hospital. PATIENTS: Fourteen nonsedated mechanically ventilated patients (n = 12 with chronic obstructive pulmonary disease). INTERVENTIONS: Patients were ventilated for 10-min periods, using two pressure support ventilation levels (lowest tolerable and +7 cm H2O higher) and two neurally adjusted ventilatory assist levels (same peak pressures and external positive end-expiratory pressure as with pressure support ventilation), delivered in a randomized order. MEASUREMENTS AND MAIN RESULTS: Diaphragm electrical activity, respiratory pressures, air flow, volume, neural and ventilator respiratory rates, and arterial blood gases were measured. Peak pressures were 17 +/- 6 cm H2O and 24 +/- 6 cm H2O and 19 +/- 5 cm H2O and 24 +/- 6 cm H2O with high and low pressure support ventilation and neurally adjusted ventilatory assist, respectively. The breathing pattern was comparable with pressure support ventilation and neurally adjusted ventilatory assist during low assist; during higher assist, larger tidal volumes (p = .003) and lower breathing frequencies (p = .008) were observed with pressure support ventilation. Increasing the assist increased cycling delays only with pressure support ventilation (p = .003). Compared with pressure support ventilation, neurally adjusted ventilatory assist reduced delays of ventilator triggering (p < .001 for low and high assist) and cycling (high assist: p = .004; low assist: p = .04), and abolished wasted inspiratory efforts observed with pressure support ventilation in six subjects. The diaphragm electrical activity and pressure-time product for ventilator triggering were lower with neurally adjusted ventilatory assist (p = .005 and p = .02, respectively; analysis of variance). Arterial blood gases were similar with both modes. CONCLUSIONS: Neurally adjusted ventilatory assist can improve patient-ventilator synchrony by reducing the triggering and cycling delays, especially at higher levels of assist, at the same time preserving breathing and maintaining blood gases.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.306
Teacher spread0.288 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations208
Published2010
Admission routes1
Has abstractyes

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