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Monitoring patient-ventilator breath contribution in the critically ill during neurally adjusted ventilatory assist: reliability and improved algorithms for bedside use

2019· article· en· W2947276749 on OpenAlexafffund
Annemijn H. Jonkman, Diana Jansen, Suvarna Gadgil, Christiaan Keijzer, Armand R. J. Girbes, Gert‐Jan Scheffer, Johannes G. van der Hoeven, Pieter R. Tuinman, A. Man, Christer Sinderby, Leo Heunks

Bibliographic record

VenueJournal of Applied Physiology · 2019
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersVrije Universiteit AmsterdamRadboud UniversiteitRadboud Universitair Medisch CentrumUniversity of TorontoAmsterdam University Medical Centers
KeywordsMedicineIntraclass correlationTidal volumeConfidence intervalCardiologyAlgorithmInternal medicineRespiratory systemComputer science

Abstract

fetched live from OpenAlex

The patient-ventilator breath contribution (PVBC) index estimates the relative contribution of the patient to total tidal volume (Vtinsp) during mechanical ventilation in neurally adjusted ventilator assist mode and has been used to titrate ventilator support. The reliability of this index in ventilated patients is unknown and was investigated in this study. PVBC was calculated by comparing tidal volume (Vtinsp) and diaphragm electrical activity (EAdi) during assisted breaths (Vtinsp/EAdi)assist and during unassisted breaths (Vtinsp/EAdi)no-assist. Vtinsp was normalized to peak EAdi (EAdipeak) using 1) one assisted breath, 2) five consecutive assisted breaths, or 3) five assisted breaths with matching EAdi preceding the unassisted breath (N1PVBC2, X5PVBC2, and [Formula: see text], respectively). In addition, PVBC was calculated by comparing only Vtinsp for breaths with matching EAdi (PVBCβ2). Test-retest reliability of the different PVBC calculation methods was evaluated with the intraclass correlation coefficient (ICC) using five repeated PVBC maneuvers performed with a 1-min interval. In total, 125 PVBC maneuvers were analyzed in 25 patients. ICC [95% confidence interval] values were 0.46 [0.23–0.66], 0.51 [0.33–0.70], and 0.42 [0.14–0.69] for N1PVBC2, X5PVBC2, [Formula: see text], respectively. Complex waveform analyses showed that insufficient EAdi filtering by the ventilator software affects reliability of PVBC calculation. With our new EAdi-matching techniques reliability improved (PVBCβ2 ICC: 0.78 [0.60–0.90]). We conclude that current techniques to calculate PVBC exhibit low reliability and that our newly developed criteria and estimation of PVBC—using Vtinsp of assisted breaths and unassisted breaths with matching EAdi—improves reliability. This may help implementation of PVBC in clinical practice. NEW & NOTEWORTHY The patient-ventilator breath contribution (PVBC) index estimates the relative contribution of the patient to tidal volume generated by the patient and the mechanical ventilator during mechanical ventilation in neurally adjusted ventilator assist mode. It could be used to titrate ventilator support and thus to limit development of diaphragm dysfunction in intensive care unit patients. Currently available methods for bedside assessment of PVBC are unreliable. Our newly developed criteria and estimation of PVBC largely improve reliability and help to quantify patient contribution to total inspiratory 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 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)0.005
metaresearch head score (Gemma)0.023
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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".

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Citations18
Published2019
Admission routes2
Has abstractyes

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