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Record W4306249000 · doi:10.1186/s13613-022-01069-7

Helmet noninvasive support for acute hypoxemic respiratory failure: rationale, mechanism of action and bedside application

2022· review· en· W4306249000 on OpenAlexaff
Melania Cesarano, Domenico Luca Grieco, Teresa Michi, Laveena Munshi, Luca S. Menga, Luca Delle Cese, Ersilia Ruggiero, Tommaso Rosà, Daniele Natalini, Michael C. Sklar, Salvatore Lucio Cutuli, Filippo Bongiovanni, Gennaro De Pascale, Bruno L. Ferreyro, Ewan C. Goligher, Massimo Antonelli

Bibliographic record

VenueAnnals of Intensive Care · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsSinai Health SystemUniversity of Toronto
FundersMinistero della Salute
KeywordsMedicinePressure support ventilationAnesthesiaOxygenationContinuous positive airway pressureHypoxemiaAirwayIntubationMechanical ventilationIntensive care medicineObstructive sleep apnea

Abstract

fetched live from OpenAlex

Helmet noninvasive support may provide advantages over other noninvasive oxygenation strategies in the management of acute hypoxemic respiratory failure. In this narrative review based on a systematic search of the literature, we summarize the rationale, mechanism of action and technicalities for helmet support in hypoxemic patients. In hypoxemic patients, helmet can facilitate noninvasive application of continuous positive-airway pressure or pressure-support ventilation via a hood interface that seals at the neck and is secured by straps under the arms. Helmet use requires specific settings. Continuous positive-airway pressure is delivered through a high-flow generator or a Venturi system connected to the inspiratory port of the interface, and a positive end-expiratory pressure valve place at the expiratory port of the helmet; alternatively, pressure-support ventilation is delivered by connecting the helmet to a mechanical ventilator through a bi-tube circuit. The helmet interface allows continuous treatments with high positive end-expiratory pressure with good patient comfort. Preliminary data suggest that helmet noninvasive ventilation (NIV) may provide physiological benefits compared to other noninvasive oxygenation strategies (conventional oxygen, facemask NIV, high-flow nasal oxygen) in non-hypercapnic patients with moderate-to-severe hypoxemia (PaO2/FiO2 ≤ 200 mmHg), possibly because higher positive end-expiratory pressure (10–15 cmH2O) can be applied for prolonged periods with good tolerability. This improves oxygenation, limits ventilator inhomogeneities, and may attenuate the potential harm of lung and diaphragm injury caused by vigorous inspiratory effort. The potential superiority of helmet support for reducing the risk of intubation has been hypothesized in small, pilot randomized trials and in a network metanalysis. Helmet noninvasive support represents a promising tool for the initial management of patients with severe hypoxemic respiratory failure. Currently, the lack of confidence with this and technique and the absence of conclusive data regarding its efficacy render helmet use limited to specific settings, with expert and trained personnel. As per other noninvasive oxygenation strategies, careful clinical and physiological monitoring during the treatment is essential to early identify treatment failure and avoid delays in intubation.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.118
GPT teacher head0.411
Teacher spread0.293 · 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
GenreReview

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

Citations26
Published2022
Admission routes1
Has abstractyes

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