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Record W3200271807 · doi:10.4187/respcare.09085

Hygrometric Performances of Different High-Flow Nasal Cannula Devices: Bench Evaluation and Clinical Tolerance

2021· article· en· W3200271807 on OpenAlexaff
Mathieu Delorme, Pierre-Alexandre Bouchard, Serge Simard, François Lellouche

Bibliographic record

VenueRespiratory Care · 2021
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsNasal cannulaMedicineCannulaVentilation (architecture)AnesthesiaAnimal scienceSurgeryMechanical engineering

Abstract

fetched live from OpenAlex

BACKGROUND: High-flow nasal cannula (HFNC) is increasingly used for the management of respiratory failure. Settings include <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mtext mathvariant="bold">F</mml:mtext> <mml:mrow> <mml:msub> <mml:mrow> <mml:mtext mathvariant="bold">IO</mml:mtext> </mml:mrow> <mml:mtext mathvariant="bold">2</mml:mtext> </mml:msub> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> , total gas flow, and temperature target. Resulting absolute humidity (AH) at the nasal cannula may affect clinical tolerance, and optimal settings with respect to hygrometry remain poorly documented. METHODS: A bench study was designed to assess AH delivered by 4 HFNC devices (Optiflow, Airvo 2, Precision Flow, and Hydrate) according to flow, ambient temperature, and other available settings. Clinical tolerance of different levels of hygrometry (20, 30, and 40 mg H 2 O/L) was evaluated in 15 healthy volunteers. RESULTS: With <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mtext mathvariant="bold">F</mml:mtext> <mml:mrow> <mml:msub> <mml:mrow> <mml:mtext mathvariant="bold">IO</mml:mtext> </mml:mrow> <mml:mtext mathvariant="bold">2</mml:mtext> </mml:msub> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> set at 1.0, normal ambient temperature, and settings made accordingly to the manufacturers’ recommendations, mean ± SD AH was 42.2 ± 3.1, 39.5 ± 1.8, 35.7 ± 2.0, and 32.9 ± 2.7 mg H 2 O/L for the Airvo 2, Optiflow, Hydrate, and Precision Flow, respectively, ( P &lt; .001). AH dropped from −3.5 to −10.7 mg H 2 O/L ( P &lt;. 001) with high ambient temperature, except for the Precision Flow. Increasing flow did not significantly affect AH except for the Precision Flow (from 36.4 ± 1.6 to 29.8 ± 0.2 mg H 2 O/L at 10 and 40 L/min, respectively, [ P &lt; .001]). The lowest AH was encountered with the Optiflow set with noninvasive ventilation (NIV) mode, without compensation algorithm, and at high ambient temperature (14.2 ± 1.5 mg H 2 O/L). In studied subjects, AH significantly affected breathing comfort, reduced from 7.0 ± 1.0 to 3.0 ± 2.0 at 40 and 20 mg H 2 O/L, respectively, ( P &lt; .001). Comfort was similar at 30 and 40 mg H 2 O/L. CONCLUSIONS: When used according to manufacturer’s recommendations and at normal ambient temperature, all the HFNC devices evaluated achieved satisfactory hygrometric output with respect to breathing comfort evaluated in healthy subjects (≥ 30 mg H 2 O/L). Substantial differences exist between devices, and optimal knowledge of their working principles is required as inappropriate usage may dramatically alter efficacy and clinical tolerance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.217
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.043
GPT teacher head0.350
Teacher spread0.307 · 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 teacher head, 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

Citations10
Published2021
Admission routes1
Has abstractyes

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