Hygrometric Performances of Different High-Flow Nasal Cannula Devices: Bench Evaluation and Clinical Tolerance
Bibliographic record
Abstract
BACKGROUND: High-flow nasal cannula (HFNC) is increasingly used for the management of respiratory failure. Settings include F IO 2 , 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 F IO 2 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 < .001). AH dropped from −3.5 to −10.7 mg H 2 O/L ( P <. 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 < .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 < .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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".