Physiologic Effects of High-Flow Nasal Cannula in Healthy Subjects
Bibliographic record
Abstract
BACKGROUND: High-flow nasal cannula (HFNC) is increasingly used in the management of acute and chronic respiratory failure. Little is known about the optimal settings for HFNC. This study was designed to assess the dose effect of HFNC on respiratory effort indexes and respiratory patterns in spontaneously breathing adults. METHODS: A randomized controlled crossover study was conducted in 10 healthy subjects. Five experimental conditions were evaluated: baseline with no therapy; 5 L/min with conventional nasal prongs; and HFNC at 20, 40, and 60 L/min. The primary outcomes were the indexes of respiratory effort (ie, esophageal pressure swing [ΔP es ], esophageal pressure-time product, and work of breathing). Secondary outcomes included breathing pattern parameters and blood gases. Dead-space ventilation and washout were calculated based on minute ventilation, breathing frequency, and Radford equations. RESULTS: ΔP es increased from median (interquartile range [IQR] 3.2 (2.2–3.6) cm H 2 O at baseline to median (IQR) 5.7 (4.6–6.8) cm H 2 O at 60 L/min ( P < .001). Neither esophageal pressure-time product nor work of breathing were modified during the tested conditions. The minute volume was significantly reduced at 40 and 60 L/min compared with baseline (P = .04), mostly driven by an important and dose-dependent reduction in breathing frequency, from median (IQR) 16 (15–18) breaths/min at baseline, to median (IQR) 8 (7–10) breaths/min at 60 L/min ( P < .001). Capillary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mtext mathvariant="bold">P</mml:mtext> <mml:mrow> <mml:msub> <mml:mrow> <mml:mtext mathvariant="bold">CO</mml:mtext> </mml:mrow> <mml:mn mathvariant="bold">2</mml:mn> </mml:msub> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> was stable in all the tested conditions. The calculated dead-space ventilation was reduced by half with HFNC. CONCLUSIONS: HFNC did not significantly modify work of breathing in healthy subjects. However, a significant reduction in the minute volume was achieved, capillary <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mtext mathvariant="bold">P</mml:mtext> <mml:mrow> <mml:msub> <mml:mrow> <mml:mtext mathvariant="bold">CO</mml:mtext> </mml:mrow> <mml:mn mathvariant="bold">2</mml:mn> </mml:msub> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> remaining constant, which suggests a reduction in dead-space ventilation with flows > 20 L/min. (ClinicalTrials.gov registration NCT02495675).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".