Automated Oxygen Titration During CPAP and Noninvasive Ventilation in Healthy Subjects With Induced Hypoxemia
Bibliographic record
Abstract
BACKGROUND: Automated oxygen titration to maintain a stable S pO 2 has been developed for spontaneously breathing patients but has not been evaluated during CPAP and noninvasive ventilation (NIV). METHODS: We performed a randomized controlled crossover, double-blind study on 10 healthy subjects with induced hypoxemia during 3 situations: spontaneous breathing with oxygen support, CPAP (5 cm H 2 O), and NIV (7/3 cm H 2 O). We conducted in random order 3 dynamic hypoxic challenges of 5 min (F IO 2 0.08 ± 0.02, 0.11± 0.02, and 0.14 ± 0.02). For each condition, we compared automated oxygen titration and manual oxygen titration by experienced respiratory therapists (RTs), with the aim to maintain the S pO 2 at 94 ± 2%. In addition, we included 2 subjects hospitalized for exacerbation of COPD under NIV and a subject managed after bariatric surgery with CPAP and automated oxygen titration. RESULTS: The percentage of time in the S pO 2 target was higher with automated compared with manual oxygen titration for all conditions, on average 59.6 ± 22.8% compared to 44.3 ± 23.9% ( P = .004). Hyperoxemia (S pO 2 > 96%) was less frequent with automated titration for each mode of oxygen administration (24.0 ± 24.4% vs 39.1 ± 25.3%, P < .001). During the manual titration periods, the RT made several changes to oxygen flow (5.1 ± 3.3 interventions that lasted 122 ± 70 s/period) compared to none during the automated titration to maintain oxygenation in the targeted S pO 2 . Time in the S pO 2 target was higher with stable hospitalized subjects in comparison with healthy subjects under dynamic-induced hypoxemia. CONCLUSIONS: In this proof-of-concept study, automated oxygen titration was used during CPAP and NIV. The performances to maintain the S pO 2 target were significantly better compared to manual oxygen titration in the setting of this study protocol. This technology may allow decreasing the number of manual interventions for oxygen titration during CPAP and NIV.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".