Impact of High-Flow Nasal Cannula Use in Subjects With COVID-19 ARDS at High Altitudes: Clinical Presentation and Prognostic Factors
Bibliographic record
Abstract
BACKGROUND: High-flow nasal cannula (HFNC) reduces the need for intubation in adult subject with acute respiratory failure. Changes in hypobaric hypoxemia have not been studied for subject with an HFNC in ICUs at altitudes > 2,600 m above sea level. In this study, we investigated the efficacy of HFNC treatment in subjects with COVID-19 at high altitudes. We hypothesized that progressive hypoxemia and the increase in breathing frequency associated with COVID-19 in high altitudes affect the success of HFNC therapy and may also influence the performance of the traditionally used predictors of success and failure. METHODS: This was a prospective cohort study of subjects >18 y with a confirmed diagnosis of COVID-19-induced ARDS requiring HFNC who were admitted to the ICU. Subjects were followed up during the 28 d of HFNC treatment or until failure. RESULTS: delivery between 0.5-0.8 (odds ratio 0.38 [95% CI 0.17-0.84]) was associated with a better response to HFNC therapy than oxygen delivery on admission between 0.8-1.0 (odds ratio 3.58 [95% CI 1.56-8.22]). This relationship continued during follow-ups at 2, 6, 12, and 24 h, with a progressive increase in the risk of failure (odds ratio 24 h 13.99 [95% CI 4.32-45.26]). A new cutoff for the ratio of oxygen saturation (ROX) index (ROX ≥ 4.88) after 24 h of HFNC administration was demonstrated to be the best predictor of success (odds ratio 11.0 [95% CI 3.3-47.0]). CONCLUSIONS: requirements were > 0.8 after 24 h of treatment. In these subjects, personalized management should include continuous monitoring of individual clinical conditions (such as oxygenation indices, with cutoffs adapted to those corresponding to high-altitude cities).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".