High-Flow Nasal Cannula Treatment in Patients with COVID-19 Acute Hypoxemic Respiratory Failure
Bibliographic record
Abstract
Background: Early use of high-flow nasal cannula (HFNC) decreases the need for endotracheal intubation (EI) in different respiratory failure causes. While HFNC is used in coronavirus disease 2019 (COVID-19)-related acute hypoxemic respiratory failure (AHRF) under weak recommendations, its efficacy remains to be investigated. Objectives: The primary objective was to examine HFNC efficacy in preventing EI among COVID-19 patients with AHRF. Secondary objectives were to determine predictors of HFNC success/failure, mortality rate, and length of hospital and intensive care unit (ICU) stay. Patients and Methods: This is a prospective cohort study conducted at a single tertiary care centre in Saudi Arabia from April to August 2020. Adult patients admitted to the ICU with AHRF secondary to COVID-19 pneumonia and managed with HFNC were included. We excluded patients who were intubated or managed with non-invasive ventilation before HFNC. Results: Forty-four patients received HFNC for a median duration of 3 days (interquartile range, 1–5 days). The mean age was 57 ± 14 years, and 86% were men. HFNC failure and EI occurred in 29 (66%) patients. Patients in whom HNFC treatment failed had a higher risk of death (52% versus 0%; P = 0.001). After adjusting for confounding factors, a high SOFA score and a low ROX index were significantly associated with HFNC failure (hazard ratio [HR], 1.42; 95% confidence interval [CI], 1.04–1.93; P = 0.025; and HR, 0.61; 95% CI, 0.42–0.88; P = 0.008, respectively). Conclusions: One-third of hypoxemic COVID-19 patients who received HFNC did not require intubation. High SOFA score and low ROX index were associated with HFNC failure.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".