High-flow nasal cannula in adults with acute respiratory failure and after extubation: a systematic review and meta-analysis
Bibliographic record
Abstract
High-flow nasal cannula (HFNC) can be used as an initial support strategy for patients with acute respiratory failure (ARF) and after extubation. However, no clear evidence exists to support or oppose HFNC use in clinical practice. We summarized the effects of HFNC, compared to conventional oxygen therapy (COT) and noninvasive ventilation (NIV), on important outcomes including treatment failure and intubation/reintubation rates in adult patients with ARF and after extubation. We searched 4 electronic databases (Pubmed, EMBASE, Scopus, and Web of Science) to identify randomized controlled trials (RCTs) comparing the effects of HFNC with either COT or NIV on rates of 1) treatment failure and 2) intubation/reintubation in adult critically ill patients. We identified 18 RCTs (n = 4251 patients) in pooled analyses. As a primary mode of support, HFNC treatment reduced the risk of treatment failure [Odds Ratio (OR) 0.65; 95% confidence interval (CI) 0.43–0.98; p = 0.04; I2 = 32%] but had no effect on preventing intubation (OR, 0.74; 95%CI 0.45–1.21; p = 0.23; I2 = 0%) compared to COT. When used after extubation, HFNC (vs. COT) treatment significantly decreased reintubation rate (OR 0.46; 95%CI 0.33–0.63; p < 0.00001; I2 = 30%) and extubation failure (OR 0.43; 95%CI 0.25–0.73; p = 0.002; I2 = 66%). Compared to NIV, HFNC significantly reduced intubation rate (OR 0.57; 95%CI 0.36–0.92; p = 0.02; I2 = 0%) when used as initial support, but did no favorably impact clinical outcomes post extubation in few trials. HFNC was superior to COT in reducing treatment failure when used as a primary support strategy and in reducing rates of extubation failure and reintubation when used after extubation. In few trials, HFNC reduced intubation rate compared to NIV when used as initial support but demonstrated no beneficial effects after extubation.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".