Effectiveness of high-flow nasal cannula therapy on clinical outcomes in adults with COVID-19: A systematic review
Bibliographic record
Abstract
Introduction/Background: Coronavirus disease 2019 (COVID-19) has high transmissibility and mortality rates. High-flow nasal cannula therapy (HFNC) might reduce the need for orotracheal intubation, easing the burden on the health system caused by COVID-19. The objective of the present study was to examine the effectiveness of HFNC in adult patients hospitalized with COVID-19. Specifically, the present study explores the effects of HFNC on rates of mortality, intubation and intensive care units (ICU) length of stay. The present study also seeks to define predictors of success and failure of HFNC. Methods: A systematic literature search was conducted in the PubMed, EMBASE and SCOPUS databases, and the study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Study quality was assessed using the National Heart, Lung, and Blood Institute's Study Quality Assessment Tools. Results: The search identified 1,476 unique titles; 95 articles received full-text reviews and 40 studies were included in this review. HFNC was associated with a reduction in the rate of orotracheal intubation, notably when compared to conventional oxygen therapy. Studies reported inconsistency in whether HFNC reduced ICU length of stay or mortality rates. Among the predictors of HFNC failure/success, a ratio of oxygen saturation index of approximately 5 or more was associated with HFNC success. Conclusion: In adult patients hospitalized with COVID-19, HFNC may prove effective in reducing the rate of orotracheal intubation. The ratio of the oxygen saturation index was the parameter most examined as a predictor of HFNC success. Low-level research designs, inherent study weaknesses and inconsistent findings made it impossible to conclude whether HFNC reduces ICU length of stay or mortality. Future studies should employ higher level research designs.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".