CPAP vs HFNC in treatment of patients with COVID-19 ARDS: A retrospective propensity-matched study
Bibliographic record
Abstract
Background Previous studies exploring the application of noninvasive ventilation or high-flow nasal cannula in patients with COVID-19-related acute respiratory distress syndrome (ARDS) have yielded conflicting results on whether any method of respiratory support is superior. Our aim is to compare the efficacy and safety of respiratory therapy with high-flow nasal cannula and noninvasive ventilation with continuous positive airway pressure in treatment of COVID-19-related ARDS. Methods This is a retrospective cohort study based on data from patients who received respiratory support as part of their treatment in the COVID intensive care unit at the University Hospital Centre Zagreb between February 2021 and February 2023. Using propensity score analysis, 42 patients treated with high-flow nasal cannula (HFNC group) were compared to 42 patients treated with noninvasive ventilation with continuous positive airway pressure (CPAP group). Primary outcome was intubation rate. Results Intubation rate was 71.4% (30/42) in the HFNC group and 40.5% (17/42) in the CPAP group ( p = 0.004). Hazard ratio for intubation was 3.676 (95% confidence interval [CI] 1.480 to 9.232) with the HFNC versus CPAP group. Marginally significant difference in survival between the two groups was observed at 30 days ( p = 0.050) but was statistically significant at 60 days ( p = 0.043). Conclusions Respiratory support with high-flow nasal cannula and noninvasive ventilation with continuous positive airway pressure yielded significantly different intubation rates in favour of continuous positive airway pressure. The same patients also had better 30-day and 60-day survival post-admission.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".