Humidified Noninvasive Ventilation versus High-Flow Therapy to Prevent Reintubation in Patients with Obesity: A Randomized Clinical Trial
Bibliographic record
Abstract
Abstract Rationale The optimal strategy to prevent reintubation in patients with obesity remains uncertain. Objectives We aimed to determine whether noninvasive ventilation (NIV) with active humidification is superior to a high-flow nasal cannula (HFNC) in preventing reintubation in patients with obesity at intermediate risk. Methods We conducted a randomized controlled trial in two ICUs in Spain (June 2020–June 2021). We included patients ready for planned extubation with a body mass index >30 and three or fewer risk factors for reintubation. Patients with hypercapnia at the end of the spontaneous breathing trial were excluded. Patients were randomized to undergo NIV with active humidification or HFNC for 48 hours after extubation. The primary outcome was the reintubation rate within 7 days after extubation. As a secondary analysis, we performed a post hoc Bayesian analysis using three different priors. Measurements and Main Results Of 144 patients (median age, 61 [25th–75th percentile range, 61–67] yr; 65 [45%] men), 72 received NIV and 72 received an HFNC. Reintubation was required in 17 (23.6%) patients receiving NIV and in 24 (33.3%) patients receiving HFNC (difference between groups, 9.7; 95% confidence interval, −4.9, 24.4). All of the secondary analysis showed nonsignificant differences. In the exploratory Bayesian analysis, the probability of a reduction in reintubation with NIV was 99% (data-driven prior), 90% (minimally informative prior), or 89% (skeptical prior). Conclusions Among adult critically ill patients with obesity at intermediate risk for extubation failure, the rate of reintubation was not significantly lower with NIV than with HFNC. Nevertheless, there is a risk for underpowered results.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| 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".