Predictors of Noninvasive Ventilation Failure in the Post-Extubation Period: A Systematic Review and Meta-Analysis*
Bibliographic record
Abstract
OBJECTIVES: To identify factors associated with failure of noninvasive ventilation (NIV) in the post-extubation period. DATA SOURCES: We searched Embase Classic +, MEDLINE, and the Cochrane Database of Systematic Reviews from inception to February 28, 2022. STUDY SELECTION: We included English language studies that provided predictors of post-extubation NIV failure necessitating reintubation. DATA EXTRACTION: Two authors conducted data abstraction and risk-of-bias assessments independently. We used a random-effects model to pool binary and continuous data and summarized estimates of effect using odds ratios (ORs) mean difference (MD), respectively. We used the Quality in Prognosis Studies tool to assess risk of bias and the Grading of Recommendations, Assessment, Development and Evaluations to assess certainty. DATA SYNTHESIS: We included 25 studies ( n = 2,327). Illness-related factors associated with increased odds of post-extubation NIV failure were higher critical illness severity (OR, 3.56; 95% CI, 1.96-6.45; high certainty) and a diagnosis of pneumonia (OR, 6.16; 95% CI, 2.59-14.66; moderate certainty). Clinical and biochemical factors associated with moderate certainty of increased risk of NIV failure post-extubation include higher respiratory rate (MD, 1.54; 95% CI, 0.61-2.47), higher heart rate (MD, 4.46; 95% CI, 1.67-7.25), lower Pa o2 :F io2 (MD, -30.78; 95% CI, -50.02 to -11.54) 1-hour after NIV initiation, and higher rapid shallow breathing index (MD, 15.21; 95% CI, 12.04-18.38) prior to NIV start. Elevated body mass index was the only patient-related factor that may be associated with a protective effect (OR, 0.21; 95% CI, 0.09-0.52; moderate certainty) on post-extubation NIV failure. CONCLUSIONS: We identified several prognostic factors before and 1 hour after NIV initiation associated with increased risk of NIV failure in the post-extubation period. Well-designed prospective studies are required to confirm the prognostic importance of these factors to help further guide clinical decision-making.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".