Effectiveness of Non-Invasive Ventilation vs. Invasive Ventilation in Acute COPD Exacerbation: A Meta-Analysis of Randomized and Observational Studies
Bibliographic record
Abstract
Background: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) often lead to acute respiratory failure, requiring ventilatory support. While invasive mechanical ventilation (IMV) is traditionally used, non-invasive ventilation (NIV) has emerged as a promising alternative with potentially fewer complications. This meta-analysis aimed to compare the effectiveness of NIV versus IMV or standard care in managing AECOPD. Methods: A systematic literature search was conducted across PubMed, Scopus, Embase, and Web of Science for studies published up to March 2024. Randomized controlled trials and observational studies comparing NIV with IMV or standard therapy in adult AECOPD patients were included. The primary outcomes were intubation rate, in-hospital mortality, and hospital length of stay. Statistical analyses were performed using Review Manager (RevMan) version 5.4, applying a random-effects model. Risk of bias was assessed using the Cochrane RoB 2.0 and Newcastle-Ottawa Scale. Results: Three studies were included, comprising 70,141 patients. NIV significantly reduced the risk of intubation (RR: 0.34; 95% CI: 0.33–0.35; I² = 0%) and in-hospital mortality (RR: 0.43; 95% CI: 0.30–0.64; I² = 3%) compared to IMV or standard care. However, no statistically significant difference was observed in hospital length of stay (MD: 0.81 days; 95% CI: –5.79 to 7.42; I² = 79%). Funnel plots suggested minimal publication bias. Conclusion: NIV is significantly more effective than IMV or standard care in reducing both intubation rates and in-hospital mortality in AECOPD patients. While its impact on hospital stay remains inconclusive, these findings support NIV as a frontline strategy in acute COPD management. Further high-quality research is needed to assess long-term outcomes and optimize patient selection.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.072 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".