Bi-Level Noninvasive Ventilation in Neonatal Respiratory Distress Syndrome. A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Bi-level noninvasive ventilation (NIV) has been used in respiratory distress syndrome (RDS) as primary treatment, post-extubation, and to treat apnea. This review summarizes studies on bi-level NIV in premature infants with RDS. Nonsynchronized nasal intermittent positive pressure ventilation (nsNIPPV) and synchronized NIPPV (SNIPPV) use pressure settings ≥ those used during mechanical ventilation (MV), and biphasic continuous positive airway pressure (BiPAP) use two nasal continuous positive airway pressure (NCPAP) levels ≤4 cm H2O apart. METHODS: A systematic review (Medline OVID and Pubmed) and meta-analysis of randomized controlled trials. Primary outcomes were bronchopulmonary dysplasia (BPD) and mortality. Secondary outcomes included NIV failure (intubation) and extubation failure (re-intubation). Data were pooled using a fixed-effects model to calculate the relative risk (RR) with 95% confidence interval (CI) between NIV modes (RevMan v 5.3, Copenhagen, Denmark). RESULTS: Twenty-four randomized controlled trials that largely did not correct for mean airway pressure (MAP) and used outdated ventilators were included. Compared with NCPAP, both nsNIPPV and SNIPPV resulted in less re-intubation (RR 0.88 with 95% CI (0.80, 0.97) and RR 0.20 (0.10, 0.38), respectively) and BPD (RR 0.69 (0.49, 0.97) and RR 0.51 (0.29, 0.88), respectively). nsNIPPV also resulted in less intubation (RR 0.57 (0.45, 0.73) versus NCPAP, with no difference in mortality. One study showed less intubation in BiPAP versus NCPAP. CONCLUSIONS: Bi-level NIV versus NCPAP may reduce MV and BPD in premature infants with RDS. Studies comparing equivalent MAP utilizing currently available machines are needed.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".