Long-term Non-Invasive Ventilation in Infants: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The use of long-term non-invasive ventilation (NIV) to treat sleep and breathing disorders in children has increased substantially in the last decade; however, less data exist about its use in infants. Given that infants have distinct sleep and breathing patterns when compared to older children, the outcomes of infants on long-term NIV may differ as well. The aim of this study is to systematically review the use and outcomes of long-term NIV in infants. METHODS: Ovid Medline, Ovid Embase, CINAHL (via EbscoHOST), PubMed, and Wiley Cochrane Library were systematically searched from January 1990 to July 2017. Studies on infants using long-term NIV outside of an acute care setting were included. Data were extracted on study design, population characteristics, and NIV outcomes. RESULTS: A total of 327 studies were full-text reviewed, with final inclusion of 60. Studies were distributed across airway (40%), neuromuscular (28%), central nervous system (10%), cardio-respiratory (2%), and multiple (20%) disease categories. Of the 18 airway studies reporting on NIV outcomes, 13 (72%) reported improvements in respiratory parameters. Of the 12 neuromuscular studies exclusively on spinal muscular atrophy type 1 (SMA1), six (50%) reported decreased hospitalizations and nine (75%) reported on mortality outcomes. Risk of bias was moderate to serious, and quality of the evidence was low to very low for all studies. Most studies had an observational design with no control group, limiting the potential for a meta-analysis. CONCLUSION: The outcomes reported in studies differed by the disease category being studied. Studies on airway conditions showed improvements in respiratory parameters for infants using NIV. Studies on neuromuscular disorder, which were almost exclusively on SMA1, reported decreased hospitalizations and prolonged survival. Overall, it appears that NIV is an effective long-term therapy for infants. However, the high risk of bias and low quality of the available evidence limited strong conclusions.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".