Acceptance of and adherence with long-term non-invasive positive airway pressure therapy in patients with COPD: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Non-invasive positive airway pressure (PAP) therapy is an effective, albeit complex, intervention for obstructive sleep apnea (OSA) and chronic hypercapnic respiratory failure (CHRF) in patients with chronic obstructive pulmonary disease (COPD). Systematic review methods were used to summarize acceptance of and adherence with long-term PAP therapy. Methods: Seven databases and the grey literature were searched. Study arms were assessed according to a predefined protocol (PROSPERO CRD42021259262). Acceptance and adherence (hours of use/day) were pooled with inverse variance weighted random effects meta-analyses using Freeman–Tukey-transformed values and quantile estimation of medians, respectively. Meta-regression explored heterogeneity. Results: Of the 86 included studies, 69 arms from 51 studies contributed data to acceptance and 78 arms from 60 studies contributed data to adherence. PAP therapy was discontinued on average in 13% (95% CI 9–17) of cases, often within 6 weeks of initiation among studies reporting repeated measures. The pooled median adherence was 6.24 hours/day (95% CI 5.80–6.69) in 66 arms reporting this specific measure. There was sporadic reporting of barriers to use other than device intolerance. Meta-regression found higher acceptance (p=0.03) and longer use/day (p<0.01) when PAP therapy was prescribed for CHRF due to COPD versus OSA. Conclusions: Patients with COPD can achieve success with PAP therapy; however, heterogeneity is significant among studies, partly explained by indication for use. Comprehensive reporting on barriers to use is needed to explore variability in PAP acceptance and adherence.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".