Auto-Titrating Versus Standard Continuous Positive Airway Pressure for the Treatment of Obstructive Sleep Apnea: Results of a Meta-analysis
Bibliographic record
Abstract
STUDY OBJECTIVE: To compare the effectiveness of auto-titrating continuous positive airway pressure (APAP) versus conventional continuous positive airway pressure (CPAP) in reducing the apnea-hypopnea index (AHI), reducing the mean airway pressure, improving subjective sleepiness, and improving treatment adherence in patients with obstructive sleep apnea (OSA). DESIGN: Meta-analysis and metaregression of published randomized trials comparing APAP to CPAP. SETTING: N/A. PARTICIPANTS: N/A. INTERVENTIONS: N/A. RESULTS: We identified 9 randomized trials studying a total of 282 patients. Compared to CPAP, there was no significant advantage of APAP in reducing AHI or sleepiness (pooled APAP-CPAP posttreatment AHI and Epworth Sleepiness Scale score = -0.20 events per hour, 95% confidence interval:[-0.74,0.35], and -0.56 [-1.4,0.3] respectively). The use of APAP reduced the mean applied pressure across the night by 2.2 cm water [1.9,2.5] compared to CPAP. Adherence with therapy was not substantially improved with APAP; pooled estimate of improvement was 0.20 hours per night ([-0.16,0.57], P = .28) using a random-effects model. CONCLUSIONS: Compared to standard CPAP, APAP is associated with a reduction in mean pressure. However, APAP and standard CPAP were similar in adherence and their ability to eliminate respiratory events and to improve subjective sleepiness. Given that APAP is more costly than standard CPAP, APAP should not be considered first-line chronic therapy in all patients with OSA. However, APAP may be useful in other situations (eg, home titrations, detection of mouth leak) or in certain subgroups of patients with OSA. Identifying circumstances in which APAP is a definite improvement over CPAP in terms of costs or effects should be the focus of future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.046 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".