Effect of Continuous Positive Airway Pressure on Intraocular Pressure in Patients With Obstructive Sleep Apnoea: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background While the association between obstructive sleep apnoea (OSA) and glaucoma has been well studied, the long‐term effects of continuous positive airway pressure (CPAP) therapy on intraocular pressure (IOP) and glaucoma progression are less known. This study aims to systematically synthesise evidence to clarify the association between CPAP therapy and IOP. Methods PubMed, Embase, The Cochrane Library, CINAHL, Scopus and Web of Science were searched through 26 March 2025 for interventional studies evaluating the effect of CPAP therapy on IOP among OSA adult patients. Two independent authors selected relevant articles, extracted data and evaluated the quality of evidence using the Newcastle–Ottawa scale (NOS), Cochrane Risk‐of‐Bias Tool for Randomised Trials (RoB 2) and Grading of Recommendations Assessment, Development and Evaluation (GRADE). Inverse variance meta‐analyses were conducted using random effects. I 2 values were used to evaluate heterogeneity. Results This study included 16 studies, pooling a cohort of 602 patients. The risk of bias of studies ranged from low to moderate, and the quality of evidence was moderate on GRADE. CPAP therapy significantly increased IOP levels overnight (MD: −4.14; 95% CI: −7.76, −0.52; I 2 = 76%; p = 0.04), and within 1 month (MD: −0.78; 95% CI: −1.21, −0.35; I 2 = 0%; p = 0.60). IOP levels remained unchanged with CPAP therapy of more than 1 month from baseline. Conclusions While short‐term CPAP therapy of 1 month or less in OSA patients was associated with elevated IOP, minimal long‐term effects were observed. Nonetheless, these findings underscore the importance of exercising caution when administering CPAP therapy on IOP especially in glaucomatous patients.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".