Abstract 17346: The Causal Role of Obstructive Sleep Apnea (OSA) in Atrial Fibrillation (AF): A Systematic Review and Meta-Analysis of Mendelian Randomization Studies
Bibliographic record
Abstract
Background: Atrial fibrillation (AF) causes significant morbidity and mortality. Obstructive sleep apnea (OSA) has been suggested as a risk factor for AF, but the causal relationship between the two is still being investigated. We performed a systematic review and meta-analysis of Mendelian Randomization (MR) studies to determine OSA's role in AF. Methods: We searched PubMed, Scopus, Embase, and Google Scholar databases for MR studies on the causal relationship between OSA and AF through June 2023. Our analysis included eligible studies. The pooled odds ratio and subgroup analysis were calculated using the random-effects model. The I2 statistic was used to assess study heterogeneity, and a leave-one-out sensitivity analysis was performed to determine how individual studies affected the overall estimate and analysis robustness. Results: 5 MR studies met our inclusion criteria until June 2023. In these studies, one used diverse datasets and included patient data of 5 cohorts with UK, Canada, Australia, USA, Finland ancestry while the remaining 4 included patient data of European ancestry. Genetic predisposition to OSA was found to cause AF in the pooled odds ratio analysis (OR 1.21, 95% CI 1.15-1.28, I2=0.00%, p <0.01). The leave-one-out sensitivity analysis showed that the odds ratio marginally changed when specific studies were excluded, suggesting that their contribution to the estimate slightly differed. Conclusion: Our findings suggest a causal relationship between OSA and AF, suggesting that OSA may cause AF. The findings emphasize the need for early evaluation and treatment of AF patients with OSA. However, large-scale prospective studies are required to confirm these findings and determine their clinical practice implications.
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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 | 0.027 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".