Efficacy of Continuous Positive Airway Pressure Treatment in Patients with Cardiac Arrhythmia and Obstructive Sleep Apnea: What is the Evidence?
Bibliographic record
Abstract
Numerous studies have been conducted in the past few decades on obstructive sleep apnea (OSA) as an independent risk factor for the development and progression of cardiac arrhythmia. Treatment with continuous positive airway pressure (CPAP) therapy lowers blood pressure, improves ejection fraction in patients with heart failure, and decreases the need of revascularization procedures in patients with coronary artery disease. However, there has been little published discussion regarding the effect of CPAP treatment on cardiac arrhythmia in patients with OSA. We aimed to summarize scientific evidence regarding the utility of CPAP treatment in managing the recurrence and severity of different types of cardiac arrhythmia. We retrieved articles published in English before December 2016, through database search of PubMed, CENTRAL, and Embase. All peer-reviewed experimental, comparative, and other observational studies focused on adults were considered eligible. Several studies documented positive changes in arrhythmia frequency and severity with CPAP application, irrespective of type of arrhythmia, study methodology, or setting. However, the available evidence on the impact of CPAP treatment on the frequency and severity of cardiac arrhythmia is limited by heterogeneity of data. The duration of CPAP application, compliance with treatment, and baseline severity of OSA and cardiac pathology are important confounding factors that influence the effect of CPAP treatment. We emphasize the need for agreement on a core set of relevant predictors of the magnitude of the effect. Future research should carefully address these factors and their influence on the potential therapeutic ability of CPAP treatment to improve cardiac outcomes and reduce the risks of adverse cardiac and noncardiac effects associated with untreated OSA.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".