Treating sleep disordered breathing for cardiovascular outcomes: observational and randomised trial evidence
Bibliographic record
Abstract
Sleep disordered breathing (SDB) is considered a risk factor for cardiovascular disease (CVD). Obstructive sleep apnoea (OSA) can be treated with continuous positive airway pressure (CPAP), and central sleep apnoea (CSA), in patients with heart failure with reduced ejection fraction (HFrEF), by peak flow-triggered adaptive servo-ventilation. Presently, there is equipoise as to whether treating SDB prevents cardiovascular events. Some propose treatment for this indication, based on observational data, while others argue against because of the lack of randomised trial evidence. This review evaluates literature concerning the cardiovascular effects of treating SDB with PAP devices in individuals with and without CVDs. Nine observational studies report significantly lower cardiovascular event rates in those treated, than in those not treated, for SDB. Conversely, 12 randomised trials in which excessive daytime sleepiness was generally an exclusion criterion showed no reduction in cardiovascular event rates. The SERVE-HF trial showed an increase in mortality with use of minute ventilation-triggered adaptive servo-ventilation for CSA in patients with HFrEF. In the ADVENT-HF trial, treating HFrEF patients with coexisting OSA or CSA using peak flow-triggered adaptive servo-ventilation was safe and improved sleep structure and heart failure-related quality of life but did not reduce all-cause mortality or cardiovascular events. More evidence is required to determine whether treating CSA in patients with HFrEF prevents cardiovascular events and improves survival. Presently, the rationale for treating SDB with PAP remains improving sleep structure and quality of life, as well as relieving excessive daytime sleepiness, but not reducing cardiovascular events.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".