Sleepiness and Sleep in Patients With Both Systolic Heart Failure and Obstructive Sleep Apnea
Bibliographic record
Abstract
BACKGROUND: Adverse effects of obstructive sleep apnea (OSA), including sleep deprivation, can contribute to the progression of heart failure. The usual indication to diagnose and treat sleep apnea is subjective sleepiness. Previous studies suggest that patients with both heart failure and obstructive sleep apnea often do not complain of sleepiness, albeit their sleep time may be reduced. Therefore, we tested the hypothesis that patients with heart failure have less sleepiness and sleep less compared with subjects without heart failure for a given severity of OSA. METHODS: Sleepiness assessed with the Epworth Sleepiness Scale and sleep structure measured with polysomnography were compared among 155 consecutive patients with heart failure and from a random community sample (n = 1139) according to categories of the apnea-hypopnea index (<5, no OSA; 5-14, mild OSA; and > or =15, moderate to severe OSA). RESULTS: Compared with the community sample, for any given severity of OSA, patients with heart failure had lower mean +/- SE Epworth Sleepiness Scale scores (7.1 +/- 0.4 vs 8.3 +/- 0.2 [P = .005]; 6.7 +/- 0.7 vs 9.2 +/- 0.3 [P < .001]; and 7.8 +/- 0.7 vs 9.8 +/- 0.4 [P = .01]), indicating less sleepiness despite sleeping less (total sleep time mean +/- SE [in minutes]: 306 +/- 7 vs 384 +/- 2, 295 +/- 19 vs 384 +/- 5, and 285 +/- 13 vs 359 +/- 7 for no, mild, and moderate to severe OSA, respectively; P < .001 for all comparisons). CONCLUSIONS: Patients with heart failure have less subjective daytime sleepiness compared with individuals from a community sample, despite significantly reduced sleep time, whether or not they have OSA. In patients with heart failure, the absence of subjective sleepiness is not a reliable means of ruling out 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".