O059 Opioid-related central sleep apnea doesn’t harm respiratory and sleep parameters
Bibliographic record
Abstract
Abstract Introduction Opioid-related central sleep apnea (CSA) is increasingly common, but its clinical implications are poorly understood. We analysed the “OpSafe” cohort, a large prospective multicentre study of chronic opioid users, to investigate whether opioid-related CSA worsens key respiratory and sleep outcomes. Methods Participants were chronic opioid users recruited from five pain clinics. They were measured daytime oxygen saturation (SpO2) and Epworth Sleepiness Scale (ESS), followed by in-lab polysomnography. Results A total of 160 participants were divided into three groups: 21 CSA, 66 obstructive sleep apnea (OSA), and 73 no-apnea. CSA participants had ~4 times higher opioid dose and exhibited a ~ 3% lower daytime SpO2 than OSA and no apnea participants. However, during sleep, oxygenation measures were similar between CSA and OSA groups. Compared daytime SpO2 to sleep mean SpO2, OSA decreased significantly (95.1 ± 1.9 vs 93.9 ± 2.1, p=.001), no-apnea moderately dropped (95.9 ± 1.9 vs 95.1 ± 2, p=.05), whereas CSA participants had no change (92.9 ± 3.4 vs 93.5 ± 2, p=.66). CSA event duration was shorter than that of OSA. Sleep architecture and arousal indices in CSA patients were normal except for a reduced REM sleep linked to higher opioid doses. Abnormalities in quantitative EEG power only occurred during REM sleep while CSA was rare. Furthermore, CSA was not a significant predictor of daytime sleepiness, explaining only 1.5% of the variance of ESS. Discussion Our novel findings showed that opioid-related CSA did not worsen key respiratory and sleep outcomes. While it is essential to alleviate respiratory depression, targeted therapies to reduce opioid-related CSA may be unnecessary.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".