0691 Comparison Of Odds Ratio Product And Other Polysomnographic Metrics Among Responders And Non-responders To Upper Airway Stimulation Treatment Of Obstructive Sleep Apnea
Bibliographic record
Abstract
Abstract Introduction Upper airway stimulation (UAS) is a surgical method of treating obstructive sleep apnea (OSA). UAS involves an implantable neuro-stimulator that stimulates the hypoglossal nerve to protrude the tongue during sleep. OSA fails to improve in 22% of patients who receive UAS as defined by a > 50% reduction in Apnea-Hypopnea Index (AHI) and an AHI <20 events/hour. Light sleep may predict UAS failure in that it may limit the stimulus strength that can be applied. The odds ratio product (ORP) is a novel polysomnographic (PSG) metric of sleep depth. We hypothesized that ORP values prior to surgery will be higher (lighter sleep) in non-responders. Having markers that predict surgical success can help reduce unnecessary surgeries. Methods This is a retrospective cohort study of 126 patients (83 responders vs. 43 non-responders) who received UAS implantation for the treatment of OSA. PSG data was obtained from the Stimulation for Apnea Reduction (STAR) trial. Raw baseline PSG data were analyzed and ORP values calculated using Michele Sleep Scoring Software (Cerebra Medical, Winnipeg, CA). In addition, 13 PSG metrics that were considered possibly relevant to surgical outcome were calculated as an exploratory analysis. The measurements included: spindle density, spindle power, spindle frequency, alpha intrusion, Right/Left sleep depth correlation coefficient, respiratory duty cycle, respiratory flow limitation, and arousal intensity. Statistical Analysis: Comparisons between responders and non-responders used parametric t-tests for continuous data and chi-squared or Fisher’s exact tests for categorical data. Statistical significance was based on a Bonferroni-corrected p<0.00357. Results Differences in ORP values and other PSG metrics between responders and non-responders were not statistically significant. Of all PSG metrics only differences in spindle density approached statistical significance (Responders = 2.33 spindles/minute vs Non-Responders = 1.39 spindles/minute, p=0.00360). Conclusion The findings suggest that differences in sleep depth and several other sleep characteristics do not play a significant role in determining response to UAS therapy. Support This project was supported by a Sleep Research Society Career Development Award #023-JP-19
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".