Ventilatory Burden Predicts Change in Sleepiness Following Positive Airway Pressure in Sleep Apnea
Bibliographic record
Abstract
Abstract Rationale Excessive daytime sleepiness, an important symptom of obstructive sleep apnea (OSA), is commonly quantified using the Epworth Sleepiness Scale score (ESS). Baseline OSA severity measures (ventilatory burden, flow limitation, and hypoxemia) provide insights into OSA pathophysiology and could predict changes in sleepiness (i.e. change-in-ESS) following continuous positive airway pressure (CPAP) treatment. Objectives We hypothesized that change-in-ESS following CPAP treatment can be predicted from baseline polysomnography. Methods Associations between OSA severity measures and ESS were evaluated in 2332 participants, adjusting for age, sex, BMI, and total sleep time. Change-in-ESS prediction was evaluated using 213 CPAP treatment studies (HomePAP, BestAIR, and ABC) in three steps: severity measures were compared (adjusted regression, n =64), a prediction model was developed using baseline ventilatory burden and baseline ESS ( n =139), and then evaluated in holdout participants ( n =74). Measurements and Main Results In cross-sectional analysis, ESS was associated with ventilatory burden (0.45 points/SD; 95% CI 0.23−0.67), hypoxic burden (0.39; 0.17−0.62), the apnea-hypopnea index (AHI) (0.36; 0.14−0.59), and flow limitation severity (0.22; 0.01−0.43). Comparison analysis revealed that change-in-ESS was most strongly associated with baseline ventilatory burden (-1.08 points/SD; -2.13 to -0.05) and baseline ESS (-2.75; -3.83 to -1.69); the AHI association was weaker (-0.97; -2.01−0.05). Predicted change-in-ESS and actual change-in-ESS were correlated in holdout participants (adjusted R² =0.313); median [IQR] actual change-in-ESS of predicted responders (≥2-point ESS improvement, n =54, 73.0%) was -5.0 [-10.0 to -2.0] and non-responders was 0.0 [-1.0−1.0] ( P <0.001). Conclusions Baseline ventilatory burden and baseline ESS were independently associated with change-in-ESS and could be used together to inform clinicians whether CPAP treatment will likely improve a patient’s sleepiness.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".