Dose–response relationship of sleep apnea therapy and healthcare use in patients with comorbidities
Bibliographic record
Abstract
STUDY OBJECTIVES: Obstructive sleep apnea (OSA) has complex interactive relationships with several other conditions. Previous research suggests that consistent adherence to positive airway pressure (PAP) therapy can reduce healthcare resource utilization (HCRU) in comorbid populations. We hypothesized that PAP therapy use would be associated with dose-dependent improvements in HCRU among patients with OSA and comorbid chronic obstructive pulmonary disease (COPD), type 2 diabetes, depression, heart failure, or atrial fibrillation. METHODS: We analyzed a linked dataset of medical/pharmacy claims data and objective PAP usage data for adults with newly diagnosed OSA between January 2015 and May 2021. Comorbidities were defined by at least two healthcare encounters or at least one hospitalization with the relevant diagnosis in the year before PAP initiation (index). HCRU outcomes included all-cause hospitalizations and emergency room (ER) visits over 12 and 24 months post-index. RESULTS: Among 377 830 patients with OSA (mean age: 51.7 years; 57.7% male), 6.6% had COPD, 18.7% type 2 diabetes, 16.5% depression, 4.2% heart failure, and 5.2% atrial fibrillation. Across all comorbidity cohorts, PAP usage was associated with a dose-dependent reduction in HCRU over 12 and 24 months. Risk-adjusted analyses showed HCRU benefits beginning at 2 to less than 4 hours of average nightly PAP use. Each additional hour of use was associated with a 4.1%-6.2% reduction in hospitalizations and ER visits (all analyses p < .0001). CONCLUSIONS: PAP therapy use is associated with dose-dependent reductions in HCRU among patients with OSA and major comorbidities. These findings may support data-driven reimbursement policies and highlight the value of treating OSA in complex patient populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".