OSA symptom subtypes and hypoxic burden independently predict distinct cardiovascular outcomes
Bibliographic record
Abstract
Study objectives Studies on obstructive sleep apnoea (OSA) have identified clinically relevant symptom-based subtypes and novel OSA-specific nocturnal hypoxic measures. Both traits are individually associated with cardiovascular outcomes, but evidence about their independent or shared effects is unknown. This study investigated the simultaneous contributions of OSA symptom subtypes and hypoxic burden (HB) on incident cardiovascular outcomes. Methods Sleep Heart Health Study participants with high-quality oxygen saturation, apnoea–hypopnea index (AHI) and symptom data were included. Participants with OSA (AHI ≥5 events·h −1 ) were grouped into symptom subtypes. HB was calculated from respiratory event-related hypoxia. Cox proportional hazards models assessed whether symptom subtypes and/or HB were independently associated with cardiovascular mortality and major adverse cardiovascular events (MACE). Results 4396 participants free of cardiovascular disease were analysed, with median follow-up >11 years. Higher HB was associated with worse cardiovascular mortality (HR (95% CI): 1.63 (1.13–2.35); p=0.009) independently of symptom subtypes. Compared to those without OSA, the excessively sleepy OSA subtype had higher risk of incident MACE (1.62 (1.23–2.15); p<0.001), independently of HB. Among participants with moderate–severe OSA (AHI ≥15 events·h −1 ), excessively sleepy participants had higher risk of cardiovascular end-points compared to other subtypes, but HB was not associated with cardiovascular mortality or MACE risk. Conclusion OSA symptom subtypes and HB are independently associated with MACE and cardiovascular mortality, respectively. Thus, both are important for understanding OSA-related cardiovascular risk. Future studies using clinical samples including OSA therapy information that incorporate symptom subtypes and novel biomarkers, such as HB, could improve predictive models for cardiovascular disease risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".