Continuous positive airway pressure increases heart rate variability in heart failure patients with obstructive sleep apnoea
Bibliographic record
Abstract
Patients with heart failure or OSA (obstructive sleep apnoea) have reduced HF-HRV (high-frequency heart rate variability), indicating reduced cardiac vagal modulation, a marker of poor prognosis. CPAP (continuous positive airway pressure) abolishes OSA in patients with heart failure, but effects on daytime HF-HRV have not been determined. We hypothesized that, in patients with heart failure, treatment of coexisting OSA by CPAP would increase morning HF-HRV. In 19 patients with heart failure (left ventricular ejection fraction <45%) and OSA (>/=20 apnoeas and hypopnoeas/h of sleep), HF-HRV was quantified before and 1 month after randomization to a control or CPAP-treated group. In the control group (n=7), there were no changes in HF-HRV over the 1 month study during wakefulness in the morning. In the CPAP-treated group (n=12) HF-HRV increased significantly during wakefulness in the morning [from 2.43+/-0.55 to 2.82+/-0.50 log(ms(2)/Hz); P=0.002] due to an increase in transfer function between changes in lung volume and changes in HF-HRV (92.37+/-96.03 to 219.07+/-177.14 ms/l; P=0.01). In conclusion, treatment of coexisting OSA by nocturnal CPAP in patients with heart failure increases HF-HRV during morning wakefulness, indicating improved vagal modulation of heart rate. This may contribute to improved prognosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".