Peak airflow-triggered adaptive servo-ventilation improves sleep structure of patients with heart failure and sleep apnea
Bibliographic record
Abstract
Introduction: The randomized Adaptive Servo-Ventilation for Therapy of Sleep Apnea in Heart Failure (ADVENT-HF) trial found that peak airflow-triggered adaptive servo-ventilation (ASVPF) improved patients’ quality of life and reduced daytime sleepiness. Aims and objectives: We hypothesized that improved sleep quality contributed to these benefits. Methods: After baseline polysomnography (PSG), patients with heart failure and left ventricular ejection fraction ≤45% (HFrEF) and apnea-hypopnea index (AHI) ≥15 events/h were randomized to control or ASVPFand 1 month later had a repeat PSG. Changes in sleep structure between the ASVPF and control groups were compared. Results: 375 patients were allocated to control and 356 to ASVPF. Baseline AHI and sleep structure were comparable in the control and ASVPF groups. Compared to the control group, ASVPF reduced the AHI and arousal index and increased mean and minimum arterial oxygen saturation (P<0.001 for all, see Figure). Stage N1 sleep decreased, and Stages N3 and REM sleep increased (P<0.001 for all). Conclusion: These data are the first to demonstrate that, in patients with HFrEF, alleviation of sleep apnea by ASVPF improves sleep by reducing arousals and redistributing sleep from lighter to deeper stages. Such improvements likely contributed to the symptomatic improvement reported by ASVPF-treated patients in ADVENT-HF.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".