Design of the Effect of Adaptive Servo-Ventilation on Survival and Cardiovascular Hospital Admissions in Patients with Heart Failure and Sleep Apnoea: The ADVENT-HF Trial
Bibliographic record
Abstract
INTRODUCTION: Both types of sleep-disordered breathing (SDB), obstructive and central sleep apnoea (OSA and CSA, respectively), are common in patients with heart failure and reduced ejection fraction (HFrEF). In such patients, SDB is associated with increased cardiovascular morbidity and mortality but it remains uncertain whether treating SDB by adaptive servo-ventilation (ASV) in such patients reduces morbidity and mortality. AIM: ADVENT-HF is designed to assess the effects of treating SDB with ASV on morbidity and mortality in patients with HFrEF. METHODS: ADVENT-HF is a multicentre, multinational, randomized, parallel-group, open-label trial with blinded assessment of endpoints of standard medical therapy for HFrEF alone vs. with the addition of ASV in patients with HFrEF and SDB. Patients with a history of HFrEF undergo echocardiography and polysomnography. Those with a left ventricular ejection fraction ≤45% and SDB (apnoea-hypopnoea index ≥15) are eligible. SDB is stratified into OSA with ≥50% of events obstructive or CSA with >50% of events central. Those with OSA must not have excessive daytime sleepiness (Epworth score of ≤10). Patients are then randomized to receive or not receive ASV. The primary outcome is the composite of all-cause mortality, cardiovascular hospital admissions, new-onset atrial fibrillation requiring anti-coagulation but not hospitalization, and delivery of an appropriate discharge from an implantable cardioverter-defibrillator not resulting in hospitalization during a maximum follow-up time of 5 years. CONCLUSION: The ADVENT-HF trial will help to determine whether treating SDB by ASV in patients with HFrEF improves morbidity and mortality.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".