Effects of auto-servo ventilation on cardiovascular function in patients with congestive heart failure and sleep-disordered breathing – A multicenter randomised controlled trial
Bibliographic record
Abstract
Background: Auto-servo ventilation (ASV) has been shown to effectively suppress sleep-disordered breathing (SDB) in patients with congestive heart failure (CHF). However, the effects of ASV on cardiac function, daytime activity and quality of life are unclear (QOL). Methods: Patients with stable optimised CHF (Left ventricular ejection fraction (LVEF) ≤40%) and SDB (Apnoea-Hypopnoea Index (AHI) ≥20/hour) were randomised to either ASV (BiPAP ASV, Philips Respironics, n=37) or the control-group (n=35). LVEF (primary endpoint of the study, echocardiography), AHI (polysomnography scored in one core lab), B-type natriuretic peptide (NT-proBNP), daytime activity duration (actigraphy) and QOL (SF-36) were assessed at baseline and 3 months. Results: Significantly larger reduction in AHI was observed in the ASV-group (average daily ASV use was 4.47±2.93 hours/day) than in the control group (-39±16 vs. -1±13/hour, p<0.001). Both groups showed similar significant increase of LVEF (+3.4±5 vs. +3.5±6%, p=0.9). In the ASV-group the reduction of NT-proBNP (-360±569 versus +135±625 ng/ml) and the increase of daytime activity duration (+14±52 vs. -24±41 min) was significantly greater than in the control group (p=0.01 for both comparisons). Significant improvement was seen in 3 of 8 domains of the SF-36 questionnaire in the ASV-group. Conclusions: ASV in CHF patients with SDB reduces NT-proBNP levels as a surrogate for improvement of cardiac function. Such changes were not associated with significant changes in LVEF. Patients on ASV improved their activity periods during the day and some domains of QOL.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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) | 0.006 | 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".