Late Breaking Abstract - Effect of peak flow triggered adaptive servo-ventilation (ASVPF) on mortality in heart failure patients with prolonged Cheyne-Stokes respiratory cycle
Bibliographic record
Abstract
Background: In ADVENT-HF, treating obstructive sleep apnea (OSA) and central sleep apnea with Cheyne-Stokes respiration (CSA-CSR) with ASVPF didn’t affect mortality, but with a trend to lower mortality in those with CSA-CSR (HR, 0.74, p=0.25). Patients with CSA-CSR had worse HFrEF, suggesting ASVPF may be more effective in sicker patients. Aims and objectives: As CSA-CSR cycle length (CL) is inversely related to cardiac output, we hypothesized that: 1) in patients with CSA-CSR among controls, mortality will be higher in those with long than short CL; and 2) ASVPF reduces mortality in those with long but not short CL. Methods: In 305 patients with HFrEF and mainly CSA-CSR or mainly OSA with a central apnea-hypopnea index of ≥10 (OSA-CSA) we calculated the mean CL of 20 CSA-CSR cycles. We divided them into short and long CL groups based on median CL of the CSA-CSR group (54.9 sec). We compared mortality between the 2 control groups and effect of ASVPF on mortality in the 2 CL groups. Results: In controls, 62 with long CL (67.8 s.) had higher mortality than 102 with short CL (43.0 s.) (50.0% vs 15.7%, p<0.0001). In patients with long CL, mortality was lower in those on ASVPF than control (HR, 0.56, 95%CI 0.32-0.99, p=0.047), but ASVPF had no effect in the short CL group (HR, 1.08, 95%CI 0.52-2.24, p=0.840). Conclusion: In HFrEF, CSR CL was related to mortality: it was higher in those with untreated CSA-CSR/OSA-CSA with long than short CL. Most importantly, the long CL group assigned to ASVPF experienced lower mortality than the long CL control group, suggesting that this particular sleep apnea phenotype is responsive to ASVPF.
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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) | 0.000 | 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".