Sleep apnoea in acute heart failure: fluid in flux
Bibliographic record
Abstract
This editorial refers to ‘Sleep-disordered breathing and post-discharge mortality in patients with acute heart failure’†, by R. Khayat et al., on page 1463. The present management of acute heart failure (AHF) suffers from a paucity of evidence-based treatment.1 Could this be because the pathophysiology that places such patients at greatest risk of death has yet to be identified or addressed? In the present issue of the journal, Khayat et al.2 report the findings of a prospective cohort study involving patients admitted to their hospital with the primary diagnosis of AHF between January 2007 and December 2010. Stable patients with left ventricular ejection fraction (LVEF) ≤45% were offered in-hospital overnight cardiopulmonary monitoring but without polysomnographic documentation of sleep. Of 1375 such studies, 1117 yielded interpretable data. Only 36 of those patients had de novo AHF; the rest had previously documented chronic heart failure with reduced ejection fraction (HFrEF). Applying a calculated apnoea–hypopnoea index (AHI) of 15 ≥ events/h, Khayat et al.2 categorized 525 patients (47%) as having primarily central sleep apnoea (CSA) and 344 (30%) obstructive sleep apnoea (OSA). The AHI was <15/h in only 22% (248) of these patients. The 1096 who survived to hospital discharge were followed for a median of 3 years. Post-discharge mortality rates were determined from vital statistic databases. In multivariable analyses, adjusting for known covariates of risk, both CSA and OSA were associated independently with increased mortality [hazard ratio for CSA, 1.61, 95% confidence interval (CI) 1.1–2.4, P = 0.02; for OSA, 1.53, 95% CI 1.1–2.2, P = 0.02], whereas mortality risk was not increased in those who were offered and who accepted non-randomized and unblinded sleep apnoea treatment.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".