Relationship between sleep apnoea and mortality in patients with ischaemic heart failure
Bibliographic record
Abstract
OBJECTIVE: To determine whether the influence of sleep apnoea (SA) on the risk of death differs in patients with ischaemic and in those with non-ischaemic heart failure (HF). DESIGN: Prospective observational study. PATIENTS: Consecutive patients with HF with left ventricular ejection fraction < or =45% newly referred to the HF clinic between 1 September 1997 and 1 December 2004. MAIN OUTCOME MEASURES: Patients underwent sleep studies and were divided into those with moderate to severe SA (apnoea-hypopnoea index > or =15/h of sleep) and those with mild to no SA (apnoea-hypopnoea index <15/h of sleep). They were followed up for a mean of 32 months to determine all-cause mortality rate. RESULTS: Of 193 patients, 34 (18%) died. In the ischaemic group, mortality risk adjusted for confounding factors was significantly higher in those with SA than in those without it (18.9 vs 4.6 deaths/100 patient-years, hazards ratio (HR) = 3.03, 95% CI 1.04 to 8.84, p = 0.043). In contrast, in the non-ischaemic HF group, there was no difference in adjusted mortality risk between those with, and those without, SA (3.9 vs 4.0 deaths/100 patient-years, p = 0.929). CONCLUSIONS: In patients with HF, the presence of SA is independently associated with an increased risk of death in those with ischaemic, but not in those with non-ischaemic, aetiology. These findings suggest that patients with ischaemic cardiomyopathy are more susceptible to the adverse haemodynamic, autonomic and inflammatory consequences of SA than are those with non-ischaemic cardiomyopathy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".