Abstract 3145: Mortal Interaction of Sleep Apnea with Ischemic, But Not Non-Ischemic Heart Failure
Bibliographic record
Abstract
Introduction: Past studies showed that in patients with heart failure (HF), sleep apnea (SA) increases mortality risk, but these patients were not characterized on the basis of HF etiology. Hypothesis: Since patients with ischemic HF may suffer greater adverse consequences of SA-related hypoxia and hypertension than those with non-ischemic HF, SA will increase risk of death in patients with ischemic, but not in those with non-ischemic HF. Methods: From 1997 to 2004, consecutive HF patients with ejection fraction (EF) ≤ 45% had sleep studies and were divided into those with SA (apnea-hypopnea index ≥ 15/hr of sleep) and those without SA. They were followed prospectively to determine all-cause mortality rate. Results: Of 218 patients enrolled, follow up data were obtained in 95%. Of these, 87 (40%) had ischemic HF. SA was found in 53% of those with ischemic HF and in 41% of those with non-ischemic HF. 14 patients with obstructive sleep apnea on CPAP therapy were excluded from the analysis. Of the remaining 193 patients, 34 (18%) died during a mean follow up of 32 months. In the non-ischemic HF group, there was no difference in mortality between those with, and those without SA (Figure ). In contrast, in the ischemic group, mortality was significantly higher in those with SA than those without it (18.9 vs. 4.6 deaths/100 patient-years, P = 0.003). After adjusting for age, EF, New York Heart Association class, β-blocker use, and the presence of diabetes using multivariate Cox analysis, SA remained a significant independent risk for death (HR 3.02, 95%CI 1.07– 8.59, P = 0.037). Conclusions: These data show that ischemic etiology identifies those HF patients with SA at increased risk of death.
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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 | 0.002 |
| 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) | 0.012 | 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".