A Systematic Assessment of Causes of Death After Heart Failure Onset in the Community
Bibliographic record
Abstract
BACKGROUND: The high mortality rate in patients with heart failure (HF) is influenced by presence of multiple comorbidities. Data are limited on the relative contributions of cardiovascular versus noncardiovascular diseases to death in individuals with HF in the community. METHODS AND RESULTS: We examined the incidence and predictors of cardiovascular versus noncardiovascular death in participants with HF in the Framingham Heart Study. Underlying, immediate, and contributing causes of death (3 key elements of the World Health Organization classification) were adjudicated by a 3-physician review panel. During 1971 to 2004, 1025 participants with HF died (499 men, mean [SD] age at death 79 [11] years), including 463 participants with left ventricular ejection fraction (LVEF) data. Cardiovascular disease was the cause of death in 66.1% overall. Stratified by LVEF, cardiovascular deaths occurred in 44.5% and 69.9% of those with preserved and reduced LVEF, respectively. Presence of reduced LVEF increased the risk of cardiovascular death, with odds ratios of 3.16 (95% confidence interval [CI], 1.73 to 5.78) in men and 2.39 (95% CI, 1.39 to 4.08) in women. Prior myocardial infarction was associated with increased cardiovascular death in women with HF (odds ratio, 1.87; 95% CI, 1.10 to 3.16) but not in men. The risk of cardiovascular disease death decreased in women (odds ratio after 1980, 0.41; 95% CI, 0.24 to 0.69) and men (odds ratio, 0.66; 95% CI, 0.41 to 1.07, P=0.095) with HF over time. Infections and kidney disease emerged as key immediate and contributing causes of death, respectively. CONCLUSIONS: Individuals with HF in the community often experience cardiovascular death, but noncardiovascular disease also contributes significantly especially among those with preserved LVEF.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.027 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".