Relation of Disease Pathogenesis and Risk Factors to Heart Failure With Preserved or Reduced Ejection Fraction
Bibliographic record
Abstract
BACKGROUND: The contributions of risk factors and disease pathogenesis to heart failure with preserved ejection fraction (HFPEF) versus heart failure with reduced ejection fraction (HFREF) have not been fully explored. METHODS AND RESULTS: We examined clinical characteristics and risk factors at time of heart failure onset and long-term survival in Framingham Heart Study participants according to left ventricular ejection fraction < or =45% (n=314; 59%) versus >45% (n=220; 41%) and hierarchical causal classification. Heart failure was attributed to coronary heart disease in 278 participants (52%), valvular heart disease in 42 (8%), hypertension in 140 (26%), or other/unknown causes in 74 (14%). Multivariable predictors of HFPEF (versus HFREF) included elevated systolic blood pressure (odds ratio [OR]=1.13 per 10 mm Hg; 95% confidence interval [CI], 1.04 to 1.22), atrial fibrillation (OR=4.23; 95% CI, 2.38 to 7.52), and female sex (OR=2.29; 95% CI, 1.35 to 3.90). Conversely, prior myocardial infarction (OR=0.32; 95% CI, 0.19 to 0.53) and left bundle-branch block QRS morphology (OR=0.21; 95% CI, 0.10 to 0.46) reduced the odds of HFPEF. Long-term prognosis was grim, with a median survival of 2.1 years (5-year mortality rate, 74%), and was equally poor in men and women with HFREF or HFPEF. CONCLUSIONS: Among community patients with new-onset heart failure, there are differences in causes and time-of-onset clinical characteristics between those with HFPEF versus HFREF. In people with HFREF, mortality is increased when coronary heart disease is the underlying cause. These findings suggest that heart failure with reduced left ventricular systolic function and heart failure with preserved left ventricular systolic function are partially distinct entities, with potentially different approaches to early detection and prevention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".