Predictors of New-Onset Heart Failure
Bibliographic record
Abstract
BACKGROUND: About one half of patients with heart failure (HF) have preserved ejection fraction (HFPEF) rather than reduced ejection fraction (HFREF). The differences in risk factors predisposing to the 2 subtypes of HF are poorly understood. We sought to identify clinical predictors of new-onset HF and to explore differences in HFPEF versus HFREF. METHODS AND RESULTS: We studied new-onset HF cases between 1981 and 2008 in Framingham Heart Study participants, classified into HFPEF and HFREF (ejection fraction >45% versus ≤45%). We used Cox multivariable regression to examine predictors of 8-year risk of incident HF and competing-risks analysis to identify predictors that differed between HFPEF and HFREF. Among 6340 participants (60±12 years) with 97 808 person-years of follow-up, 512 developed incident HF. Of 457 participants with left ventricular ejection fraction evaluation at the time of HF diagnosis, 196 (43%) were classified as HFPEF and 261 (56%) as HFREF. Fourteen predictors of overall HF were identified. Older age, diabetes mellitus, and a history of valvular disease predicted both types of HF (P≤0.0025 for all). Higher body mass index, smoking, and atrial fibrillation predicted HFPEF only, whereas male sex, higher total cholesterol, higher heart rate, hypertension, cardiovascular disease, left ventricular hypertrophy, and left bundle-branch block predicted risk of HFREF. CONCLUSIONS: Although multiple risk factors preceded overall HF, distinct clusters of risk factors determine risk for new-onset HFPEF versus HFREF. This knowledge may enable the design of clinical trials of targeted prevention and the introduction of therapeutic strategies for prevention of HF and its 2 major subtypes.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".