Factors Associated With Outcome in Heart Failure With Preserved Ejection Fraction
Bibliographic record
Abstract
BACKGROUND: The determinants of prognosis in patients with heart failure and preserved ejection fraction (HF-PEF) are poorly documented. METHODS AND RESULTS: We evaluated data from 4128 patients in the I-PRESERVE trial (Irbesartan in Heart Failure with Preserved Ejection Fraction Study). Multivariable Cox regression models were developed using 58 baseline demographic, clinical, and biological variables to model the primary outcome of all-cause mortality or cardiovascular hospitalization (1505 events), all-cause mortality (881 events), and HF death or hospitalization (716 events). Log N-terminal pro-B-type natriuretic peptide, age, diabetes mellitus, and previous hospitalization for HF were the most powerful factors associated with the primary outcome and with the HF composite. For all-cause mortality, log N-terminal pro-B-type natriuretic peptide, age, diabetes mellitus, and left ventricular EF were the strongest independent factors. Other independent factors associated with poor outcome included quality of life, a history of chronic obstructive lung disease, log neutrophil count, heart rate, and estimated glomerular filtration rate. The models accurately stratified the actual 3-year rate of outcomes from 8.1% to 59.9% (primary outcome) 2.7% to 36.5% (all-cause mortality), and 2.1% to 38.9% (HF composite) for the lowest to highest septiles of predicted risks. CONCLUSIONS: In a large sample of elderly patients with HF and preserved EF enrolled in I-Preserve, simple clinical, demographic, and biological variables were associated with outcome and identified subgroups at very high and very low risk of events.
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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 | 0.001 |
| 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 | 0.001 |
| 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".