Predictors and Outcomes of Heart Failure with Mid-Range Ejection Fraction
Bibliographic record
Abstract
AIMS: While heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF) are well described, determinants and outcomes of heart failure with mid-range ejection fraction (HFmrEF) remain unclear. We sought to examine clinical and biochemical predictors of incident HFmrEF in the community. METHODS AND RESULTS: We pooled data from four community-based longitudinal cohorts, with ascertainment of new heart failure (HF) classified into HFmrEF [ejection fraction (EF) 41-49%], HFpEF (EF ≥50%), and HFrEF (EF ≤40%). Predictors of incident HF subtypes were assessed using multivariable Cox models. Among 28 820 participants free of HF followed for a median of 12 years, there were 200 new HFmrEF cases, compared with 811 HFpEF and 1048 HFrEF. Clinical predictors of HFmrEF included age, male sex, systolic blood pressure, diabetes mellitus, and prior myocardial infarction (multivariable adjusted P ≤ 0.003 for all). Biomarkers that predicted HFmrEF included natriuretic peptides, cystatin-C, and high-sensitivity troponin (P ≤ 0.0004 for all). Natriuretic peptides were stronger predictors of HFrEF [hazard ratio (HR) 2.00 per 1 standard deviation increase, 95% confidence interval (CI) 1.81-2.20] than of HFmrEF (HR 1.51, 95% CI 1.20-1.90, P = 0.01 for difference), and did not differ in their association with incident HFmrEF and HFpEF (HR 1.56, 95% CI 1.41-1.73, P = 0.68 for difference). All-cause mortality following the onset of HFmrEF was worse than that of HFpEF (50 vs. 39 events per 1000 person-years, P = 0.02), but comparable to that of HFrEF (46 events per 1000 person-years, P = 0.78). CONCLUSIONS: We found overlap in predictors of incident HFmrEF with other HF subtypes. In contrast, mortality risk after HFmrEF was worse than HFpEF, and similar to HFrEF.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".