Discriminating clinical features of heart failure with preserved vs. reduced ejection fraction in the community
Bibliographic record
Abstract
AIMS: Heart failure (HF) is a major public health burden worldwide. Of patients presenting with HF, 30-55% have a preserved ejection fraction (HFPEF) rather than a reduced ejection fraction (HFREF). Our objective was to examine discriminating clinical features in new-onset HFPEF vs. HFREF. METHODS AND RESULTS: Of 712 participants in the Framingham Heart Study (FHS) hospitalized for new-onset HF between 1981 and 2008 (median age 81 years, 53% female), 46% had HFPEF (EF >45%) and 54% had HFREF (EF ≤45%). In multivariable logistic regression, coronary heart disease (CHD), higher heart rate, higher potassium, left bundle branch block, and ischaemic electrocardiographic changes increased the odds of HFREF; female sex and atrial fibrillation increased the odds of HFPEF. In aggregate, these clinical features predicted HF subtype with good discrimination (c-statistic 0.78). Predictors were examined in the Enhanced Feedback for Effective Cardiac Treatment (EFFECT) study. Of 4436 HF patients (median age 75 years, 47% female), 32% had HFPEF and 68% had HFREF. Distinguishing clinical features were consistent between FHS and EFFECT, with comparable discrimination in EFFECT (c-statistic 0.75). In exploratory analyses examining the traits of the intermediate EF group (EF 35-55%), CHD predisposed to a decrease in EF, whereas other clinical traits showed an overlapping spectrum between HFPEF and HFREF. CONCLUSION: Multiple clinical characteristics at the time of initial HF presentation differed in participants with HFPEF vs. HFREF. While CHD was clearly associated with a lower EF, overlapping characteristics were observed in the middle of the left ventricular EF range spectrum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".