Abstract 11958: Predicting Heart Failure With Preserved and Reduced Ejection Fraction: The International Collaboration on Heart Failure Subtypes
Bibliographic record
Abstract
Introduction: Heart failure (HF) is a major growing public health burden, and preventive strategies focused on at-risk individuals are needed. Recent initiatives have advocated for early prevention and aggressive treatment in ACC/AHA stage A/B HF, but prior HF risk prediction models remain poorly defined and validated. Moreover, risk factors for HF-specific subtypes have not yet been examined. Methods: We developed and validated separate HF risk prediction models for preserved and reduced ejection fraction (HFPEF, HFREF) in four community-based prospective cohorts (FHS, CHS, PREVEND, MESA). Fine-Gray proportional sub-distribution hazards models were used to account for competing risks (death, other HF subtype, and unclassified HF). FHS, CHS, and PREVEND samples were combined and a 2:1 random split was used for derivation and internal validation. MESA served for external validation. Results: There were 982 incident HFPEF and 909 HFREF events among 28,820 participants during follow-up (median 12 years). We created a HFPEF-specific model which included age, sex, systolic blood pressure, body mass index, hypertension treatment, and prior myocardial infarction; it had good discrimination in derivation (c-statistic 0.80, 95% CI 0.78-0.82) and validation samples (internal 0.79, 95% CI 0.77-0.82; external 0.76, 95% CI 0.71-0.80). The HFREF-specific model added smoking, left ventricular hypertrophy (LVH), left bundle branch block (LBBB), and diabetes; it had good discrimination in derivation (c-statistic 0.82, 95% CI 0.80-0.84) and validation samples (internal 0.80, 95% CI 0.78-0.83; external 0.76, 95% CI 0.71-0.80). Age had a greater effect on HFPEF risk, whereas male sex, LVH, LBBB, previous myocardial infarction, and smoking had greater effects on HFREF risk (P for comparison ≤ 0.02 for all). Conclusions: We describe and validate risk prediction models that are distinct for HF subtypes, and we demonstrate good discrimination in four community-based cohorts. Some risk factors differed in HFPEF vs HFREF, supporting distinct pathogenesis between HF subtypes. Studies are needed to examine the clinical utility of risk models, with the ultimate goal of targeted preventive strategies.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".