Incident Heart Failure Prediction in the Elderly
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Despite the rising heart failure (HF) incidence and aging United States population, there are no validated prediction models for incident HF in the elderly. We sought to develop a new prediction model for 5-year risk of incident HF among older persons. METHODS AND RESULTS: Proportional hazards models were used to assess independent predictors of incident HF, defined as hospitalization for new-onset HF, in 2935 elderly participants without baseline HF enrolled in the Health ABC study (age, 73.6 +/- 2.9 years, 47.9% males, 58.6% whites). A prediction equation was developed and internally validated by bootstrapping, allowing the development of a 5-year risk score. Incident HF developed in 258 (8.8%) participants during 6.5 +/- 1.8 years of follow-up. Independent predictors of incident HF included age, history of coronary disease and smoking, baseline systolic blood pressure and heart rate, serum glucose, creatinine, and albumin levels, and left ventricular hypertrophy. The Health ABC HF model had a c-statistic of 0.73 in the derivation dataset, 0.72 by internal validation (optimism-corrected), and good calibration (goodness-of-fit 2 6.24, P=0.621). A simple point score was created to predict incident HF risk into 4 risk groups corresponding to <5%, 5% to 10%, 10% to 20%, and >20% 5-year risk. The actual 5-year incident HF rates in these groups were 2.9%, 5.7%, 13.3%, and 36.8%, respectively. CONCLUSION: The Health ABC HF prediction model uses common clinical variables to predict incident HF risk in the elderly, an approach that may be used to target and treat high-risk individuals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it