Predictors of Heart Failure in Patients With Stable Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a disease commonly associated with coronary artery disease. Most risk models for HF development have focused on patients with acute myocardial infarction. The Prevention of Events with Angiotensin-Converting Enzyme Inhibition population enabled the development of a risk model to predict HF in patients with stable coronary artery disease and preserved ejection fraction. METHODS AND RESULTS: In the 8290, Prevention of Events with Angiotensin-Converting Enzyme Inhibition patients without preexisting HF, new-onset HF hospitalizations, and fatal HF were assessed over a median follow-up of 4.8 years. Covariates were evaluated and maintained in the Cox regression multivariable model using backward selection if P<0.05. A risk score was developed and converted to an integer-based scoring system. Among the Prevention of Events with Angiotensin-Converting Enzyme Inhibition population (age, 64+/-8; female, 18%; prior myocardial infarction, 55%), there were 268 cases of fatal and nonfatal HF. Twelve characteristics were associated with increased risk of HF along with several baseline medications, including older age, history of hypertension, and diabetes. Randomization to trandolapril independently reduced the risk of HF. There was no interaction between trandolapril treatment and other risk factors for HF. The risk score (range, 0 to 21) demonstrated excellent discriminatory power (c-statistic 0.80). Risk of HF ranged from 1.75% in patients with a risk score of 0% to 33% in patients with risk score >or=16. CONCLUSIONS: Among patients with stable coronary artery disease and preserved ejection fraction, traditional and newer factors were independently associated with increased risk of HF. Trandolopril decreased the risk of HF in these patients with preserved ejection fraction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".