Predictors of Long‐term Clinical Endpoints in Patients With Refractory Angina
Bibliographic record
Abstract
BACKGROUND: Clinical outcomes in patients with refractory angina (RA) are poorly characterized and variably described. Using the Duke Database for Cardiovascular Disease (DDCD), we explored characteristics that drive clinical endpoints in patients with class II to IV angina stabilized on medical therapy. METHODS AND RESULTS: We explored clinical endpoints and associated costs of patients who underwent catheterization at Duke University Medical Center from 1997 to 2010 for evaluation of coronary artery disease (CAD) and were found to have advanced CAD ineligible for additional revascularization, and were clinically stable for a minimum of 60 days. Of 77 257 cardiac catheterizations performed, 1908 patients met entry criteria. The 3-year incidence of death; cardiac rehospitalization; and a composite of death, myocardial infarction, stroke, cardiac rehospitalization, and revascularization were 13.0%, 43.5%, and 52.2%, respectively. Predictors of mortality included age, ejection fraction (EF), low body mass index, multivessel CAD, low heart rate, diabetes, diastolic blood pressure, history of coronary artery bypass graft surgery, cigarette smoking, history of congestive heart failure (CHF), and race. Multivessel CAD, EF<45%, and history of CHF increased risk of mortality; angina class and prior revascularization did not. Total rehospitalization costs over a 3-year period per patient were $10 185 (95% CI 8458, 11912) in 2012 US dollars. CONCLUSIONS: Clinically stable patients with RA who are medically managed have a modest mortality, but a high incidence of hospitalization and resource use over 3 years. These findings point to the need for novel therapies aimed at symptom mitigation in this population and their potential impact on health care utilization and costs.
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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 | 0.003 |
| 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 | 0.001 | 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".