Simple risk models to predict cardiovascular death in patients with stable coronary artery disease
Bibliographic record
Abstract
AIMS: Risk estimation is important to motivate patients to adhere to treatment and to identify those in whom additional treatments may be warranted and expensive treatments might be most cost effective. Our aim was to develop a simple risk model based on readily available risk factors for patients with stable coronary artery disease (CAD). METHODS AND RESULTS: Models were developed in the CLARIFY registry of patients with stable CAD, first incorporating only simple clinical variables and then with the inclusion of assessments of left ventricular function, estimated glomerular filtration rate, and haemoglobin levels. The outcome of cardiovascular death over ∼5 years was analysed using a Cox proportional hazards model. Calibration of the models was assessed in an external study, the CORONOR registry of patients with stable coronary disease. We provide formulae for calculation of the risk score and simple integer points-based versions of the scores with associated look-up risk tables. Only the models based on simple clinical variables provided both good c-statistics (0.74 in CLARIFY and 0.80 or over in CORONOR), with no lack of calibration in the external dataset. CONCLUSION: Our preferred model based on 10 readily available variables [age, diabetes, smoking, heart failure (HF) symptom status and histories of atrial fibrillation or flutter, myocardial infarction, peripheral arterial disease, stroke, percutaneous coronary intervention, and hospitalization for HF] had good discriminatory power and fitted well in an external dataset. STUDY REGISTRATION: The CLARIFY registry is registered in the ISRCTN registry of clinical trials (ISRCTN43070564).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".