Development and validation of a multivariable prediction model for major adverse cardiovascular events after early stage breast cancer: a population-based cohort study
Bibliographic record
Abstract
AIMS: Develop a score to predict the risk of major adverse cardiovascular events (MACE) after early stage breast cancer (EBC) to facilitate personalized decision-making about potentially cardiotoxic treatments and interventions to reduce cardiovascular risk. METHODS AND RESULTS: Using administrative databases, we assembled a cohort of women diagnosed with EBC in Ontario between 2003 and 2014, with follow-up through 2015. Two-thirds of the cohort were used for risk score derivation; the remainder were reserved for its validation. The outcome was a composite of hospitalizations for acute myocardial infarction, unstable angina, transient ischaemic attack, stroke, peripheral vascular disease, heart failure (HF), or cardiovascular death. We developed the score by regressing MACE incidence against candidate predictors in the derivation sample using a Fine-Gray model. Discrimination was assessed in the validation sample using Wolber's c-index for prognostic models with competing risks, while calibration was assessed by comparing predicted and observed MACE incidence. The risk score was derived in 60 294 women and validated in 29 810 women. Age, hypertension, diabetes, ischaemic heart disease, atrial fibrillation, HF, cerebrovascular disease, peripheral vascular disease, chronic obstructive pulmonary disease, and chronic kidney disease were significantly associated with MACE incidence and incorporated into the score. Ten-year MACE incidence was >40-fold higher for patients in the highest score decile compared to the lowest. The c-index was 81.9% (95% confidence interval 80.9-82.9%) at 5 years and 79.8% (78.8-80.8%) at 10 years in the validation cohort, with good agreement between predicted and observed MACE incidence. CONCLUSION: Cardiovascular prognosis after EBC can be estimated using patients' pre-treatment characteristics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".