Abstract 14810: ASCVD Risk Score vs Machine Learning-Based Algorithm in the Prediction of ASCVD Events in Women With Breast Cancer
Bibliographic record
Abstract
Introduction: Cancer patients may face an elevated risk of developing atherosclerotic cardiovascular disease (ASCVD). Consequently, a conventional pooled cohort equation may not accurately predict cardiovascular (CV) outcomes in this population. Hypothesis: A cancer-specific algorithm is superior to conventional ASCVD risk scores in women with breast cancer (BC). Methods: Women ≥18 years, diagnosed with BC between 2005-2012 at a hybrid academic-community practice (Northeast Ohio, US) were included. A Machine Learning (ML) XGBoost algorithm (with survival modelling), developed using a training subset of the cohort (60% train + 20% test), ranked 40 covariates (including demographic, treatment-related and social determinants of health information) for ASCVD prediction using SHAP (SHapley Additive exPlanations) values. The top 10 ML predictors were transformed in a predictive equation using logistic regression models. This equation was tested in the cohort validation subset (20%), and subsequently compared to the ACC/AHA ASCVD risk score via time-dependent receiver operating characteristic curve. Results: BC women (n=5,687, Table 1) had a median age of 60 (interquartile range 50-71) years, 17% had advanced stage disease (TNM III-IV), 44.2% received chemotherapy, 60% endocrine therapy, 29.2% radiotherapy, and 73.3% surgery, respectively. Of those, 16.7% had a 10-year ASCVD. The ACC/AHA risk score had an area under the curve (AUC)=0.76 and underestimated ASCVD in 4.9% (mean risk=21.3% [95% CI 19.9-22.6] vs. mean predicted risk=16.4% [95% CI 15.2-17.5]). The equation pooled from the top-10 predictors of the ML algorithm (C-index=0.81 [95%CI 0.80-0.82]) achieved an AUC=0.84. Conclusion: Conventional ASCVD risk scores tend to underestimate the risk in women with BC. A cancer-specific model exhibited excellent performance and generated a user-friendly equation for predicting ASCVD risk in this population. Further external validation is needed.
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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 | 0.001 |
| 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 | 0.000 |
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".