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 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".