Key result
XGBoost model predicts 1- to 3-year CV events in breast cancer patients with ~0.79 AUC.
Why the study?
The study aimed to develop and validate an interpretable machine learning model predicting 1- to 3-year cardiovascular event risk in breast cancer patients using baseline and treatment variables.
Observational (n=31,878)
No
Effect estimate: AUC 0.790
An interpretable machine learning model (XGBoost) successfully predicted 1- to 3-year cardiovascular risk in breast cancer patients, identifying endocrine therapy, anemia management, and short-term cardiac decline as key predictors.
May support CV risk stratification in breast cancer survivors; hypothesis-generating for ML implementation in cardio-oncology.
Purpose: This study aimed to develop and validate an interpretable machine learning model to predict the 1- to 3-year risk of cardiovascular events in breast cancer patients by integrating baseline and treatment variables, while preliminarily investigating the potential association between short-term cardiac function decline and long-term adverse cardiovascular events. Methods: We analyzed electronic medical records from 31,878 breast cancer patients. A composite cardiovascular event outcome was used. Predictors were selected via a two-step process: removing highly correlated variables (|r|≥0.7) and applying LASSO regression with 10-fold cross-validation, which refined 62 initial variables down to 18. Five models were built and compared using the area under the receiver operating characteristic curve (AUC-ROC). The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results: Among 31,878 breast cancer patients, 3,960 (12.4%) experienced cardiovascular events. The XGBoost model demonstrated the best overall discriminative performance (AUC = 0.790). SHAP analysis identified endocrine therapy, anemia management therapy, and history of cerebrovascular disease as the top three predictors. Crucially, short-term decline in cardiac function was also selected as a significant predictor, supporting its role as a precursor to long-term events. Model robustness was confirmed via sensitivity analysis.
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Wang et al. (2026) conducted an observational in Breast cancer (n=31,878). Antineoplastic treatment and baseline risk factors was evaluated on First occurrence of any cardiovascular event within 36 months of treatment initiation (AUC 0.790). An XGBoost machine learning model integrating baseline and treatment variables predicted 1- to 3-year cardiovascular events in breast cancer patients with an AUC of 0.790.
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