Predicting Cancer Treatments Induced Cardiotoxicity of Breast Cancer Patients

TL;DR

This study develops predictive models using EHR data to assess cardiotoxicity risks in breast cancer patients undergoing various treatments, highlighting the importance of baseline health in interpreting treatment effects.

Contribution

The paper introduces machine learning models for predicting cardiotoxicity in breast cancer patients, accounting for baseline health differences across treatment groups.

Findings

Chemotherapy and targeted therapy are associated with higher cardiotoxicity risk.

Predictive models achieved high AUC scores for various cardiotoxicity outcomes.

Baseline cardiac health significantly influences treatment-related cardiotoxicity risk.

Abstract

Cardiotoxicity induced by the breast cancer treatments (i.e., chemotherapy, targeted therapy and radiation therapy) is a significant problem for breast cancer patients. The cardiotoxicity risk for breast cancer patients receiving different treatments remains unclear. We developed and evaluated risk predictive models for cardiotoxicity in breast cancer patients using EHR data. The AUC scores to predict the CHF, CAD, CM and MI are 0.846, 0.857, 0.858 and 0.804 respectively. After adjusting for baseline differences in cardiovascular health, patients who received chemotherapy or targeted therapy appeared to have higher risk of cardiotoxicity than patients who received radiation therapy. Due to differences in baseline cardiac health across the different breast cancer treatment groups, caution is recommended in interpreting the cardiotoxic effect of these treatments.