Nearly 20% of the 3 million breast cancer survivors in the U.S. have cardiovascular disease (CVD). The National Cancer Institute, NHLBI, and professional oncology and cardiology societies have all endorsed the importance of reducing CVD burden in breast cancer survivors through earlier recognition and intervention. Although women diagnosed with stages I to III breast cancer have an excellent prognosis with 5-year relative survival >90%, specific adjuvant therapies have been reported to lead to cardiovascular (CV) events that impair health-related quality of life and/or lead to premature CVD death. CV events including acute myocardial infarction, stroke, and venous thromboembolism have been reported to be associated with adjuvant chemotherapy, biological agents, radiation therapy, and/or hormonal therapies. These treatment-related CV events pose a significant public health problem because they will affect the increasing number of breast cancer survivors? health-related quality of life over a long-life expectancy. Currently, no standard risk model exists to predict the risk of CV events associated with multiple adjuvant breast cancer therapies in the presence of established CV risk factors (such as hypertension, hyperlipidemia, smoking) to inform practice guidelines and promote shared clinical decision-making. Such models can inform women before treatment about the potential risks of CVD from alternative treatment strategies while maintaining the best chances for cancer cure. These models can also help to identify women at highest risk of CVD after therapy who would potentially benefit from earlier and more intensive CV monitoring via routine imaging and/or use of preventive medications to mitigate risk of CV events. To address this gap, our study will create risk prediction models by analyzing a large, demographically heterogeneous cohort of adult women (N=40,500) with newly diagnosed stages I to III invasive breast cancer in real-world health care settings. We will study women diagnosed from 2008-2020 and followed up to 15 years using the comprehensive electronic records of one of the largest health plans in the U.S., Kaiser Permanente.
In Aim 1, we will assess incident CV events (acute myocardial infarction, stroke, heart failure) following adjuvant breast cancer therapies, adjusting for tumor characteristics and CVD risk factors such as age, race/ethnicity, pre-existing CVD, CVD medications (statins, anti-hypertensives, anti- diabetics), hypertension, diabetes, BMI, and smoking. We will then estimate whether the risk of CV events is greater in the breast cancer cohort versus an age, race- matched cancer-free cohort.
In Aim 2, we will create and validate risk prediction models for early (<1 year) and late (up to 15 years) CV events. Our project will be the first to estimate the association of multiple established CVD risk factors with the risk of breast cancer adjuvant treatment-related CV events in a real-world, ethnically and socioeconomically diverse community-based cohort. Our risk prediction models will provide new information to guide evidence-based clinical decision-making concerning adjuvant therapy use for breast cancer and concurrent and post-treatment cardio-oncology care.
There is an urgent need to reduce cardiovascular disease (CVD) burden in breast cancer survivors through earlier recognition and intervention for treatment-related CV toxicity. Currently, no standard risk model exists to predict CV events associated with various cardiotoxic adjuvant breast cancer therapies to promote shared clinical decision-making regarding prevention and management of CVD associated with these therapies. Our study will address this gap by measuring the risk of CV events following adjuvant breast cancer therapies, and we will then use results from this analysis to develop several risk prediction models for early (before 1 year) and late (up to 15 years) CV events.
