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As part of a collaborative effort, our researchers developed and validated a statistical model to determine a patient’s risk of heart failure or cardiomyopathy following breast cancer treatment.
Survival has long been the primary goal of breast cancer treatment. With early detection and targeted therapies, the five-year relative survival rate for early-stage (localized) invasive breast cancer is now above 99%. Yet as more women live longer, we’ve learned that some breast cancer treatments can raise their risk of developing a heart condition later in life.
Breakthrough research from MHRI, Georgetown University, and Kaiser Permanente Southern California provides the first standard risk stratification that could help providers personalize breast cancer treatment while minimizing patients’ future cardiovascular risk.
In the course of our research, which was recently published in JAMA Oncology, we developed and tested a statistical model that can identify a patient’s risk of developing a heart condition before they even begin cancer treatment.
MedStar Health has long recognized the importance of heart health during cancer care. Our dedicated Cardio-Oncology Program, the first of its kind in the region, is designed to protect patients throughout their treatment journey.
This collaboration between MedStar Heart and Vascular Institute and Georgetown Lombardi Comprehensive Cancer Center brings cardiologists and oncologists together to provide proactive risk assessment and real-time imaging to monitor heart function and treat any potential issues early on. Our research supports this vital work to safeguard heart health for patients with breast cancer.
Seeing the future with data from the past.
Studies have shown that people who survive breast cancer face a higher risk of cardiovascular
conditions than those who have never had cancer. Incidence of rhythm disorders, heart failure, and cardiomyopathy is among the leading long-term health risks for cancer survivors.
Our study set out to solve this complex problem: How can we predict a patient’s heart health a decade into the future at the moment of a breast cancer diagnosis?
We found the answer in data, and lots of it. In partnership with Georgetown and Kaiser Permanente Southern California, we analyzed the anonymous health data of more than 26,000 women diagnosed with early-stage invasive breast cancer between 2008 and 2020.
Predicting risk isn’t just about the type of chemotherapy or radiation therapy a patient receives. To get a real sense of risk, we examined each patient’s health history data before breast cancer treatment and their heart health in the years after treatment.
Using advanced statistical modeling, we recognized powerful predictors that could lead to heart conditions, such as:
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Clinical factors: Age at diagnosis, cancer stage, and treatment specifics
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Health history: Pre-existing conditions such as diabetes, high blood pressure, obesity, kidney disease, or lifestyle considerations like smoking
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Social determinants of health: Factors such as race and ethnicity, neighborhood, economic status, and access to education and healthcare can have a significant influence on health outcomes
Accurate risk assessment empowers personalized care.
The demonstrated accuracy in our study enabled us to use the model to separate patients into clear categories of risk:
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Low-risk group: Participants identified as part of the low-risk group had only a 1.7% chance of developing heart failure or cardiomyopathy over 10 years following breast cancer treatment
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High-risk group: People who shared factors that placed them in this group had a 19.4% chance of developing cardiomyopathy or heart failure over 10 years, a risk 12 times higher than the low risk group
Understanding when a patient fits into these significantly different risk groups could enable clinicians to provide better, more personalized care.
Related: Read “How Early Detection and Leading Breast Cancer Treatment Save Lives.”
Enabling precise, equitable care.
Today, heart monitoring during cancer treatment is often determined by broad, drug-based guidelines. Our research shows that two people receiving the same chemotherapy drug can have very different cardiovascular risks and follow-up heart imaging or exams.
This model enables oncologists to tailor a patient’s care to their individual risk level:
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High-risk patients: A cardiologist can join the care team right away, and heart-protective medications, such as beta-blockers, may be considered alongside chemotherapy. The team may choose a treatment strategy that reduces the impact on the heart.
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Low-risk patients: This risk assessment can offer patients peace of mind, reducing the stress, time, and expense of unnecessary frequent heart monitoring.
Because our model included racial and socioeconomic data as key variables, we could account for these factors when determining risk. Creating a more precise and personalized assessment can improve the chances of key monitoring for vulnerable patients.
Related: Read: “Patient-Centered Teamwork: The Power of Multidisciplinary Heart Care.”
Innovation that makes a difference.
This exciting publication is just the beginning for our new model. Our next steps include validating this research in other health systems to ensure this model works for everyone.
Ultimately, this complex statistical model could become a simple, trustworthy AI tool. Imagine an oncologist reviewing a patient’s health record before cancer treatment. Providing a clear “Heart Risk Score” could help guide them to the safest treatment plan for each patient.
At MedStar Health Research Institute, we’re committed to practical innovation. This is work that can change the way care is delivered, moving toward a future where we treat cancer while providing personalized care to protect survivors’ hearts.
