Model Predicts Heart Age, a Potential Indicator for Heart Disease
Tool Correlates Health Risks for Heart Disease, No. 1 Cause of Death in U.S.
Age is just a number. Or is it?
Age is the leading risk factor for disease and disability. What’s more: Aging affects everyone differently. Although your chorological age refers to the number of years you’ve been alive, your biological age can reflect the age estimated by your physical health, which is influenced by genetics, lifestyle and diet.
Clinicians and scientists are developing innovative tools to better understand how aging affects different organs and what patients can do to support healthy aging. Biological age can also be another clinical indicator of a patient’s likelihood of developing many diseases, including cardiovascular disease. A fast-aging heart raises the risk of heart disease.
Figure 1: Measuring one’s biological age can help understand how aging affects different organs, including the heart.
In collaboration with Balijash Singh Cheema, MD, cardiologist and medical director of Northwestern Medicine Human Longevity Clinic, the Machine Learning and Artificial Intelligence (AI) team at Northwestern Medicine developed a deep learning model that uses electrocardiogram (EKG) to predict a patient’s cardiovascular age. This model helps clinicians evaluate health risks potentially linked to someone’s cardiovascular age and provide follow-up care.
How the Tool Helps Predict the Heart’s Age
The model compares a patient’s EKG pattern to thousands of others to render an estimated cardiovascular age. This helps identify if a patient’s heart is functionally older than their chronological age. The EKG-Age algorithm is one component of a protocol of tests and measurements that help clinicians understand a patient’s general well-being relative to their chronological age.
“The measures that we perform help us precisely determine the cardiovascular age in people that go through the laboratory,” says Cardiologist Douglas E. Vaughan, MD, director of Potoscnak Longevity Institute at Northwestern Medicine and the Human Longevity Clinic. “These tools include measuring arterial stiffness, endothelial function, heart rate variability and AI-based tools for analyzing retinal photographs and 12-lead electrocardiograms.”
Care teams then share the model’s predictions with patients at the Human Longevity Clinic, helping them better understand their personal gap between chronological age and biological age.
The measures that we perform help us precisely determine the cardiovascular age in people that go through the laboratory...
Douglas E. Vaughan, MD Director of Potoscnak Longevity Institute at Northwestern Medicine and the Human Longevity Clinic
The Importance of Knowing Heart Age
This model and tools like it are helping clinicians better determine patients’ health risk so they can address conditions early when interventions are more effective.
The model is just one tool paving the way for future health care. AI-powered tests could become part of routine checkups.
Discover how Northwestern Medicine is reimagining cardiovascular care with an AI model that examines lab results, symptoms and medications to assess someone’s risk of heart failure.
