Coronary artery disease (CAD) is the leading cause of death in the United States. Although effective preventative treatments exist, these measures are often underutilized, in part because people don’t know they’re at risk of CAD until it’s too late.
Now, scientists at the Scripps Research Translational Institute have developed a machine learning model that more accurately estimates a patient’s risk of CAD compared to the standard clinical practice, which is based primarily on age. The findings, published in Nature Medicine on April 16, 2025, leveraged data that spanned 10 years. Their new model is personalized and integrates factors including genetics, lifestyle and medical history, enabling clinicians to provide patients with advice and preventative treatment tailored to their individual needs…
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