What You Should Know:
– Tempus, an $8B precision medicine company, announced a collaborative study with Geisinger focusing on artificial intelligence (AI) model that can accurately identify patients at increased risk of undiagnosed structural heart disease (SHD). The study addresses a critical diagnostic gap – SHD is a progressive disease that affects the valves, walls, chambers, and muscles of the heart, and causes debilitating symptoms or death, yet many patients with the disease go undiagnosed.
ECG-Based AI Model Can Predict Undiagnosed Structural Heart Disease
Structural heart disease (SHD) is a group of conditions that adversely affect the valves, walls, chambers, or muscles of the heart. SHD is typically a progressive disease that causes a variety of debilitating symptoms or death, making it important to diagnose and treat patients early to prevent these poor outcomes. However, many patients with the disease are undiagnosed.
The team of data scientists and medical researchers used 2.2 million ECGs from more than 480,000 patients over 37 years of patient care at Geisinger to train a deep neural network—a specialized type of AI model—to predict who, among patients without a prior history of SHD, would develop clinically significant disease that could benefit from guideline-directed monitoring or treatment. Overall, the study found that the model achieved excellent performance, exceeding the performance of any previously published model predicting any single disease. The findings show that clinicians using this model could find more diseases with fewer diagnostic studies.
“Structural heart disease carries a high burden of morbidity and mortality, and this model can be both actionable and practical for identifying undiagnosed patients in clinical practice,” said Joel Dudley, Ph.D., chief scientific officer at Tempus. “Our two teams are working to find new ways of applying AI to predict heart disease before it reaches a severe stage of irreversible debilitation for patients, and the rECHOmmend study builds on that foundational work.”
This study expands the AI-based cardiology research the Tempus and Geisinger teams have pursued in recent years, starting with a Nature Medicine paper that demonstrated that AI can predict mortality directly from ECG data even in the large subset of ECGs interpreted by physicians as normal. In 2021, a jointly created AI model that can predict risk of new atrial fibrillation (AF) and AF-related stroke was published in Circulation and was later granted Breakthrough Device Designation by the U.S. Food & Drug Administration.