Millions of people suffer from undiagnosed heart disease, leading to debilitating outcomes, such as stroke, that can potentially be avoided with early diagnosis. Now, a new artificial intelligence (AI)-enabled technology aims to help clinicians find these patients.

Tempus (Chicago, IL, USA) has launched a multi-center study titled, “Electrocardiogram-based Artificial Intelligence-Assisted Detection of Heart Disease,” or ECG-AID. The study aims to evaluate the impact of the company’s investigational, AI-enabled, predictive tests in cardiology and focuses on finding patients at high risk of developing atrial fibrillation (AFib) or any of seven structural heart diseases (SHD), including diseases of the mitral, aortic and tricuspid valves, abnormal heart function, and abnormal heart thickening. The new algorithmic tests created by Tempus use a type of AI called a deep neural network to automatically interpret a 12-lead ECG - a widely used clinical test that measures the electrical signals of the heart - to identify patients at high risk of developing heart diseases. The algorithms are being developed to provide results that, when interpreted in conjunction with other available clinical information, can support clinicians in pursuing early and proactive diagnoses with the goal of enabling improved clinical management of these conditions and their associated health risks.

The study investigates whether layering a machine learning model onto a clinically acquired electrocardiogram (ECG) can make it smarter with new functionality. The ECG-AID study will be conducted in collaboration with a growing network of providers and cardiologists and will welcome additional research sites over the coming months. Patients who have received a 12-lead ECG during routine clinical care are eligible for the study. Their ECG data will be analyzed using Tempus’ investigational ECG Analysis Platform algorithms to identify which patients are at high risk of developing heart disease.

The patients participating in the study who are over the age of 65 with no known history of AFib and who are identified as high risk will receive a ZioXT long-term, continuous cardiac monitor, to assess for AFib and other abnormal heart rhythms. Additionally, study participants over the age of 40 years with no prior history of SHD who are identified as high risk will undergo an echocardiogram. Those participants who receive new diagnoses from cardiac monitoring or the echocardiogram will be routed to the appropriate physician for further care and potential treatment with their provider.

“As a practicing cardiologist, I’m excited to be launching a study with the goal of finding treatable heart disease before it is too late. We owe it to patients to build technology like the Tempus ECG Analysis Platform to deliver on the promise of data-driven precision medicine,” said John Pfeifer, MD, MPH, Vice President of Clinical Cardiology at Tempus. 

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