As with many diseases, there are several forms of heart disease, and some are harder to detect than others, particularly in their early stages.

A recent study funded by the Mayo Clinic focused on one form of heart disease, known as low ejection fraction, with the goal of determining whether an AI screening tool developed to detect low ejection fraction (EF) using data from an EKG could improve the diagnosis of this condition in routine practice. 

According to the study’s findings, published recently in Nature Medicine, the “results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.”

Systolic low EF is indicated by the heart’s inability to contract strongly enough with each beat to pump at least 50% of the blood from its chamber. While an echocardiogram can diagnose low ejection fraction, it’s a time-consuming imaging test that requires more resources than a 12-lead EKG, which is fast, inexpensive and readily available.

For the ECG AI-Guided Screening for Low Ejection Fraction, or EAGLE, trial, 348 primary care clinicians from 120 medical care teams were randomly assigned to usual care or intervention. The intervention group was alerted to a positive screening result for low ejection fraction via the electronic health record, prompting them to order an echocardiogram to confirm.

"The AI-enabled EKG facilitated the diagnosis of patients with low ejection fraction in a real-world setting by identifying people who previously would have slipped through the cracks," said Peter Noseworthy, M.D., a Mayo Clinic cardiac electrophysiologist and senior author on the study.

Over eight months, more than 22,000 adult patients had an EKG under the care of the clinicians in the trial, with the AI finding positive results in 6% of the patients.

"The AI intervention increased the diagnosis of low ejection fraction overall by 32% relative to usual care. Among patients with a positive AI result, the relative increase of diagnosis was 43%," explained Xiaoxi Yao, Ph.D., a health outcomes researcher in cardiovascular diseases at Mayo Clinic. "To put it in absolute terms, for every 1,000 patients screened, the AI screening yielded five new diagnoses of low ejection fraction over usual care."

EAGLE is one of the first large-scale trials to demonstrate value of AI in routine practice. The low EF algorithm has received Food and Drug Administration breakthrough designation and is one of several algorithms developed by Mayo and licensed to Anumana Inc., a new company focusing on unlocking hidden biomedical knowledge to enable early detection as well as accelerate treatment of heart disease.