Investigators have developed and tested an artificial intelligence-based method for predicting an individual’s five-year risk of developing atrial fibrillation, or an irregular heartbeat, according to a news release from Massachusetts General Hospital.
The AI-based method was described in a study published in Circulation. The study team was led by researchers at Massachusetts General Hospital (MGH) and the Broad Institute of MIT and Harvard.
The investigators developed the artificial intelligence-based method to predict the risk of atrial fibrillation within the next five years based on results from electrocardiograms (noninvasive tests that record the electrical signals of the heart) in 45,770 patients receiving primary care at MGH.
Next, the scientists applied their method to three large data sets from studies including a total of 83,162 people. The AI-based method predicted atrial fibrillation risk on its own and was synergistic when combined with known clinical risk factors for predicting atrial fibrillation. The method was also highly predictive in subsets of individuals such as those with prior heart failure or stroke.
“We see a role for electrocardiogram-based artificial intelligence algorithms to assist with the identification of individuals at greatest risk for atrial fibrillation,” says senior author Steven A. Lubitz, MD, MPH, Cardiac Electrophysiologist at MGH and associate member at the Broad Institute.
Lubitz adds that the algorithm could serve as a form of pre-screening tool for patients who may currently be experiencing undetected atrial fibrillation, prompting clinicians to search for atrial fibrillation using longer-term cardiac rhythm monitors, which could in turn lead to stroke prevention measures.