AI-Based Method Predicts Atrial Fibrillation Risk Based on ECG Results

Investigators have developed and tested an artificial intelligence (AI)-based method for predicting an individual’s five-year risk of developing atrial fibrillation, or an irregular heartbeat, from electrocardiogram results.

The method developed by researchers at the Massachusetts General Hospital (MIG; Boston, MA, USA) could be used to identify patients who might benefit from preventative measures. Atrial fibrillation—an irregular and often rapid heart rate—is a common condition that often leads to the formation of clots in the heart that can travel to the brain to cause a stroke. MIG researchers developed the AI-based method to predict the risk of atrial fibrillation within the next five years based on results from electrocardiograms (non-invasive 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 individuals. 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. 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. The study’s findings also demonstrate the potential power of AI—which in this case involve a specific type called machine learning—to advance medicine.

“We see a role for electrocardiogram-based artificial intelligence algorithms to assist with the identification of individuals at greatest risk for atrial fibrillation,” said senior author Steven A. Lubitz, MD, MPH, a cardiac electrophysiologist at MGH and associate member at the Broad Institute.

“The application of such algorithms could prompt clinicians to modify important risk factors for atrial fibrillation that may reduce the risk of developing the disease altogether,” added co–lead author Shaan Khurshid, MD, MPH, an electrophysiology clinical and research fellow at MGH.

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