BOSTON – 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. As described in a study published in Circulation, a team led by researchers at Massachusetts General Hospital (MGH) and the Broad Institute of MIT and Harvard has developed an artificial intelligence–based method for identifying patients who are at risk for developing atrial fibrillation and could therefore benefit from preventative measures.
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 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.
“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, a cardiac electrophysiologist at MGH and associate member at the Broad Institute. Adds co–lead author Shaan Khurshid, MD, MPH, an electrophysiology clinical and research fellow at MGH: “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.”
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.
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. “With the explosion of data science technologies and the vast amounts of clinical data now available, machine learning is poised to help clinicians and researchers make great strides in enhancing cardiology care,” says co-author Anthony Philippakis, MD, PhD, chief data officer at the Broad and co-director of the institute’s Eric and Wendy Schmidt Center. “As a data scientist and former cardiologist, I’m excited to see how machine learning–based methods can work with the tests and clinical approaches we use every day to help us improve risk prediction and take care of patients with atrial fibrillation.”
Lubitz is an associate professor of Medicine at Harvard Medical School.Co-authors include Samuel Friedman, PhD, Christopher Reeder, PhD, Paolo Di Achille, PhD, Nathaniel Diamant, BS, Pulkit Singh, BS, Lia X. Harrington, PhD, Xin Wang, MBBS, MPH, Mostafa A. Al-Alusi, MD, Gopal Sarma, MD, PhD, Andrea S. Foulkes, ScD, Patrick T. Ellinor, MD, PhD, Christopher D. Anderson, MD, MMSc, Jennifer E. Ho, MD, and Puneet Batra, PhD.
This work was supported by the National Institutes of Health, the American Heart Association, the Doris Duke Foundation, and the Leducq Foundation.
About the Massachusetts General Hospital
Massachusetts General Hospital, founded in 1811, is the original and largest teaching hospital of Harvard Medical School. The Mass General Research Institute conducts the largest hospital-based research program in the nation, with annual research operations of more than $1 billion and comprises more than 9,500 researchers working across more than 30 institutes, centers and departments. In August 2021, Mass General was named #5 in the U.S. News & World Report list of "America’s Best Hospitals."
About Broad Institute of MIT and Harvard
Broad Institute of MIT and Harvard was launched in 2004 to empower this generation of creative scientists to transform medicine. The Broad Institute seeks to describe the molecular components of life and their connections; discover the molecular basis of major human diseases; develop effective new approaches to diagnostics and therapeutics; and disseminate discoveries, tools, methods, and data openly to the entire scientific community.
Founded by MIT, Harvard, Harvard-affiliated hospitals, and the visionary Los Angeles philanthropists Eli and Edythe L. Broad, the Broad Institute includes faculty, professional staff, and students from throughout the MIT and Harvard biomedical research communities and beyond, with collaborations spanning over a hundred private and public institutions in more than 40 countries worldwide.
Method of Research
Subject of Research
Electrocardiogram-based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
Article Publication Date
Dr. Lubitz receives sponsored research support from Bristol Myers Squibb / Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit, and IBM, and has consulted for Bristol Myers Squibb / Pfizer, Bayer AG, and Blackstone Life Sciences. Dr. Ellinor receives sponsored research support from Bayer AG and IBM, and has consulted for Novartis, MyoKardia and Bayer AG. Dr. Ho has received sponsored research support from Bayer AG and research supplies from EcoNugenics, Inc. Dr. Batra receives sponsored research support from Bayer AG and IBM, and has consulted for Novartis and Prometheus Biosciences. Dr. Anderson receives sponsored research support from Bayer AG and has consulted for ApoPharma.