Atrial fibrillation (AFib) is a common type of cardiac arrhythmia that affects about 59 million people globally. While AFib itself is not usually life threatening, it can increase a person’s risk of mortality from cardiovascular conditions such as stroke, heart attack, and heart failure, as well as other diseases like dementia and gastrointestinal issues. Early detection of AFib is crucial for better health outcomes, and researchers have developed new technology to predict cardiac arrhythmia about 30 minutes before it occurs using artificial intelligence (AI) and electrocardiogram information gathered through wearable devices.
The AI model, named WARN (Warning of Atrial fibRillatioN), was trained and tested on electrocardiogram data gathered from individuals wearing Holter devices for 24 hours at Tongji Hospital in Wuhan, China. The model was able to predict the transition from normal cardiac rhythm to atrial fibrillation with an average warning time of 30 minutes before onset with an accuracy rate of about 80%. This early warning system could potentially allow individuals to take preventive measures such as medication to manage the condition before more serious complications arise.
While the study used medical devices to collect heart electrical activity data, the researchers hope to eventually implement this predictive model into everyday smartwatches for continuous monitoring of cardiovascular health. By monitoring subtle changes in heart rate dynamics, individuals may receive alerts of potential issues such as AFib or life-threatening events like heart attacks. This early detection can prompt patients to seek medical attention sooner and potentially prevent the onset of certain diseases and their associated complications.
Moving forward, the researchers plan to personalize the algorithm for individual patients by having them wear smartwatches for extended periods. This personalized approach aims to enhance the early warning window and performance for each patient based on their unique disease features. The next steps in their research involve developing apps for different smartwatches and conducting prospective studies to further validate the predictive capabilities of the model.
Cardiologist Paul Drury, MD, praised the study findings, noting that detecting AFib before it occurs can significantly improve treatment outcomes. Early alerts can empower patients to take proactive measures to manage their condition and potentially reduce emergency room visits and other morbidities associated with AFib. While current smart devices are effective at detecting AFib, the ability to predict AFib episodes could revolutionize how patients manage their condition and prevent complications. Drury emphasized the importance of further validation through larger-scale studies before implementing AI technology into wearable devices for clinical use.
