Mayo Clinic researchers used artificial intelligence (AI) to evaluate patients’ electrocardiograms (ECGs) in a targeted strategy to screen for atrial fibrillation, a common heart rhythm disorder. Atrial fibrillation is an irregular heartbeat that can lead to blood clots that can travel to the brain and cause a stroke, but it’s largely underdiagnosed. In a digital decentralized study, artificial intelligence detected new cases of atrial fibrillation that would not have attracted clinical attention during conventional treatment.
Previous studies have already developed an artificial intelligence algorithm to identify patients with a high probability of previously unknown atrial fibrillation.
“We believe that screening for atrial fibrillation has great potential, but currently the yield is too low and the cost is too high to make widespread screening a reality,” says Peter Noseworthy, MD, a cardiac electrophysiologist at the Mayo Clinic and lead author of the study. . . “This study demonstrates that an AI-ECG algorithm can help direct screening to patients most likely to benefit.”
The study included 1,003 patients for continuous follow-up and used another 1,003 patients from usual care as actual controls. Findings published in Lancetshowed that AI can indeed identify a subset of high-risk patients who would benefit from further intensive cardiac monitoring for atrial fibrillation, supporting an AI-guided targeted screening strategy.
ECGs are commonly performed for a variety of diagnostic purposes, but because atrial fibrillation can be transient, the probability of detecting an episode with a single 10-second ECG is low. Patients can undergo continuous or intermittent cardiac monitoring, which have a higher detection rate, but are too resource-intensive to apply to everyone and can be difficult and expensive for patients.
This is where an AI-supervised EKG might come in handy. An AI algorithm can identify patients who, even if they are in normal rhythm on the day of the ECG, may be at increased risk of undetected episodes of atrial fibrillation at other times. Such patients can then undergo additional monitoring to confirm the diagnosis.
“Traditional screening programs select patients based on age (65 and older) or the presence of conditions such as high blood pressure. These approaches make sense because advanced age is one of the most important risk factors for atrial fibrillation. However, this is not possible repeatedly perform intensive cardiac monitoring in more than 50 million older adults nationwide,” says Xiaoxi Yao, Ph.D., a health outcomes researcher in the Division of Cardiovascular Medicine and Mayo Clinic, Robert D. and Patricia E. Kern Center. for Nursing Science. Dr. Yao is the senior author of the study.
“The study shows that an artificial intelligence algorithm can select a subset of older people who could benefit more from intensive monitoring. If this new strategy is widely implemented, it could reduce undiagnosed atrial fibrillation and prevent stroke and death in millions of patients worldwide,” says Dr. Yao.
The next step in this research will be a multicenter hybrid study focused on the effectiveness of implementing the AI-ECG workflow in different clinical settings and patient populations.
“We hope that this approach will be particularly valuable in resource-limited settings, where the rate of undetected atrial fibrillation may be particularly high and resources to detect it may be limited. However, additional work is needed to overcome barriers to implementation and further research should evaluate targeted screening strategies in these settings,” says Dr Knowsworthy.
“Now that we have demonstrated that AI-guided atrial fibrillation screening is feasible, we will also need to show that patients with screen-detected atrial fibrillation Atrial fibrillation benefit from treatment to prevent a stroke” says Dr. Knowsworthy. “Our ultimate goal is to prevent strokes. I believe that the current study has brought us one step closer.”
Peter A Noseworthy and others. Artificial intelligence-guided screening for atrial fibrillation using the electrocardiogram during sinus rhythm: a prospective, nonrandomized interventional study. Lancet (2022). DOI: 10.1016/S0140-6736(22)01637-3
Citation: AI-guided screening uses electrocardiogram data to detect hidden stroke risk factor (September 28, 2022) Retrieved September 28, 2022, from https://medicalxpress.com/news/2022-09-ai-guided-screening- electrocardiogram-hidden -factor.html
This document is subject to copyright. Except in good faith for the purpose of private study or research, no part may be reproduced without written permission. The content is provided for informational purposes only.