Patient-Operated ECGs Show How AF Burden Predicts Long-Term Outcomes in Early Rhythm Control

New research shows patient-operated ECGs can help predict long-term cardiovascular outcomes by assessing AF burden, supporting personalized AF management.
Recent research presented at the European Society of Cardiology (ESC) congress reveals that patient-operated electrocardiograms (ECGs) can help predict long-term health outcomes in patients with atrial fibrillation (AF). The study analyzed ECG data from the EAST—AFNET 4 trial, focusing on the relationship between AF burden—the proportion of time spent in AF during monitored periods—and future cardiovascular events.
Atrial fibrillation is a common heart rhythm disorder associated with serious complications such as stroke, heart failure, and increased mortality. Traditionally, AF diagnosis relies on a single ECG showing irregular heartbeat, which offers a binary result—either the presence or absence of AF. However, this approach may overlook the severity or duration of AF episodes.
The study involved 1,178 patients, averaging 70 years old with 47% women, who transmitted over 300,000 ECGs over more than five years. Using artificial intelligence to analyze these ECGs, researchers estimated each patient's AF burden. They found that a low AF burden—less than 6% during the first year—was associated with a reduced risk of adverse cardiovascular events such as death, stroke, or hospitalization due to heart failure or acute coronary syndrome. Conversely, higher AF burdens correlated with increased event rates, comparable to those seen with standard care.
Prof. Ulrich Schotten emphasized that understanding AF burden through regular patient-operated ECGs could refine disease assessment and enable more personalized treatment strategies. The findings suggest that actively monitoring and reducing AF burden might improve outcomes in patients undergoing early rhythm control therapy, which includes antiarrhythmic medications or catheter ablation.
This research underscores the potential of digital and remote monitoring tools in managing atrial fibrillation and tailoring interventions based on the quantitative assessment of AF severity.
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