Effectiveness of Artificial IntelliGence-Driven Single-LEad Long-TerM Electrocardiograms MonItoring in Detecting New-Diagnosed Atrial FIbrillation
Background: Atrial fibrillation (AF) is a common arrhythmia associated with a significantly increased risk of stroke and heart failure. Early detection and appropriate anticoagulation therapy are critical for reducing the risk of stroke in AF patients. However, traditional screening methods, such as pulse palpation and 12-lead ECG, often fail to detect asymptomatic or paroxysmal AF. Newer technologies, such as long-term ECG monitoring, show promise in improving detection rates.
Objective: This study aims to evaluate the effectiveness of a 7-day single-lead long-term ECG patch in detecting AF in rural residents aged 60 years and older and to explore its potential in reducing long-term cardiovascular events.
Methods: This is a parallel, two-stage, cluster randomized controlled trial conducted in Qujiang District, Quzhou City, Zhejiang Province. Participants will be randomly assigned to either a long-term monitoring group (7-day single-lead ECG) or a conventional monitoring group (12-lead ECG). The primary endpoints include AF detection rates at 1-year and cardiovascular events (stroke, heart failure hospitalization, and all-cause mortality) at 3-year follow-up. Secondary endpoints include detection rates of paroxysmal and persistent AF.
Results: Participants in the long-term monitoring group will undergo baseline health checks and 12-lead ECG, followed by 7-day single-lead ECG monitoring. The routine group will receive standard care, including annual 12-lead ECGs. Annual education on AF management will be provided to both groups.
Conclusion: This trial seeks to determine whether long-term ECG monitoring can enhance AF detection rates and reduce cardiovascular risks in the elderly rural population, potentially informing guidelines for AF management in similar settings.
• Age 60 years or older No previous history of atrial fibrillation (AF) Willing to participate in random assignment and follow-up