Effectiveness of Artificial IntelliGence-Driven Single-LEad Long-TerM Electrocardiograms MonItoring in Detecting New-Diagnosed Atrial FIbrillation

Recruiting now · Not applicable

Conditions studied: Atrial Fibrillation (AF)

In brief

Abstract Purpose: Atrial fibrillation (AF) is a leading cause of stroke and heart failure, yet detection remains suboptimal in rural settings due to limited resources. This study evaluates whether an enhanced screening strategy using artificial intelligence (AI)-integrated 7-day single-lead electrocardiogram (ECG) patches improves AF detection and long-term clinical outcomes compared to routine care in rural China. Methods: This cluster-randomized trial will be conducted across 128 village clinics in Quzhou, Zhejiang Province. Villages are randomized 1:1 to either enhanced or routine screening. Participants aged 60 years or older (approximately 120 per village) in both arms receive family-centered AF education and opportunistic assessments. The enhanced group undergoes screening via 7-day single-lead ECG patches, while the routine group utilizes standard 12-lead ECGs. Results: The trial features two primary endpoints. The Phase 1 endpoint is the newly diagnosed AF detection rate during a 1-year screening period. The Phase 2 endpoint is a 3-year composite outcome of all-cause mortality, stroke or systemic embolism, and hospitalization for heart failure. Conclusion: By integrating wearable AI technology into primary care, this trial seeks to overcome diagnostic barriers in resource-limited environments. The findings will determine if prolonged digital monitoring can significantly enhance AF detection and reduce major cardiovascular events in elderly rural populations.

Key facts

Study ID
NCT06842147
Run by
Beijing Anzhen Hospital
People needed
15360
Starts
2025-03-01
Expected to finish
2028-07-30
Last updated by the study team
2026-01-07

Who can join

Age: 60 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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