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

Condition(s) studied

Investigational drug(s) / intervention(s)

7-day single-lead long-term ECG monitoring device.: Participants in the long-term screening group will undergo baseline data collection and a 12-lead ECG at baseline, followed using a 7-day single-lead long-term ECG monitoring device. This device continuously collects dynamic ECG data for seven days and allows participants to view and store ECGs through the accompanying software (registration number: 20192070163).

Standard Screening Group: Patients in the standard screening group will undergo baseline data collection and a standard 12-lead ECG at the start of the study, with follow-up including opportunistic ECG checks as needed. Those diagnosed with AF will receive anticoagulation and antiarrhythmic therapy according to clinical guidelines.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Detection rate of new atrial fibrillation diagnoses — 1 year
  Detection rate of new atrial fibrillation diagnoses in both groups. Atrial fibrillation is defined by a single-lead ECG (≥30s) or a 12-lead ECG (≥10s) showing absent P waves, replaced by irregular fibrillation waves (f-waves) of varying size, shape, and duration, and absolute irregularity of RR intervals. Two cardiologists will interpret the ECG, and if there is doubt, a third cardiologist will review the case. Opportunistic ECG is defined as any ECG performed for medical reasons during follow-up (primarily 12-lead ECG), which must be reported and diagnosed.
• Composite endpoints — 3 years
  The all-cause mortality, stroke/systemic embolism, and heart failure hospitalization rate in the study population.

Trial sites (1)

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