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Recruiting NCT06842147

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

No phase Interventional Atrial Fibrillation (AF)

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: 7-day single-lead long-term ECG monitoring device., Standard Screening Group.
Who it may be relevant to
Registry conditions: Atrial Fibrillation (AF). Basic parameters: from 60 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Overview

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.

Detailed description

Effectiveness of Artificial IntelliGence-Driven Single-Lead Long-Term Electrocardiograms Monitoring In Detecting New-Diagnosed Atrial FIbrillation (GEMINI) trial is a parallel, two-stage cluster randomized trial being conducted in 128 villages in Qujiang District, Quzhou City, Zhejiang Province. Village clinics serve as the primary health care units and provide essential services to rural residents, with one clinic allocated to each administrative village in China. Each village clinic is staffed by a village doctor with basic medical training (certificate-level) and operates under the supervision of physicians based at 8 township health centers. These township health centers are, in turn, overseen by a single district-level hospital, forming a three-tier hierarchical healthcare system . This decentralized structure, designed to address the shortage of specialist resources at the village level, enables consistent outreach and sustained medical engagement across rural populations. In this study, the villages (clusters) were randomly assigned to either the enhanced screening group (intervention arm) or the routine screening group (control arm). The coprimary outcomes are the 1-year detection rate of newly diagnosed AF (Phase 1) and a 3-year composite of all-cause mortality, stroke or systemic embolism, and hospitalization for heart failure (Phase 2).

Interventions

  • Device 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).
  • Device 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.

Primary outcome measures

  • Detection rate of new atrial fibrillation diagnoses [Time frame: 1 year]
  • Composite endpoints [Time frame: 3 years]
Secondary outcome measures (7)
  • Atrial Fibrillation Burden Assessment [Time frame: 1 year]
  • All-Cause Mortality Rate [Time frame: 3 years]
  • Cardiovascular Mortality Rate [Time frame: 3 years]
  • Stroke/systemic embolism: [Time frame: 3 years]
  • Heart Failure Hospitalization Rate [Time frame: 3 years]
  • Cardiovascular Hospitalization Rate [Time frame: 3 years]
  • Detection Rate of New Atrial Fibrillation [Time frame: 3 years]

Eligibility criteria

Inclusion criteria

Age 60 years or older No previous history of atrial fibrillation (AF) Willing to participate in random assignment and follow-up

Exclusion criteria

Patients with a pacemaker or implanted cardioverter-defibrillator (ICD) Patients with cognitive impairment or unable to provide informed consent Patients with an estimated life expectancy of less than one year (e.g., advanced cancer or end-stage renal disease) Patients deemed unsuitable for the study by the investigator Patients who refuse to participate

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Open label
Primary purpose
Screening

Study locations

China · 1 center
  • Beijing Anzhen Hospital — Zhejiang

Identifiers

NCT: NCT06842147 · Beijing An Zhen Hospital

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗