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

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

Recruiting
60 years and older
All
Phase N/A

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Overview

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.

Description

Introduction: Atrial fibrillation (AF) is the most common arrhythmia in clinical practice, significantly increasing the risk of stroke and heart failure. Early diagnosis and intervention, including anticoagulation therapy, are crucial for reducing stroke risk in AF patients. While traditional screening methods, such as 12-lead ECGs and pulse palpation, often miss asymptomatic or paroxysmal AF, new technologies like long-term ECG monitoring show potential in improving detection rates.

The aim of this study is to evaluate the effectiveness of a 7-day single-lead ECG patch for detecting AF among rural populations aged 60 years and older. This trial also aims to explore the potential of this intervention to reduce long-term cardiovascular events, such as stroke and heart failure hospitalization.

Trial Design and Setting: This study will be a parallel, two-stage, cluster randomized controlled trial. Participants will be residents aged 60 years and older from administrative villages in Qujiang District, Quzhou City, Zhejiang Province, China. Villages will be randomized to either the long-term monitoring group (7-day single-lead ECG) or the conventional monitoring group (12-lead ECG). The study design will be divided into two phases:

Phase 1 (1-year follow-up): Detection rates of AF in both groups. Phase 2 (3-year follow-up): Assessment of cardiovascular events, including stroke, heart failure hospitalization, and all-cause mortality.

Intervention and Control: Participants in the long-term monitoring group will undergo baseline screening, followed by 7-day single-lead ECG monitoring. In contrast, the control group will receive standard care, including annual 12-lead ECG screening and any additional tests as required based on symptoms or clinical indications.

Data Collection and Analysis: Data will be collected at baseline and during follow-up visits, including demographic information, medical history, ECG readings, and outcomes related to cardiovascular events. The data will be transmitted securely to a central database, ensuring strict adherence to confidentiality protocols. A mixed-effects logistic regression model will be used to analyze the data, and multiple imputation will be employed to handle missing data.

Ethical Considerations: This study has received approval from the local Institutional Review Boards (IRBs) and ethics committees, including Beijing Anzhen Hospital's Ethics Committee. Informed consent will be obtained from all participants either in physical or electronic form.

Eligibility

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

Study details
    Atrial Fibrillation New Onset

NCT06842147

Beijing Anzhen Hospital

15 October 2025

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