Wearable ECG for AF Screening and Stroke Risk Assessment

Not yet recruiting

• Conditions: Atrial Fibrillation; Stroke

• Registration Number: NCT06907264
• Lead Sponsor: Beijing Tsinghua Chang Gung Hospital

Brief Summary

This study aims to evaluate the application of wearable ECG garments in atrial fibrillation (AF) screening and stroke risk assessment. Using a prospective, multicenter, observational design, the study will recruit high-risk stroke patients aged 40 and above to undergo 24-hour continuous ECG monitoring with wearable ECG garments. The study will assess the detection rate of AF and explore the correlation between heart rate variability (HRV) parameters and stroke risk. Additionally, the study will analyze the association between P-wave indices and AF, and evaluate the acceptability of the device among patients and healthcare providers. The primary goal is to validate the accuracy of wearable ECG garments in AF detection and explore their predictive value for stroke risk in high-risk populations.

Detailed Description

This study is a prospective, multicenter, observational study designed to evaluate the application of wearable ECG garments in atrial fibrillation (AF) screening and stroke risk assessment. The study will be conducted at two centers: the Tsinghua Community under the jurisdiction of Tsinghua University Hospital and the Pinggu District under the jurisdiction of Pinggu District Hospital. The study design includes the following key components:

Study Population:

The study population consists of individuals aged 40 and above who are at high risk of stroke, as determined by the "8+2" stroke risk score.

Participants must be able to operate the wearable ECG garment independently or with the assistance of family members and must provide informed consent.

Sample Size:

Based on preliminary data, the AF detection rate in the local community population aged 40 and above is 3.68%. Using a two-sided test with a significance level of α=0.05 and an allowable error of d=2.5%, the calculated sample size is 218. Considering a 10% dropout rate, the final sample size is 243.

Intervention Methods:

Baseline Assessment: Collect demographic information (e.g., age, gender, height, weight) and clinical information (e.g., hypertension, diabetes, smoking history) from participants.

Device Wear and Monitoring: Participants will wear the wearable ECG garment for 24-hour continuous ECG monitoring. The device will record ECG signals in real time, including heart rate, rhythm, HRV parameters (e.g., SDNN, RMSSD, LF/HF), and P-wave indices.

Data Processing: Daily review of uploaded ECG data to analyze AF events. For participants with suspected AF, further evaluation with a 12-lead ECG or 24-hour Holter monitoring is recommended. HRV parameters will be extracted and analyzed for their correlation with stroke risk.

Acceptability Assessment: The acceptability of the device among participants and healthcare providers will be assessed through quantitative and qualitative methods. Quantitative data include device wear time and interruption rates, while qualitative data are collected through questionnaires and semi-structured interviews.

Outcome Measures:

Primary Outcomes: AF detection rate of the wearable ECG garment; correlation between HRV parameters (e.g., SDNN, RMSSD, LF/HF) and stroke risk.

Secondary Outcomes: Correlation between P-wave indices and AF; acceptability of the device among patients and healthcare providers; incidence of stroke and composite vascular events (e.g., myocardial infarction, heart failure, vascular death) during long-term follow-up.

Follow-up Plan:

Participants will be followed up at 6 and 12 months after enrollment to record the occurrence of stroke, AF, and other cardiovascular events.

Statistical Analysis:

Data will be analyzed using SPSS 25.0. Normally distributed continuous variables will be expressed as mean ± standard deviation, while non-normally distributed variables will be expressed as median and interquartile range. Group comparisons will be made using t-tests or chi-square tests, and correlation analyses will be performed using Pearson or Spearman rank correlation. Predictive factors for AF events and other outcomes will be determined through multivariate competing risk analysis.

Data Management:

All data will be entered into an electronic case report form (eCRF) and uploaded to a cloud database in real time. The research team will regularly review the completeness and accuracy of the data to ensure data quality.

Safety Assessment:

The safety of device use will be assessed, including the comfort of long-term wear and the incidence of adverse reactions (e.g., skin allergies or local irritation). The impact of false positives or false negatives on medical decision-making will also be evaluated.

Study Timeline

• Status Verified: 2025-02
• Study First Submitted: 2025-03-26
• Study First Submitted QC: 2025-03-26
• Last Update Submitted: 2025-03-26
• Study Start: 2025-04-01
• Study First Posted: 2025-04-02
• Last Update Posted: 2025-04-02
• Primary Completion: 2026-12-31
• Study Completion: 2027-12-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 243

Inclusion Criteria

1. Age ≥ 40 years.
2. High-risk stroke population identified by the "8+2" risk score in stroke screening.
3. Ability to operate the device independently or with assistance from family members.
4. Willingness to participate in the study and provide signed informed consent.

Exclusion Criteria

1. Patients with severe diseases that limit device wear (e.g., advanced malignant tumors, severe infections, Class IV heart failure) or those receiving hospice care.
2. Patients unable to operate the device or understand the study procedures due to cognitive impairment, mental illness, or language communication barriers.
3. Patients who may experience severe discomfort or allergic reactions from wearing the device.
4. Patients already using implanted cardiac monitoring devices.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Atrial Fibrillation Detection Rate in Stroke High-Risk Populations Using Wearable ECG Garment	Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up at 6 and 12 months to assess AF.	1.Atrial Fibrillation (AF) Detection Rate: The proportion of AF cases identified by the wearable ECG garment in the high-risk stroke population during 24-hour continuous monitoring.
Correlation Between HRV Parameters and Stroke Risk in High-Risk Populations Using Wearable ECG Garment	Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up at 6 and 12 months to assess stroke.	Correlation Between HRV Parameters and Stroke Risk: Analysis of key HRV parameters (e.g., SDNN, RMSSD, LF/HF) to assess their association with AF and stroke risk in high-risk individuals.

Secondary Outcome Measures

Name	Time	Method
Development of an AF Risk Prediction Model Using P-Wave Indices in High-Risk Stroke Populations	P-wave indices collected during 24-hour ECG monitoring at baseline, with model validation using follow-up data at 6 and 12 months.	Development and validation of an AF risk prediction model incorporating P-wave indices (e.g., P-wave duration, PtfV1) to assess its predictive ability in high-risk stroke populations.
Patient and Healthcare Provider Acceptability of Wearable ECG Garment	Baseline to 12 months: Acceptability assessed at baseline (after initial use) and during follow-up visits at 6 and 12 months.	Evaluation of patient and healthcare provider satisfaction, usability, and comfort with the wearable ECG garment through questionnaires and semi-structured interviews.
Incidence of Stroke, AF, and Composite Vascular Events During Long-Term Follow-Up	Baseline to 12 months: Continuous 24-hour ECG monitoring at baseline, with follow-up assessments at 6 and 12 months to track clinical outcomes.	Occurrence of stroke、AF and Composite vascular events, including myocardial infarction, heart failure, and vascular death during the 12-month follow-up period.

Trial Locations

Locations (2)

Beijing Tsinghua Changgung Hospital; 🇨🇳 Beijing, Beijing, China

Pinggu District Hospital; 🇨🇳 Beijing, China