Actively Recruiting
DELTA (Detecting and Predicting Atrial Fibrillation in Post-Stroke Patients)
Led by Emory University · Updated on 2026-01-21
500
Participants Needed
1
Research Sites
297 weeks
Total Duration
On this page
Sponsors
E
Emory University
Lead Sponsor
D
Duke University
Collaborating Sponsor
AI-Summary
What this Trial Is About
Atrial Fibrillation (AF) is an abnormal heart rhythm. Because AF is often asymptomatic, it often remains undiagnosed in the early stages. Anticoagulant therapy greatly reduces the risks of stroke in patients diagnosed with AF. However, diagnosis of AF requires long-term ambulatory monitoring procedures that are burdensome and/or expensive. Smart devices (such as Apple or Fitbit) use light sensors (called "photoplethysmography" or PPG) and motion sensors (called "accelerometers") to continuously record biometric data, including heart rhythm. Smart devices are already widely adopted. This study seeks to validate an investigational machine-learning software (also called "algorithms") for the long-term monitoring and detection of abnormal cardiac rhythms using biometric data collected from consumer smart devices. The research team aims to enroll 500 subjects who are being followed after a stroke event of uncertain cause at the Emory Stroke Center. Subjects will undergo standard long-term cardiac monitoring (ECG), using FDA-approved wearable devices fitted with skin electrodes or implantable continuous recorders, and backed by FDA-approved software for abnormal rhythm detection. Patients will wear a study-provided consumer wrist device at home, for the 30 days of ECG monitoring, 23 hours a day. At the end of the 30 days, the device data will be uploaded to a secure cloud server and will be analyzed offline using proprietary software (called "algorithms") and artificial intelligence strategies. Detection of AF events using the investigational algorithms will be compared to the results from the standard monitoring to assess their reliability. Attention will be paid to recorded motion artifacts that can affect the quality and reliability of recorded signals. The ultimate aim is to establish that smart devices can potentially be used for monitoring purposes when used with specialized algorithms. Smart devices could offer an affordable alternative to standard-of-care cardiac monitoring.
CONDITIONS
Official Title
DELTA (Detecting and Predicting Atrial Fibrillation in Post-Stroke Patients)
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Adults 55 years of age or older
- Diagnosed with ischemic stroke of uncertain cause and recently discharged
- Receiving follow-up care at the Emory Stroke Clinic
- Prescribed clinical extended cardiac monitoring
- Able and willing to provide informed consent or have a legal representative do so
- Participant, family proxy, or caregiver understands English and can manage and recharge the study wrist device
You will not qualify if you...
- Younger than 55 years of age at time of consent
- No indication for clinical extended cardiac monitoring
- Unable to understand English or follow instructions to manage and recharge the study wrist device
- Diagnosis of structural valve disease, endocarditis, aortic arch atheroma greater than 3 mm, hypercoagulability, on lifelong anticoagulation, or active neoplastic disease
- Unwilling or unable to provide informed consent
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Emory Clinic
Atlanta, Georgia, United States, 30322
Actively Recruiting
Research Team
X
Xiao Hu, PhD
CONTACT
C
Corey Williams
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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