Develop and Validate Machine-Learning Algorithm to Detect Atrial Fibrillation With Wearable Devices
Trial ID: NCT05795842
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2023-03-21
Completion date: 2028-12
Sponsor: Emory University
Collaborators: Duke University; National Heart, Lung, and Blood Institute (NHLBI)
Conditions: Stroke, Ischemic
Interventions: wearable wristband model; Samsung Galaxy Watch 6; Standard of care extended ECG monitoring
Locations: Emory Clinic
Countries: United States
Enrollment: 500
Age range: 55 Years - None
Sex: ALL

Summary:
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.

Eligibility criteria:
Inclusion Criteria:

* Adults 55 years of age or older.
* Post-discharge with diagnostic of index ischemic stroke with uncertain cause.
* Subject must be treated at the Emory Stroke Clinic for follow-up treatment.
* Subject must be prescribed a clinical extended cardiac monitoring.
* Subject or their Legal Authorized Representative (LAR) must be willing and able to provide informed consent.
* Subject, family proxy, or caregiver must understand English and the instructions to manage and recharge the study wrist device.

Exclusion Criteria:

* Subject is younger than 55 years of age at the time of consent.
* No indication for clinical extended cardiac monitoring.
* Subject, family proxy, or caregiver unable to understand English and unable to follow the instructions on how to manage and recharge the study wrist device.
* Subject has a diagnosis of structural valve disease, endocarditis, aortic arch atheroma \>3 mm, hypercoagulability, on lifelong anticoagulation, or has an active neoplastic disease
* Subject or LAR is not willing or able to provide informed consent.

Outcomes:
Sensitivity and specificity for detecting AF with PPG; The algorithm concordance index or c-index for predicting AF compared with EHR data