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Develop and Validate Machine-Learning Algorithm to Detect Atrial Fibrillation With Wearable Devices
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.
Details
| Lead sponsor | Emory University |
|---|---|
| Status | RECRUITING |
| Enrolment | 500 |
| Start date | 2023-03-21 |
| Completion | 2028-12 |
Conditions
- Stroke, Ischemic
Interventions
- wearable wristband model
- Samsung Galaxy Watch 6
- Standard of care extended ECG monitoring
Primary outcomes
- Sensitivity and specificity for detecting AF with PPG — At completion of the study up to five years
Sensitivity and specificity of the algorithm will be calculated at study completion - The algorithm concordance index or c-index for predicting AF compared with EHR data — At completion of the study up to five years
The c-index is a metric to evaluate the predictions made by an algorithm. It is defined as the proportion of concordant pairs divided by the total number of possible evaluation pairs. For predicting AF with EHR data, researchers are targeting a higher c-index. Participants with a higher predicted probability of AF will have AF sooner than those with a lower predicted probability.
Countries
United States