DELTA (Detecting and Predicting Atrial Fibrillation in Post-Stroke Patients)

NCT05795842 | Recruiting FDA Device

• 3 other identifiers: STUDY000045301R01HL166233-012025P010026
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Stroke, Ischemic

Keywords

Cardiac monitoring; Atrial Fibrillation

Outcome Measures

Primary Outcomes (2)

• Sensitivity and specificity for detecting AF with PPG

  Sensitivity and specificity of the algorithm will be calculated at study completion

  At completion of the study up to five years

• The algorithm concordance index or c-index for predicting AF compared with EHR data

  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.

  At completion of the study up to five years

Secondary Outcomes (3)

• Assess the characteristics and quality of long-term, continuous high-fidelity ambulatory photoplethysmographic (PPG) data using consumer wearable devices with PPG and accelerometers sensors.

  Baseline and up to five years

• Effect of wrist motion and skin tone on PPG signal

  At completion of the study up to five years

• Validate atrial fibrillation (AF) pattern detection using investigational machine-learning algorithms from wearable devices in post-stroke patients.

  At completion of the study up to five years

Study Arms (1)

AFib monitoring learning algorithms

Participants will wear a prescribed (standard of care) ambulatory ECG monitoring (Biotel Patch or LINQ insertable cardiac monitor) and either a MOTO 360 smartwatch, fitted with proprietary firmware (LifeQ) to collect continuous biometric signals, including PPG signals and 3-axis accelerometers in an ambulatory setting or a Samsung Galaxy watch 6 paired with the Samsung Galaxy phone S21 to continuously record PPG and/or ECG data that can transmit data.

Device: wearable wristband model; Other: Samsung Galaxy Watch 6; Device: Standard of care extended ECG monitoring

Interventions

wearable wristband model DEVICE

MOTO 360 smartwatch: is a specific consumer wearable wristband model (Motorola: MOTO 360), fitted with proprietary firmware (LifeQ) to collect continuous biometric signals, including PPG signals and 3-axis accelerometers in an ambulatory setting. The device is not a medical or diagnostic device, but rather a photoplethysmography (PPG) data collection device. PPG is a non-invasive technology that uses light to measure the change in the volume of blood beneath the skin that occurs as the heart beats. LifeQ has developed software that enables the collection of vital signs data from PPG technology.

Also known as: Algorithm Device

AFib monitoring learning algorithms

Samsung Galaxy Watch 6 OTHER

The Samsung Galaxy Watch6 will collect study data on physiological signals with a compatible Samsung Galaxy phone S21. The Samsung Galaxy Watch6 will include various models, the difference being the size of the watch face or the analog front end of the device. The software device is installed on the Samsung Galaxy Watch. The app on the watch continuously records PPG and/or ECG data and transmits it. The phone app allows study staff to enter the subject ID, initiate data collection, and stop data collection sessions on the watch. It also receives and stores PPG and ECG data from the paired watch. The PPG app used in the study does not trigger irregular rhythm notifications or display rhythm classification. The data collected using the PPG app will support algorithm development.

AFib monitoring learning algorithms

Standard of care extended ECG monitoring DEVICE

Participants enrolled in the study are prescribed ambulatory ECG monitoring (Mobile Cardiac Outpatient Telemetry, Biotel e-Patch, or LINQ insertable cardiac monitor). If the patient is negative for Afib during their time wearing an ECG monitoring patch, then patients may proceed with LINQ insertable cardiac monitor, as part of their standard of care. These are standard-of-care FDA-approved devices and detection software. Researchers will rely on the final ECG report to identify arrhythmic events to use as a golden standard to evaluate the algorithm findings. Specifically, the raw data will be used for establishing and getting an accurate ground truth for the algorithm.

AFib monitoring learning algorithms

Eligibility Criteria

• Age: 55 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Subjects 55 years of age or older, who are discharged from an index ischemic stroke, who are treated at one of the hospitals within the Emory Health Care System, and who initiate follow-up care at the Emory Stroke Clinic. The patient population should have clinically prescribed extended cardiac monitoring.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• National Heart, Lung, and Blood Institute (NHLBI): collaborator
• Emory University: lead
• Duke University: collaborator

Study Sites (1)

Emory Clinic

Atlanta, Georgia, 30322, United States

RECRUITING

MeSH Terms

Conditions

Ischemic Stroke; Atrial Fibrillation

Condition Hierarchy (Ancestors)

Stroke; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases; Arrhythmias, Cardiac; Heart Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Xiao Hu, PhD

  Emory University, School of Nursing

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Xiao Hu, PhD

CONTACT

404-712-8520; xhu40@emory.edu

Corey Williams

CONTACT

404-251-4060; corey.williams2@emory.edu

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: March 21, 2023
• First Posted: April 3, 2023
• Study Start: March 21, 2023
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2028
• Last Updated: January 21, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE
• Time Frame: Beginning 3 months and ending 5 years following article publication
• Access Criteria: Researchers who provide a methodologically sound proposal to achieve aims in the approved proposal. To gain access, data requesters will need to sign a data access agreement. Data are available for 5 years at a third-party website.

Locations

US (1)