Clinical trial NCT05795842: DELTA (Detecting and Predicting Atrial Fibrillation in Post-Stroke Patients)
Official title: Develop and Validate Machine-Learning Algorithm to Detect Atrial Fibrillation with Wearable Devices
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Stroke, Ischemic
Keywords: Cardiac monitoring
Atrial Fibrillation
Lead sponsor: Emory University
Sponsor class: OTHER
Collaborators: name: Duke University
name: National Heart, Lung, and Blood Institute (NHLBI)
First posted: 2023-04-03
Enrollment: 500
Sex: ALL
Minimum age (years): 55
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
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.

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.

Detailed description: An estimated 1.6% - 6% of the population over age 65 have undiagnosed and often asymptomatic AF. Oral anticoagulant therapy (OAC) reduces the risks of ischemic stroke by 64% and all-cause mortality by 26% for those diagnosed with AF. Hence, not proactively diagnosing and treating AF will be too great an opportunity to miss. Opportunistic AF screening is endorsed as a cost-effective way of diagnosing AF at primary care facilities and/or pharmacies using various techniques. However, the benefits, costs, and potential harms of more powerful systematic AF screening remain a matter of debate. Continuous AF monitoring is also needed to characterize AF occurrence in terms of its burden and temporal relation to symptoms. On the other hand, technologies for continuous monitoring of AF need excellent acceptability by patients. Well-established ambulatory techniques (e.g., Holter) are not suited because of their poor wearability and short monitoring duration. Techniques of implantable loop recorders have advanced significantly to support AF monitoring. However, only some patients can experience the benefits of these techniques because of their associated high costs and invasiveness. Cutaneous ECG patches are clinically used for AF monitoring, but they last for 2 to 4 weeks and are limited to a selected patient population with approved reimbursement. Consumer-facing solutions exist to provide spot-check ECG with an accuracy on par with that of clinical ECG devices, but they are not continuous and are infeasible for patients with compromised fine motor functions.

In contrast to these techniques, PPG is much better positioned for passive AF monitoring because of its strong physiological premise and the practical consideration that PPG sensors are ubiquitously available in more than 71% of consumer wearable devices. However, because PPG is ubiquitously available on mainstream wearables with companion software capable of generating AF alerts, laypeople can readily use PPG to monitor themselves and take actions without clinician guidance. An untoward consequence of this approach is the potential inappropriate utilization of healthcare resources when following up on false AF detections by potentially millions of users. Unfortunately, algorithms described in 24 published papers have not yet achieved adequate precision that can effectively combat such a risk. For example, many studies reported an accuracy of \> 95% but a 5% of error is still too high for a technology that will be used by millions of people to continuously monitor AF in free-living settings.

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.

Primary outcomes:
measure: Sensitivity and specificity for detecting AF with PPG
timeFrame: At completion of the study up to five years
measure: The algorithm concordance index or c-index for predicting AF compared with EHR data
timeFrame: At completion of the study up to five years
Secondary outcomes:
measure: Assess the characteristics and quality of long-term, continuous high-fidelity ambulatory photoplethysmographic (PPG) data using consumer wearable devices with PPG and accelerometers sensors.
timeFrame: Baseline and up to five years
measure: Effect of wrist motion and skin tone on PPG signal
timeFrame: At completion of the study up to five years
measure: Validate atrial fibrillation (AF) pattern detection using investigational machine-learning algorithms from wearable devices in post-stroke patients.
timeFrame: At completion of the study up to five years
Other outcomes:
Locations:
city: Atlanta
country: United States
facility: Emory Clinic
state: Georgia
Study officials:
affiliation: Emory University, School of Nursing
name: Xiao Hu, PhD
role: PRINCIPAL_INVESTIGATOR
References: