Realtime Diagnosis from Electrocardiogram Artificial Intelligence-Guided Screening for Atrial Fibrillation with Long Follow-Up (REGAL): Rationale and design of a pragmatic, decentralized, randomized controlled trial.

Atrial fibrillation (AF) is associated with increased risks of stroke and dementia. Early diagnosis and treatment could reduce the disease burden, but AF is often undiagnosed. An artificial intelligence (AI) algorithm has been shown to identify patients with previously unrecognized AF; however, monitoring these high-risk patients has been challenging. Consumer wearable devices could be an alternative to enable long-term follow-up. To test whether Apple Watch, used as a long-term monitoring device, can enable early diagnosis of AF in patients who were identified as having high risk based on AI-ECG. The Realtime diagnosis from Electrocardiogram (ECG) Artificial Intelligence (AI)-Guided Screening for Atrial Fibrillation (AF) with Long Follow-up (REGAL) study is a pragmatic trial that will accrue up to 2,000 older adults with a high likelihood of unrecognized AF determined by AI-ECG to reach our target of 1,420 completed participants. Participants will be 1:1 randomized to intervention or control and will be followed up for 2 years. Patients in the intervention arm will receive or use their existing Apple Watch and iPhone and record a 30-second ECG using the watch routinely or if an abnormal heart rate notification is prompted. The primary outcome is newly diagnosed AF. Secondary outcomes include changes in cognitive function, stroke, major bleeding, and all-cause mortality. The trial will utilize a pragmatic, digitally-enabled, decentralized design to allow patients to consent and receive follow-up remotely without traveling to the study sites. The REGAL trial will examine whether a consumer wearable device can serve as a long-term monitoring approach in older adults to detect AF and prevent cognitive function decline. If successful, the approach could have significant implications on how future clinical practice can leverage consumer devices for early diagnosis and disease prevention. GOV: : NCT05923359.

Yao X ，Attia ZI ，Behnken EM ，Hart MS ，Inselman SA ，Weber KC ，Li F ，Stricker NH ，Stricker JL ，Friedman PA ，Noseworthy PA ... -  《-》

Does early detection of atrial fibrillation reduce the risk of thromboembolic events? Rationale and design of the Heartline study.

The impact of using direct-to-consumer wearable devices as a means to timely detect atrial fibrillation (AF) and to improve clinical outcomes is unknown. Heartline is a pragmatic, randomized, and decentralized application-based trial of US participants aged ≥65 years. Two randomized cohorts include adults with possession of an iPhone and without a history of AF and those with a diagnosis of AF taking a direct oral anticoagulant (DOAC) for ≥30 days. Participants within each cohort are randomized (3:1) to either a core digital engagement program (CDEP) via iPhone application (Heartline application) and an Apple Watch (Apple Watch Group) or CDEP alone (iPhone-only Group). The Apple Watch Group has the watch irregular rhythm notification (IRN) feature enabled and access to the ECG application on the Apple Watch. If an IRN notification is issued for suspected AF then the study application instructs participants in the Apple Watch Group to seek medical care. All participants were "watch-naïve" at time of enrollment and have an option to either buy or loan an Apple Watch as part of this study. The primary end point is time from randomization to clinical diagnosis of AF, with confirmation by health care claims. Key secondary endpoint are claims-based incidence of a 6-component composite cardiovascular/systemic embolism/mortality event, DOAC medication use and adherence, costs/health resource utilization, and frequency of hospitalizations for bleeding. All study assessments, including patient-reported outcomes, are conducted through the study application. The target study enrollment is approximately 28,000 participants in total; at time of manuscript submission, a total of 26,485 participants have been enrolled into the study. The Heartline Study will assess if an Apple Watch with the IRN and ECG application, along with application-facilitated digital health engagement modules, improves time to AF diagnosis and cardiovascular outcomes in a real-world environment. ClinicalTrials.gov Identifier: NCT04276441.

Gibson CM ，Steinhubl S ，Lakkireddy D ，Turakhia MP ，Passman R ，Jones WS ，Bunch TJ ，Curtis AB ，Peterson ED ，Ruskin J ，Saxon L ，Tarino M ，Tarakji KG ，Marrouche N ，Patel M ，Harxhi A ，Kaul S ，Nikolovski J ，Juan S ，Wildenhaus K ，Damaraju CV ，Spertus JA ，Heartline Steering Committee ... -  《-》

Protocol for Home-Based Solution for Remote Atrial Fibrillation Screening to Prevent Recurrence Stroke (HUA-TUO AF Trial): a randomised controlled trial.

Current international guidelines recommend ECG monitoring after an ischaemic stroke to detect atrial fibrillation (AF) in order to prevent stroke recurrence. However, optimal strategies to detect AF and the downstream management to prevent stroke recurrence remain to be established. The objective of the study was to explore the use of long-term home-based ECG monitoring for AF detection and stroke prevention in patients with a history of stroke. This prospective, randomised, open-label trial with blinded endpoint adjudication aimed to evaluate the efficacy of long-term home-based ECG monitoring for AF detection and stroke prevention in a 24-month period. Patients aged >18 years with a history of ischaemic stroke will be stratified according to the time from the index ischaemic stroke: <1, 1-3 and >3 years and then randomised in 1:1 to (1) home-based AF screening and (2) control. The home-based AF screening system comprises (1) a handheld single-lead ECG recorder (Comfit Healthcare Devices, Hong Kong SAR, China) and (2) a patient-facing smartphone application specially designed for the study. Patients randomised to the home-based AF group will record a 30 s single-lead ECG using a specially designed handheld ECG device every morning or when symptomatic. All remotely obtained data will be automatically transmitted in real-time through the study smartphone application to a secured cloud hosting and analysed using an artificial intelligence-based diagnostic system. When a diagnosis of AF is made with the system, the patients will be called back for a formal cardiology consultation within 1 week. The primary endpoint is the time to first detection of AF at 24 months of follow-up. Secondary endpoints include recurrent stroke or transient ischaemic attack, initiation of long-term anticoagulation therapy, hospitalisation for heart failure, cardiovascular death and all-cause death. The study protocol has been approved by the institutional review board of The University of Hong Kong, and Hong Kong West Cluster, Hospital Authority, Hong Kong SAR, China. Results will be published in peer-reviewed journals. NCT04523649.

Wong CK ，Hai JJ ，Lau YM ，Zhou M ，Lui HW ，Lau KK ，Chan KH ，Mok TM ，Liu Y ，Feng Y ，Tan N ，Tam WC ，Tam KC ，Feng X ，Zuo ML ，Yin LX ，Tan J ，Zhang WJ ，Jiang X ，Huang X ，Ye J ，Liang Y ，Jiang W ，Lei Z ，Huang D ，Yue WS ，Tan G ，Yan BP ，Evora MA ，Chen JY ，Siu CW ... -  《BMJ Open》

Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source.

Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized AF. We pursued this study to determine if the AI-ECG model differentiates between patients with ESUS and those with known causes of stroke, and to evaluate whether the AF prediction by AI-ECG among patients with ESUS was associated with the results of prolonged ambulatory cardiac rhythm monitoring. We reviewed consecutive patients admitted with acute ischemic stroke to a comprehensive stroke center between January 2018 and August 2019 and employed the TOAST classification to categorize the mechanisms of ischemia. Use and results of ambulatory cardiac rhythm monitoring after discharge were gathered. We ran the AI-ECG model to obtain AF probabilities from all ECGs acquired during the hospitalization and compared those probabilities in patients with ESUS versus those with known stroke causes (apart from AF), and between patients with and without AF detected by ambulatory cardiac rhythm monitoring. The study cohort had 930 patients, including 263 patients (28.3%) with known AF or AF diagnosed during the index hospitalization and 265 cases (28.5%) categorized as ESUS. Ambulatory cardiac rhythm monitoring was performed in 226 (85.3%) patients with ESUS. AF probability by AI-ECG was not associated with ESUS. However, among patients with ESUS, the probability of AF by AI-ECG was associated with a higher likelihood of AF detection by ambulatory monitoring (P = 0.004). A probability of AF by AI-ECG greater than 0.20 was associated with AF detection by ambulatory cardiac rhythm monitoring with an OR of 5.47 (95% CI 1.51-22.51). AI-ECG may help guide the use of prolonged ambulatory cardiac rhythm monitoring in patients with ESUS to identify those who might benefit from anticoagulation.

Rabinstein AA ，Yost MD ，Faust L ，Kashou AH ，Latif OS ，Graff-Radford J ，Attia IZ ，Yao X ，Noseworthy PA ，Friedman PA ... -  《-》

Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial.

Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive. To build infrastructure for digital trials to improve efficiency and generalizability and test it using a study to validate an artificial intelligence algorithm to detect atrial fibrillation (AF). We will prospectively enroll 1,000 patients who underwent an electrocardiogram for any clinical reason in routine practice, do not have a previous diagnosis of AF or atrial flutter and would be eligible for anticoagulation if AF is detected. Eligible patients will be identified using digital phenotyping algorithms, including natural language processing that runs on the electronic health records. Study invitations will be sent in batches via patient portal or letter, which will direct patients to a website to verify eligibility, learn about the study (including video-based informed consent), and consent electronically. The method aims to enroll participants representative of the general patient population, rather than a convenience sample of patients presenting to clinic. A device will be mailed to patients to continuously monitor for up to 30 days. The primary outcome is AF diagnosis and burden; secondary outcomes include patients' experience with the trial conduct methods and the monitoring device. The enrollment, intervention, and follow-up will be conducted remotely, ie, a patient-centered site-less trial. This is among the first wave of trials to adopt digital technologies, artificial intelligence, and other pragmatic features to create efficiencies, which will pave the way for future trials in a broad range of disease and treatment areas. Clinicaltrials.gov: NCT04208971.

Yao X ，Attia ZI ，Behnken EM ，Walvatne K ，Giblon RE ，Liu S ，Siontis KC ，Gersh BJ ，Graff-Radford J ，Rabinstein AA ，Friedman PA ，Noseworthy PA ... -  《-》