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.

BACKGROUND: 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. OBJECTIVES: 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. DESIGN: 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. SUMMARY: 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. CLINICALTRIALS: GOV: : NCT05923359.

Effect of hospital-at-home vs. traditional brick-and-mortar hospital care in acutely ill adults: study protocol for a pragmatic randomized controlled trial.

BACKGROUND: Delivering acute hospital care to patients at home might reduce costs and improve patient experience. Mayo Clinic's Advanced Care at Home (ACH) program is a novel virtual hybrid model of "Hospital at Home." This pragmatic randomized controlled non-inferiority trial aims to compare two acute care delivery models: ACH vs. traditional brick-and-mortar hospital care in acutely ill patients. METHODS: We aim to enroll 360 acutely ill adult patients (≥18 years) who are admitted to three hospitals in Arizona, Florida, and Wisconsin, two of which are academic medical centers and one is a community-based practice. The eligibility criteria will follow what is used in routine practice determined by local clinical teams, including clinical stability, social stability, health insurance plans, and zip codes. Patients will be randomized 1:1 to ACH or traditional inpatient care, stratified by site. The primary outcome is a composite outcome of all-cause mortality and 30-day readmission. Secondary outcomes include individual outcomes in the composite endpoint, fall with injury, medication errors, emergency room visit, transfer to intensive care unit (ICU), cost, the number of days alive out of hospital, and patient-reported quality of life. A mixed-methods study will be conducted with patients, clinicians, and other staff to investigate their experience. DISCUSSION: The pragmatic trial will examine a novel virtual hybrid model for delivering high-acuity medical care at home. The findings will inform patient selection and future large-scale implementation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05212077. Registered on 27 January 2022.