Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Traditional AF classification (e.g., paroxysmal, persistent, permanent) relies largely on patient-reported symptoms and intermittent electrocardiographic monitoring, which cannot continuously or objectively reflect disease progression, nor effectively inform optimal intervention timing. Although "AF burden" (i.e., duration of AF episodes) has emerged as a potential marker, its association with clinical outcomes remains inconsistent due to limitations in monitoring methods and its one-dimensional nature. Based on our previous work, investigators developed a five-dimensional AF progression model using photoplethysmography (PPG) signals collected from wearable devices. This model quantifies AF progression across five domains: episode frequency, duration, temporal aggregation, circadian rhythm, and tachycardia burden, enabling continuous and multidimensional assessment. Prior validation has demonstrated high agreement with 24-hour Holter monitoring and effective identification of high-risk patients.
The BEAT-AF trial is designed to evaluate the clinical utility of this model in a real-world setting. Specifically, investigators will investigate whether early intervention (e.g., optimization of medical therapy or consideration of catheter ablation) in patients with elevated five-dimensional AF burden (≥4.59%) can reduce symptoms, rhythm/rate-related abnormalities, and AF-related adverse events (such as stroke and heart failure). This study is expected to provide new evidence for dynamic monitoring of AF progression, optimal timing of intervention, and personalized management strategies, ultimately improving patient outcomes.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 5D-AF Burden-Guided Active Management | Experimental | Participants will receive wearable-based AF burden monitoring with treatment decisions guided by a predefined AF burden threshold (≥4.59%). |
|
| Usual Care | No Intervention | Participants will receive guideline-directed standard care without AF burden-guided intervention. Routine follow-up; Standard medical therapy; Physician-directed rhythm/rate control; |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 5D-AF Burden-Guided Active Management | Behavioral | Continuous PPG-based AF monitoring; Threshold-triggered intervention; Stepwise rhythm control strategy; Antiarrhythmic drug optimization; Catheter ablation evaluation; Anticoagulation management; Comorbidity management; Digital follow-up and education; |
| Measure | Description | Time Frame |
|---|---|---|
| At the 12-month follow-up, the time until the occurrence of the first AF-related composite adverse event endpoint. | The occurrence of the AF-related composite adverse event endpoint, which consists of any of the following events: progression of atrial fibrillation, heart failure, stroke, systemic embolism, and cardiovascular-related hospitalization. | 12 months |
| Measure | Description | Time Frame |
|---|---|---|
| Patient-reported outcomes | Atrial fibrillation symptoms (EHRA symptom score) | 24 months |
| Endpoint of effectiveness of atrial fibrillation stress control | The overall load of 5D-AF at the assessment time point < 4.59% |
Not provided
Inclusion Criteria:
Inpatients ≥18 years of age at admission;
Paroxysmal atrial fibrillation was diagnosed;
Own a smart phone and a smart watch and be able to operate them basically;
Before being enrolled, the average daily atrial fibrillation burden was calculated to be ≥ 4.59% based on monitoring through an intelligent wristwatch for at least 3 days;
The treatment status meets any of the following conditions:
Sign the informed consent form
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| JIN ZHI-GENG, Doctor | Contact | 86+15801402223 | lwgjzg@163.com | |
| YU-TAO GUO, Doctor, Study Chair | Contact | 86+13810021492 | dor_guoyt@hotmail.com |
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sixth Medical Center of Chinese PLA General Hospital | Beijing | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 15842354 | Background | Schulman S, Kearon C; Subcommittee on Control of Anticoagulation of the Scientific and Standardization Committee of the International Society on Thrombosis and Haemostasis. Definition of major bleeding in clinical investigations of antihemostatic medicinal products in non-surgical patients. J Thromb Haemost. 2005 Apr;3(4):692-4. doi: 10.1111/j.1538-7836.2005.01204.x. | |
| 11704390 |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D001281 | Atrial Fibrillation |
| ID | Term |
|---|---|
| D001145 | Arrhythmias, Cardiac |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D010335 | Pathologic Processes |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
|
| 24 months |
| clinical endpoint | Atrial fibrillation progression, heart failure, stroke, systemic embolism, transient ischemic attack, venous thromboembolism, cardiovascular-related hospitalization, all-cause hospitalization, cardiovascular-related mortality, all-cause mortality, bleeding events, and the rate of catheter ablation treatment for atrial | 24 months |
| Patient-reported outcomes | quality of life score(EQ-5D-5L) | 24 months |
| Patient-reported outcomes | cognitive function changes (MoCA score) | 24 months |
| Background |
| Przyklenk K, Li G, Whittaker P. No loss in the in vivo efficacy of ischemic preconditioning in middle-aged and old rabbits. J Am Coll Cardiol. 2001 Nov 15;38(6):1741-7. doi: 10.1016/s0735-1097(01)01603-5. |
| 41908207 | Background | Guo Y, Wang H, Wang H, Zhang H, Jin Z. Beyond burden metrics: Wearable photoplethysmography-derived spatiotemporal progression of atrial fibrillation linked to clinical outcomes. Heart Rhythm O2. 2026 Jan 8;7(3):535-544. doi: 10.1016/j.hroo.2025.12.020. eCollection 2026 Mar. |
| 41213119 | Background | Wang H, Liu B, Zhang H, Zhang Z, Jin Z, Wang H, Guo YT. Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and Validation. JMIR Cardio. 2025 Nov 10;9:e78075. doi: 10.2196/78075. |
| 38953776 | Background | Becher N, Metzner A, Toennis T, Kirchhof P, Schnabel RB. Atrial fibrillation burden: a new outcome predictor and therapeutic target. Eur Heart J. 2024 Aug 16;45(31):2824-2838. doi: 10.1093/eurheartj/ehae373. |
| 40544216 | Background | Lin M, Liang H, Zhang K, Chen T, Wang J, Han W, Rong B, Zhong J. Optimal timing for atrial fibrillation patients to undergo catheter ablation. Commun Med (Lond). 2025 Jun 22;5(1):245. doi: 10.1038/s43856-025-00960-1. |
| D013568 |
| Pathological Conditions, Signs and Symptoms |