Our research
Atrial fibrillation is the most common sustained cardiac arrhythmia worldwide, affecting more than 3% of the adult population. Yet it is often asymptomatic and undiagnosed—putting individuals at high risk of preventable stroke and other serious outcomes. Our work addresses this gap by exploring novel approaches for detecting and treating AF before complications arise.
Main Research Areas
- Systematic and risk-based screening for atrial fibrillation
- Device-based detection using implantable loop recorders, wearables, and patch ECG
- Artificial intelligence and digital innovation for AF risk prediction
- Personalized prevention strategies to reduce stroke and AF burden
- Implementation of care pathways and clinical decision support for AF
Our research bridges electrophysiology, public health, and digital medicine. Through multidisciplinary collaboration, we translate research findings into improved tools and strategies for AF detection and risk management.
Research projects
STROKESTOP I & II
These landmark randomized controlled trials evaluated population-based screening for atrial fibrillation in older adults (aged 75–76 years). In STROKESTOP I, 28,768 individuals were randomized to invitation to screening versus usual care. The study demonstrated that systematic ECG-based screening is feasible and associated with a reduction in stroke and all-cause mortality over long-term follow-up.
STROKESTOP II built on this by integrating NT-proBNP biomarker preselection, further refining cost-effectiveness and targeting higher-risk individuals. The results of these trials have influenced European guidelines and helped establish a foundation for scalable screening strategies.
WAIT (Waiting for Atrial Fibrillation Treatment) Study
The WAIT study explores the effect of structured weight reduction interventions in patients with atrial fibrillation. Obesity is a well-established risk factor for both AF onset and progression. This randomized trial investigates whether targeted weight loss can reduce AF burden, improve symptom control, and enhance the effectiveness of rhythm control therapies. The study integrates digital weight management tools, lifestyle counseling, and longitudinal follow-up of AF burden using ambulatory monitoring.
AI-Enabled Prediction of Atrial Fibrillation and Outcomes
We are developing and validating artificial intelligence (AI) models to identify individuals at high risk for atrial fibrillation and its complications—based on ECG signals, electronic health records, wearable data, and biomarkers.
Projects include the use of machine learning for:
– Predicting the onset of AF before clinical diagnosis
– Estimating AF burden and progression risk
– Identifying individuals at increased risk of stroke or heart failure even before AF is diagnosed
These tools may support more personalized screening invitations, early initiation of preventive treatment, and risk-adapted follow-up strategies.