Atrial fibrillation (AF) is a common condition affecting over 10 million patients in Europe. It is caused by alterations in the atrial substrate, which disrupt the normal flow of electrical impulses and lead to atrial conduction disturbances. AF increases the risk of stroke, mortality, and reduces quality of life. Medications or ablation procedures aimed at restoring sinus rhythm have limited success, with 30-50% of patients still experiencing AF. This is because current treatments focus on electrical abnormalities, rather than addressing the underlying fibrotic substrate.
Women and patients with cardiovascular comorbidities are more likely to have extensive changes in the atrial substrate, respond poorly to therapy, and experience more complications from drugs and interventions. Therefore, a one-size-fits-all treatment approach is not effective for AF. There is also a lack of consensus on the best way to assess the AF substrate, making it difficult to standardize treatment strategies.
To address these challenges, the consortium aims to improve rhythm control therapy for AF by better understanding the differences in the atrial fibrotic substrate between men and women, and patients with and without cardiovascular comorbidities. Parameters such as conduction velocity, functional mapping, surface ECG, and markers from cardiac CT scans can provide a deeper understanding of the arrhythmogenic substrate and guide treatment based on catheter ablation.
In addition, computer models and machine learning techniques can be used to explore underlying mechanisms and predict the prognosis of patients undergoing AF treatment. The ultimate goal is to automate treatment strategies and improve outcomes for AF patients through a better understanding of fibrosis formation and its underlying mechanisms.