Atrial Fibrillation (AF) is a complex cardiac disease that can lead to blood clots and increase the risk of stroke. AF is gaining epidemic proportions and the majority of AF patients are prescribed anticoagulants, but at the cost of increased risk of severe bleedings. Individualized anticoagulation management, therefore, remains a major challenge but routinely available patient-specific clinical data are under-utilized. Computational models based on patient-specific medical images of the atria have reached a high level of sophistication, but remain insufficiently validated and tested to be used for individualized clinical predictions. PARIS will utilize existing medical records of AF patients with known clinical outcome, to validate computer models and predictive machine learning methods in an iterative process. The ambition is to identify biomarkers that correlate with stroke and to prospectively outperform the current risk score to reduce individual bleeds by optimizing personalized treatment and clinical follow-up.


ERA-Net on Systems Medicine under the EU Framework Programme Horizon2020.


Inria Epione (France)

University Heart Center Hamburg (Germany)