Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Since AF is progressive, the longer one has it, the harder it is to treat, and the risks of stroke, dementia and heart failure increase. The most effective treatment is catheter ablation therapy, a procedure that strategically destroys tissue to restrict propagation of electrical waves. However, approaches are currently generic, ignoring patient variability in atrial structure, and AF usually recurs. We aim to develop a personalised medicine approach based on computer modelling, to use digital twins to plan AF ablation to prevent recurrence. We propose to use preoperative measurements, imaging (MRI/CT) and the ECG, to build digital twins. However, these data are insufficient to uniquely characterize the atria, so we will build sets of potential digital twins for each patient, each of which will have its ideal ablation treatment determined. Invasive measurements acquired during the ablation procedure will be then used to select the digital twin that best matches the patient.
IHS leads Work Package 4 (Health Economics and Implementation Support). The aim of this work package is to conduct an economic analysis evaluating the costs and benefits arising from early preventative and longer-lasting treatment, reduced duration and procedural risks of interventions. This entails the development of a tailor-made model to evaluate the differences between standard and personalised therapeutic strategies in order to support decision makers.