Atrial fibrillation (AF) is associated with an increased risk of heart failure, stroke, and death. Current therapy options, including pharmacological treatment and catheter ablation, are not satisfactory due to high recurrence rates. To generate mechanistic insight that is not only applicable to a single patient but to entire subgroups of the population, virtual patient cohorts capturing relevant variability are used. This enables the assessment of different therapy options on a population basis and thus the derivation of specific treatment options for subgroups of the populations.
 
This project aims to develop methodology for cohort-based in silico studies and use it to evaluate the AF vulnerability of selected patient groups. Therefore, virtual cohorts of the atria will be generated encompassing anatomical and functional variability. The generated cohort will be validated by comparing features from simulated and clinical 12-lead ECG signals. By analyzing the AF vulnerability for different subgroups of the generated virtual cohort, the identification of subgroups with an increased arrhythmia risk is aimed. In the last part, optimal treatment strategies for both, pharmacological treatment and ablation, will be identified by employing virtual cohorts to improve clinical decision-making when treating AF.