| Summary: | <p>Atrial fibrillation (AF) is the most prevalent sustained arrhythmia, affecting over 43 million people worldwide and one in 45 people in the UK. The modest efficacy of currently available therapies adds to the nature of the problem. Antiarrhythmic medication and catheter ablation are the two fundamental pillars of AF treatment, but both have limited efficacy, especially for persistent forms of the disease.</p>
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<p>The general aim of this thesis is to provide digital evidence for the optimal stratification of AF patients to catheter ablation and pharmacological treatment, through in-silico trials in large cohorts of virtual patients. For this, a computational framework is developed based on multi-scale human modelling and simulation. The framework allows predicting and explaining how AF management can be modulated based on several patient characteristics, such as electrophysiology, tissue structure and anatomy of the human heart.</p>
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<p>Throughout the thesis, AF inducibility and maintenance have been evaluated in large populations of virtual patients with variability in relevant clinical features. Different mechanisms of the arrhythmia (i.e., stable micro-re-entries, wavefront meandering, transient rotors and wave breakups) have been linked to specific ionic current dysregulations and their relationship with the structurally-remodelled substrate (i.e., presence of low voltage areas and atrial enlargement).</p>
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<p>Besides influencing AF dynamics, I have provided evidence that the ionic current profile of the atria modulates the success of pharmacological treatment. Therefore, an accurate estimation of the ionic current substrate might be crucial to optimally stratify patients to antiarrhythmic drug therapy. In this sense, a non-invasive characterisation of the ionic profile of the atria results from the analysis of the electrocardiogram.</p>
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<p>Moreover, the ionic current substrate also influences the outcomes of catheter ablation therapy, although the latter is mainly determined by the presence of heterogeneities in tissue structure (i.e., presence of low voltage areas) and the bi-atrial size (i.e., left and right atrial volume). In fact, analysis of nine state-of-the-art catheter ablation strategies suggests that the assessment of the latter patient characteristics could guide stratification of AF patients to optimal ablation therapies.</p>
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<p>The digital evidence obtained from the in-silico trials has informed decision algorithms to guide optimal selection of AF treatment. Importantly, this thesis demonstrates overall agreement between simulated and clinical results, supporting the credibility of the in-silico trials, and provides novel insights for the evaluation of novel and existing treatments for AF, which facilitates translation into clinical practice.</p>
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