Abstract:

Cardiovascular diseases are the leading cause of death worldwide, and atrial fibrillation (AF) is the most prevalent cardiac arrhythmia affecting more than 6 million individuals in Europe, with a cost exceeding 1% of the EU health care system budget (13,5 billion annually). New treatment strategies and the progress achieved in research on AF mechanisms and substrate evaluation methods to date have not been commensurate with an equivalent development of the knowledge and technologies required to individually characterize each patient in search of the most efficient therapy. Catheter ablation is the suggested treatment when anti-arrhythmic drugs are not effective. However, the success rates are still not satisfactory, and a large share of AF patients need to undergo multiple ablation procedures. Computational modelling of the atria bears the potential of better understanding AF patho-mechanisms and tailoring ablation therapy. The content of the thesis is split into two projects involving computer models of human atria anatomy and electrophysiology to build a bridge between medicine and engineering.The first project leverages computer models to provide deeper insights into AF vul- nerability and maintenance mechanisms. This work aimed first at evaluating the impact of the choice of the protocol on AF onset and perpetuation in in-silico experiments. An efficient and automated method to standardize the assessment of AF vulnerability in atrial models was proposed. Then, this thesis sought to quantify the influence of heterogeneous anatomical thickness in AF onset and continuation. AF episodes were induced and analyzed on highly detailed 3D atrial models, including various regional myocardial thicknesses. The results of this work confirmed the findings of previous experimental studies proposing highly heterogeneous anatomical thickness as susceptible areas to maintain AF and probable regions to ablate.In the second project, I enter the realm of virtual replicas of patients’ hearts to tailor arrhythmia characterization and treatment, the so called cardiac digital twins. The aim was to develop algorithms delivering atrial models with personalized anatomy augmenting clinical information. This technology was incorporated in a unique pipeline to provide atrial digital twins with patient-specific electrophysiology integrating multiple clinical datasets. The advances described enable the generation of accurate personalized atrial models from medical images and electro-anatomical data. In parallel to advancing the models’ personal- ization, major progress was achieved in translating them into clinical application to support ablation therapy. An automated platform to assess AF vulnerability and identify the optimal ablation targets on anatomical and functional digital twins was realized to provide clinicians personalized ablation plans to stop AF onset and perpetuation. This work highlighted the im- portance of digital twins in optimizing existing and exploring new diagnostic and therapeutic approaches, complementing and enriching clinical information with simulated data.Computational modelling facilitated the multilevel integration of multiple datasets and offered new opportunities for mechanistic understanding, risk prediction and personalized therapy. This thesis changes the paradigm of classification and diagnosis of AF by delivering individualized digital twin models of patients to personalize medicine, unraveling fundamen- tal physiological and pathological mechanisms with implications for clinical treatments.