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Project Topic
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Atrial Fibrillation (AF) is the most common cardiac arrhythmia, with incidence increasing with age: 10% of the population >80 years is afflicted with it. As AF is progressive, over time it is harder to treat, which increases risk of stroke, dementia and heart failure. The most effective treatment is catheter ablation therapy (CAT) which selectively destroys tissue to create lesions blocking conduction; however, CAT follows generic patterns, without personalisation. AF often recurs after treatment, with >25% of patients requiring re-ablation after 2 years.
We aim to develop a personalised medicine approach based on computer modelling, to plan AF ablation to prevent recurrence. We propose to use physiological digital twins of patient hearts, created from imaging (MRI/CT), and calibrated using machine learning, to analyse and fit ECG and electrogram recordings acquired clinically, from implantable devices or wearables. Novel technology for real-time simulation of AF will be developed and integrated in a clinically viable platform to support the easy flow, robust analysis and interpretation of information, to achieve a scalable translation to large cohorts, and, thus, to enable clinicians to speed up the translation of observations to diagnosis and therapy planning.
Due to inherent uncertainty in measurements, anatomical structures, and properties, multiple AF scenarios will be simulated to derive biomarkers for assessing risk of AF progression, and determine potential ablation sets for each individual prior to CAT. Intraoperatively, electroanatomic recordings will be used on-the-fly to determine which simulation corresponds best to the patient, with its optimal ablation set. Platform development will use large-scale retrospective clinical data, but will be equally applicable to prospective trials. Economic analysis will evaluate benefits arising from early preventative and longer-lasting treatment, reduced duration and procedural risks of interventions.
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