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Patient-specific modelling of atrial fibrillation incorporating atrial anisotropy and fibrosis

Project ID: 2019_S13

1st supervisor: Steven Niederer, King’s College London
2nd supervisor: Mark O’Neill, King’s College London

Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting over 1.1 million people in the UK alone, and is associated with increased risk of cardiovascular diseases, stroke and death. Radio-frequency catheter ablation therapy, which creates lesions in cardiac tissue, is a routine treatment for drug refractory AF. Early in AF, isolating the pulmonary veins (PVI) using catheter ablation eliminates electrical triggers and is a successful treatment approach. With increasing AF duration, the atrial tissue undergoes electrical and structural fibrotic remodelling, and PVI catheter ablation treatment has a lower success rate. These persistent AF patients are a heterogeneous population: some patients require multiple procedures, with more extensive ablation strategies; while for others, PVI is enough, as indicated by the recent STAR AF II clinical trial. Identifying persistent AF patients where PVI will be a sufficient treatment remains a clinical challenge, which if solved could lead to improved safety, better patient selection, as well as decreased time and cost for procedures.

Previous clinical and modelling studies suggest that the amount and location of atrial fibrosis affects arrhythmia properties and ablation outcome. In particular, many of the changes that occur during AF that modify atrial conduction are associated with atrial fibrosis, including the deposition of collagen and interstitial fibrosis, as well as changes in atrial fibre direction, including fibre disarray. These components of fibrotic remodelling affect the heterogeneity and anisotropy of atrial conduction. The interpretation of atrial conduction properties and their relationship with scar tissue requires the use of atrial anisotropy and fibre direction information. However, currently atrial fibre direction cannot be recorded in vivo and atrial electrical anisotropy is unknown. Characterising the atrial substrate scar and conduction properties may inform ablation approaches through improved understanding of the arrhythmia substrate or through patient-specific modelling.

The overall aim of this project is to construct intra operative patient-specific models of AF patients that include personalised atrial anisotropy information and fibrosis distribution to predict the outcome of ablation strategies. To achieve this, we will address the following key objectives:

  • To characterise atrial anisotropy across a healthy patient cohort as a mean atlas
  • To use atrial anisotropy measurements together with intra procedure atrial pacing and fibrosis imaging data to dissect the effects of atrial fibrosis on arrhythmia properties
  • To use patient-specific computational models to investigate the effects of electrical and structural properties on arrhythmia conduction patterns
  • To develop a computational tool to predict whether PVI is likely to work for a given persistent AF patient both on integrated pre-procedure and intra-procedure measurements.

This project will provide training in computational modelling, signal and image processing techniques, and machine learning algorithms, applied to the field of cardiac electrophysiology. Specifically, the project involves analysing imaging and electrical data from persistent AF patients, in close collaboration with the clinical teams at GSTT. These data will be used to tune biophysical models to investigate the effects of electrical and structural properties on AF.

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