Myocardial architecture and patient variability in clinical patterns of atrial fibrillation

Abstract

Atrial fibrillation (AF) increases the risk of stroke by a factor of 4–5 and is the most common abnormal heart rhythm. The progression of AF with age, from short self-terminating episodes to persistence, varies between individuals and is poorly understood. An inability to understand and predict variation in AF progression has resulted in less patient-specific therapy. Likewise, it has been a challenge to relate the microstructural features of heart muscle tissue (myocardial architecture) with the emergent temporal clinical patterns of AF. We use a simple model of activation wave-front propagation on an anisotropic structure, mimicking heart muscle tissue, to show how variation in AF behavior arises naturally from microstructural differences between individuals. We show that the stochastic nature of progressive transversal uncoupling of muscle strands (e.g., due to fibrosis or gap junctional remodeling), as occurs with age, results in variability in AF episode onset time, frequency, duration, burden, and progression between individuals. This is consistent with clinical observations. The uncoupling of muscle strands can cause critical architectural patterns in the myocardium. These critical patterns anchor microreentrant wave fronts and thereby trigger AF. It is the number of local critical patterns of uncoupling as opposed to global uncoupling that determines AF progression. This insight may eventually lead to patient-specific therapy when it becomes possible to observe the cellular structure of a patient’s heart.

Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model

“…P waves) for defined periods. Similar methods have been adopted by Manani [12]. In the following analysis, we set the non-zero threshold to be 0.5 and the time period to be 2 seconds.…”

“…CA models have been superseded by more biophysically detailed models recently [24], but are still employed, both in standalone theoretical studies and combined with clinical investigations [11,25,12,26]. Our approach complements, for example, studies by Manani [12], who similarly used a CA formulation, to investigate effect of time dependent fibrosis on arrhythmia susceptibility. Our model naturally handles variability and uncertainty through its stochastic formulation and the large number of sample paths, and thus permits a systematic investigation within the model framework, whilst accepting the model limitations.…”

“…Directional fibrosis: In this work we modelled fibrosis by setting nodes to be electrically active, whereas fibrosis may act to promote faster propagation in certain directions within cardiac tissue [12]. This could be be achieved in our CA model by assigning weights to neighbouring nodes.…”

“…Our contribution can broadly be summarised as follows. First, in contrast to previous cellular automata models [11,12], which used a simplified geometry, we generalised to a more realistic topology representing the left atrium. Whilst the geometry is still stylised we think this is a step towards reality.…”

Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation

“…AF is driven through the spontaneous emergence of re-entrant circuits which generate rapid, irregular atrial activity. This is reflected in the electrograms generated from the model which are fractionated during AF [ 15 ]. This mechanism is a form of micro-anatomical re-entry.…”