Key result
A stochastic individual-based model of atrial fibrillation progression predicted that the first episode lasting >14.4 minutes occurs at 44-48 years of age, progressing to permanent AF over 28-32 years.
A novel stochastic individual-based model provides a framework for simulating atrial fibrillation progression over a patient's lifetime, aiming to bridge mechanistic models and epidemiological data.
We propose a stochastic individual-based model of the progression of atrial fibrillation (AF). The model operates at patient level over a lifetime and is based on elements of the physiology and biophysics of AF, making contact with existing mechanistic models. The outputs of the model are times when the patient is in normal rhythm and AF, and we carry out a population-level analysis of the statistics of disease progression. While the model is stylised at present and not directly predictive, future improvements are proposed to tighten the gap between existing mechanistic models of AF, and epidemiological data, with a view towards model-based personalised medicine.
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Chang et al. (2015) studied Atrial fibrillation (n=5,000). Stochastic individual-based computational model was evaluated on Age at onset of paroxysmal AF and time to progression to permanent AF. A stochastic individual-based model of atrial fibrillation progression predicted that the first episode lasting >14.4 minutes occurs at 44-48 years of age, progressing to permanent AF over 28-32 years.
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