Quantifying uncertainty in cardiac electrophysiology predictions (360G-Wellcome-206981_Z_17_Z)

There are many factors that influence cardiac electrophysiology in a particular patient. These include the size and shape of the heart, the orientation of tissue fibres within it, and the electrophysiology of different cells and cell types throughout the myocardium (from endo to epicardium, apex to base, and left to right). These properties are very difficult to measure precisely (or at all), and it is not yet known how uncertainties in these properties affect the predictions that we make for electrical activation and the likelihood of arrhythmias. In this project we will use techniques from weather forecasting to estimate the uncertainty in predictions. Simulations are run repeatedly with different conditions. The spread of the subsequent model predictions increases over time, and allows uncertainty to be estimated for the predictions, and for this to be communicated to the public. We will transfer these techniques to electrophysiology, estimating uncertainty in the factors above, and running multiple tissue simulations to estimate the uncertainty in model predictions, and to visualize this with the results we present. This is a first step to allowing predictions to be used in a clinical setting, when their results could directly impact or decide a patient's treatment.