Uncertainty Quantification in Prospective and Predictive Patient Specific Cardiac Models

Lead Research Organisation: University of Sheffield
Department Name: Computer Science


Clinical diagnosis is seldom definitive. Clinical data are noisy and sparse, and often support multiple diagnoses and potential therapies. To decide how best to treat a patient requires identifying the many possible outcomes for an individual and their corresponding probabilities. In this project we will apply the mathematics of uncertainty quantification, developed for automotive, geological and meteorological predictions, combined with biophysical models of individual patient physiology and pathophysiology to predict patient outcomes and their corresponding probabilities. This will demonstrate how patient specific computational models can be used to make prospective predictions to guide procedures and inform uncertain clinical decisions.

The use of uncertainty quantification and predictive patient specific models will be applied to patients with atrial fibrillation. Atrial fibrillation (AF) is the most common cardiac arrhythmia in the UK. In patients who do not respond to drug treatment, the pathological regions of the atria are removed or isolated through catheter ablation. However, up to 40% of patients with advanced (persistent) AF require further ablations to treat atrial tachycardia (pathological but regular activation) that develops after they have had an initial ablation to treat their AF. To reduce the number of additional procedures, this project will predict the probability that a patient will develop atrial tachycardia and the path that the atrial tachycardia will take, based on measurements recorded at the time of the initial persistent AF ablation procedure. If successful this approach would guide preventative ablations during the initial procedure to reduce the need for repeat procedures.


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Clayton RH (2020) An audit of uncertainty in multi-scale cardiac electrophysiology models. in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

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Coveney S (2020) Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity. in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

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Coveney S (2020) Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds. in IEEE transactions on bio-medical engineering

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Coveney S (2018) Fitting two human atrial cell models to experimental data using Bayesian history matching. in Progress in biophysics and molecular biology

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Lei CL (2020) Considering discrepancy when calibrating a mechanistic electrophysiology model. in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

Description We have implemented a novel method that enables us to take a small number of noisy measurements of electrical activation in the heart, and use them to interpolate and extrapolate activation times over the surface of the left atrium. This method is novel because there it accounts for uncertainties in the measurement as well as the interpolation and extrapolation, so the output is a best estimate of local activation time with an associated confidence in the estimate. This method has been extended to estimate the speed of the electrical activation wave across the left atrium in the heart, and we are working on ways to assess how uncertainty and variability in the shape of the left atrium affects speed.

We have also undertaken a systematic and detailed analysis of simulation models, so as to identify model parameters that can be identified from routinely available patient data, as well as those that cannot. This work has enabled us to develop a methodology for calibrating personalised models of individual patients, which embeds uncertainties arising from variability and noise in the data, as well as uncertainties that are associated with the methodology.

This method has been evaluated using simulated patient data, and we have shown that it can be used to identify pacing protocols that optimise the information that can be obtained in the clinical setting.
Exploitation Route There are many ways that the outcomes of this award could be taken forward. These include the use of these techniques to guide clinical interventions, as well as direct implementation of our methods in medical equipment. However, as of February 2021 these outcomes are speculative at this stage.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology

Description Atrial UQ 
Organisation King's College London
Department Department of Biomedical Engineering
Country United Kingdom 
Sector Academic/University 
PI Contribution Sheffield is providing expertise on uncertainty quantification.
Collaborator Contribution King's College London is providing patient data and analysis expertise.
Impact No outputs yet.
Start Year 2017
Title quaLATi -- Quantifying Uncertainty for Local Activation Time Interpolation 
Description This package is for Quantifying Uncertainty for Local Activation Time Interpolation. It implements Gaussian Process Manifold Interpolation (GPMI) for doing Gaussian process regression on a manifold represented by a triangle mesh. 
Type Of Technology Software 
Year Produced 2020 
Open Source License? Yes  
Impact This software underpins several publications including https://doi.org/10.1098/rsta.2019.0345