Details of Grant 

EPSRC Reference:	EP/P01268X/1
Title:	Uncertainty Quantification in Prospective and Predictive Patient Specific Cardiac Models
Principal Investigator:	Niederer, Dr SA
Other Investigators:	O'Neill, Professor M
Researcher Co-Investigators:
Project Partners:	Medical University of Graz University of Luebeck Zuse Institute Berlin
Department:	Imaging & Biomedical Engineering
Organisation:	Kings College London
Scheme:	Standard Research
Starts:	01 May 2017	Ends:	31 January 2022	Value (£):	765,474
EPSRC Research Topic Classifications:	Med.Instrument.Device& Equip.
EPSRC Industrial Sector Classifications:	Healthcare
Related Grants:	EP/P010741/1
Panel History:	Panel Date Panel Name Outcome 01 Dec 2016 Engineering Prioritisation Panel Meeting 1 and 2 December 2016 Announced
Summary on Grant Application Form
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.
Key Findings
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