Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 123, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2020.103895
Keywords
Vectorcardiogram; Non-ischaemic dilated cardiomyopathy; Scar; Fibrosis; Conduction slowing; Computer modelling; Random forests
Categories
Funding
- National Institute for Health Research (NIHR) Biomedical Research Centre, UK
- CRF based at Guy's and St Thomas' NHS Foundation Trust and King's College London, UK
- Wellcome EPSRC Centre for Medical Engineering at King's College London [WT 203148/Z/16/Z]
- Medical Research Council, UK [MR/N011007/1]
- Wellcome Trust Innovator award [213342/Z/18/Z]
- MRC [MR/N011007/1] Funding Source: UKRI
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Patients with scar-associated fibrotic tissue remodelling are at greater risk of ventricular arrhythmic events, but current methods to detect the presence of such remodelling require invasive procedures. We present here a potential method to detect the presence, location and dimensions of scar using pacing-dependent changes in the vectorcardiogram (VCG). Using a clinically-derived whole-torso computational model, simulations were conducted at both slow and rapid pacing for a variety of scar patterns within the myocardium, with various VCG-derived metrics being calculated, with changes in these metrics being assessed for their ability to discern the presence and size of scar. Our results indicate that differences in the dipole angle at the end of the QRS complex and differences in the QRS area and duration may be used to predict scar properties. Using machine learning techniques, we were also able to predict the location of the scar to high accuracy, using only these VCG-derived rate-dependent changes as input. Such a non-invasive predictive tool for the presence of scar represents a potentially useful clinical tool for identifying patients at arrhythmic risk.
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