1. A computational investigation into rate-dependant vectorcardiogram changes due to specific fibrosis patterns in non-ischæmic dilated cardiomyopathy
- Author
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Edward J. Vigmond, Karli Gillette, Martin J. Bishop, Ronak Rajani, Gabriel Balaban, Gernot Plank, and Philip M. Gemmell
- Subjects
0301 basic medicine ,Cardiomyopathy, Dilated ,medicine.medical_specialty ,Heart Ventricles ,Health Informatics ,Article ,Rapid pacing ,03 medical and health sciences ,QRS complex ,Cicatrix ,Electrocardiography ,0302 clinical medicine ,Scar ,Fibrosis ,Vectorcardiogram ,Internal medicine ,medicine ,Humans ,Arrhythmic risk ,business.industry ,Myocardium ,Dilated cardiomyopathy ,Non-ischæmic dilated cardiomyopathy ,Random forests ,medicine.disease ,Computer Science Applications ,030104 developmental biology ,Computer modelling ,Cardiology ,Conduction slowing ,business ,030217 neurology & neurosurgery - Abstract
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., Graphical abstract, Highlights • Non-invasive assessment of scar arrhythmic risk remains a significant challenge. • Rate-dependent changes in vectorcardiograms convey the functional presence of scar. • Random forest machine-learning allows high accuracy for determining scar location.
- Published
- 2020