A computational investigation into rate-dependant vectorcardiogram changes due to specific fibrosis patterns in non-ischæmic dilated cardiomyopathy. (August 2020)

• Record Type: Journal Article
• Authors: Gemmell, Philip M.; Gillette, Karli; Balaban, Gabriel; Rajani, Ronak; Vigmond, Edward J.; Plank, Gernot; Bishop, Martin J.
• Is Part Of: Computers in biology and medicine. Volume 123(2020)
• Publication Date: 2020-08
• Subjects: Vectorcardiogram -- Non-ischæmic dilated cardiomyopathy -- Scar -- Fibrosis -- Conduction slowing -- Computer modelling -- Random forests; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103895
• Languages: English
• ISSNs: 0010-4825

Abstract

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.