ArticleComputers in biology and medicine2020
A computational investigation into rate-dependant vectorcardiogram changes due to specific fibrosis patterns in non-ischæmic dilated cardiomyopathy.
Article in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 21 citations in OpenAlex.
- Anatomical-electrical coupling of cardiac axes: Definitions and population variability for advancing personalised ECG interpretation.PLoS computational biology · 2025Article
- Article
- Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.Nature cardiovascular research · 2025Article
- The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.JMIR cardio · 2024Article
- Case report: Magnetocardiography as a potential method of therapy monitoring in amyloidosis.Frontiers in cardiovascular medicine · 2023Article
- Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar.PLoS computational biology · 2022Article
- Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction.Computers in biology and medicine · 2022Article
- Review of Processing Pathological Vectorcardiographic Records for the Detection of Heart Disease.Frontiers in physiology · 2022Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
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.
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Registered trials
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