Myocardial infarction (MI) is a common cardiovascular disease that causes irre-
versible damage to the left ventricle (LV) myocardium, resulting in the formation of
scar tissue. When this phenomenon occurs, reentry circuits appear, generating alter-
native conduction channels. This is associated with an increased risk of developing
ventricular arrhythmias and consequently, sudden cardiac death. Patient-specific 3D
computational modelling and simulations can be used to predict non-invasively ...
Myocardial infarction (MI) is a common cardiovascular disease that causes irre-
versible damage to the left ventricle (LV) myocardium, resulting in the formation of
scar tissue. When this phenomenon occurs, reentry circuits appear, generating alter-
native conduction channels. This is associated with an increased risk of developing
ventricular arrhythmias and consequently, sudden cardiac death. Patient-specific 3D
computational modelling and simulations can be used to predict non-invasively the
reentry circuits causing ventricular tachycardia (VT). Cellular automata (CA) elec-
trophysiological models allow to reproduce VT while performing simulations near
real-time, overcoming the computational burden limitations of biophysical models.
The aim of the present study was to create computational cardiac models capable of
stratifying VT inducibility in infarcted patients by virtually applying the real pacing
protocol followed in the clinic using a novel CA-based solver developed at Universi-
tat de València, in which no real clinical data has been tested yet. 3D computational
LV and biventricular models were obtained from cardiac magnetic resonance images
provided by Centro Médico Teknon, allowing to identify the scar configuration and
arrhythmogenic substrate of each patient. The models were reconstructed to fit in
the CA, and seven pacing sites were defined to apply the virtual pacing protocol.
The obtained in-silico simulations results were compared with the actual results ob-
tained by patients during an electrophysiological study (EPS). The similarity of the
results between in-silico and EPS demonstrated that the novel CA-based fast elec-
trophysiological simulator together with the implementation of real pacing protocols
were valid for assessing VT risk in infarcted patients.
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