Systolic dysfunction and extensive non-ischemic myocardial fibrosis are common structural features in heart failure patients at high risk of ventricular arrhythmias and sudden cardiac death (SCD), a leading cause of mortality worldwide. In Brazil, these patients often undergo invasive electrophysiological studies (EPS), involving catheter insertion and programmed electrical stimulation to induce arrhythmias for diagnostic purposes. Despite its effectiveness, EPS is costly, invasive, and poses significant risks.
In this work, we propose the use of a personalized digital twin as a minimally invasive alternative for the virtual replication of EPS. Magnetic resonance imaging (MRI) and electrocardiogram (ECG) data were obtained from a heart failure patient treated at the university hospital of the Federal University of Juiz de Fora (HU-UFJF). Ventricular structures and fibrotic regions were segmented from the MRI images, allowing the construction of a three-dimensional computational cardiac model that incorporates tissue heterogeneity. The model differentiates three regions: healthy myocardium, fibrotic tissue, and a border zone (BZ). The healthy myocardium was modeled as a homogeneous conductive medium, while dense fibrosis was represented as a completely non-conductive region. The BZ was modeled in layers, with an increasing proportion of active cells from the fibrotic core to the healthy tissue.
Sixty electrophysiological variants of a single cardiac digital twin were generated, with variable BZ thicknesses and randomly generated microscopic fibrosis patterns in the border zone. Model conductivity was calibrated based on the patient's QRS complex duration from the ECG. The calibrated models were then used to virtually simulate the clinical EPS protocol.
The results showed that only fibrotic configurations with thick and heterogeneous border zones were able to reproduce the arrhythmias observed in the real patient, highlighting the critical role of microstructural variability in the formation of ventricular arrhythmias.