Studying Arrhythmic Risk with In-Silico Programmed Ventricular Stimulation and Patient-Specific Computational Models

Cardiac arrhythmias can be life-threatening, and early identification of patients at high risk of developing arrhythmias is crucial to implementing preventive measures. Programmed ventricular stimulation (PVS) is a clinical tool to assess arrhythmic risk. In this study, we developed patient-specific computational models using magnetic resonance imaging (MRI) data to evaluate arrhythmic risk through virtual PVS simulations. We applied virtual PVS on a patient with dilated cardiomyopathy and a history of non-sustained ventricular tachycardia. The simulation results revealed the presence of cardiac arrhythmias in the form of spiral waves circulating a fibrotic scar in the patient’s heart. These findings, consistent with the patient’s medical history, indicate that patient-specific computational models hold great promise as a tool for assessing cardiac arrhythmic risk. The patient-specific computational models have the potential to assist clinicians in identifying high-risk patients and developing personalized treatment plans. By incorporating patient-specific information and simulating various scenarios, computational models can provide valuable insights into the underlying mechanisms of arrhythmia and guide clinical decision-making.