Natalia A. Trayanova (Department of Biomedical Engineering and Medicine, School of Medicine)

Katherine C. Wu (Department of Medicine, Division of Cardiology, School of Medicine)

Dan M. Popescu (Department of Applied Mathematics and Statistics, , Whiting School of Engineering)

The goal of the research proposed here is to develop and utilize in clinical practice groundbreaking targeted strategies for predicting risk of sudden cardiac death (SCD) from arrhythmias. The proposed research will utilize a novel disease-specific personalized virtual-heart approach combined with machine learning on clinical data to predict the functional electrical behavior of the patient’s heart under a variety of stressor conditions and unmask potential dysfunctions. The robust disease-specific personalized risk assessment approaches proposed here are expected to lead to a radical change in patient stratification for SCD risk and selection for prophylactic implantable defibrillator deployment. This will result in a dramatically improved SCD prevention and in elimination of unnecessary device implantations, engendering precise clinical decision-making regarding personalized treatment.