Improving Risk Prediction for Sudden Cardiac Death through Multiscale Computational Modeling of Microvolt T-Wave Alternans

Abstract

Sudden cardiac death (SCD) is one of the leading causes of death worldwide. The current most effective therapy to prevent SCD is the implantable cardioverter defibrillator (ICD). The problem is that the risks and costs involving this intervention are high; thus there is a need to improve SCD risk stratification methods. One of these non-invasive methods is through detection of microvolt T-wave alternans (MTWA), which are microvolt beat-to-beat magnitude alternations in the electrocardiogram (ECG) recordings. Although the current MTWA tests detect these alternations, they give no information about presence of spatial heterogeneities of these alternans in the heart, which are known to lead to higher risk of SCD. Using two cardiac modeling and simulation approaches, based on fixed transmembrane potential models (ECGSim) and ionic anatomical heart models, we have been able to generate different MTWA spatial distributions in the heart and propagate them to the torso surface. Our results indicate that different spatial distributions of MTWA in the heart propagate to different spatial alternan distributions on the body surface. These differences in spatial distributions on the tors, have great potential to be used to improve the robustness of current MTWA detectors and also increase the ability to assess SCD risk by detecting heterogeneities of the MTWA in the heart. In addition to modeling studies, we are currently exploring the presence of these differences in measured electrocardiograms from MTWA patients.