Advancing Personalized Medicine For Ventricular Arrhythmias

To better understand ventricular arrhythmias, Johns Hopkins University researchers are studying the use of whole-heart computational models. Whole-heart computational modeling can lead to personalized medicine and predict a patient’s risk of sudden cardiac death or outcomes of cardiac procedures.

Whole-heart ventricular modeling is currently witnessing an evolution of a variety of computational approaches, especially when pursuing personalized treatment and technologies. Ventricular arrhythmias are one of the leading causes of mortality worldwide. With whole-health computational models, researchers hope to gain a better understanding of the heart condition.

The researchers described using various computational approaches to address the mechanisms of cardiac dysfunction and problems related to the clinical application of computation-drive diagnostic and therapeutic approaches for cardiac disease and arrhythmias.

Through basic science experiments, it was determined that the heart’s electrical properties can be modeled via fundamental biophysical principles. Whole-heart computational models are multiscale, meaning they factor in both cellular and organ-level properties. These models include most of the biophysical complexity of an individual’s cardiac pathology.

The complex biophysical system “can be represented using a set of mathematical equations,” co-author and professor of biomedical engineering and medicine at Johns Hopkins University, Natalia Trayanova, said in a press release. “Solving these equations using computer software allows us to run detailed simulations to mimic the heart’s electrical activity.”

Additionally, computational models of the heart linking cellular electrophysiology to whole-organ behavior are showing promise as platforms for in-silico evaluation of novel diagnostic and therapeutic techniques.

“Personalized computational modeling of patient hearts is making strides developing models that incorporate the individual geometry and structure of the heart, as well as other patient-specific information,” Trayanova said.

According to researchers, these patient-specific models can also use predictive analytics to determine the risk of sudden cardiac death or the outcomes of a cardiac procedure.

“Patient-specific models are also used for determining the optimal treatment for arrhythmia, both atrial and ventricular, with the latter often based on different biophysical underpinnings,” said Trayanova. “These types of models can enable fast evaluation of medical device settings and patient-​selection criteria, as well as the development of novel therapeutic agents.”

“Computational modeling can also be combined synergistically with machine learning approaches to better account for the information available within patient health records.”

By pursuing the development of whole-heart computational models, researchers can enhance personalized treatment and risk predictions. Additionally, the model can work collaboratively with artificial intelligence to improve patient outcomes.