Personalized virtual heart can predict the risk of sudden cardiac death
When electrical waves in the heart run amok, the results can be deadly. Current treatment for the condition, called arrhythmia, includes implanting a small defibrillator which senses the onset of arrhythmia and jolts the heart back to a normal rhythm. But a thorny question remains: How should doctors decide which patients truly need an invasive, costly electrical implant that is not without health risks of its own?
To address this question, an interdisciplinary Johns Hopkins University team has developed a non-invasive, 3-D virtual heart assessment tool to help doctors determine whether a particular patient faces the highest risk of a life-threatening arrhythmia and would benefit most from a defibrillator implant. In a proof-of-concept study published today in the online journal Nature Communications, the team reported that its new digital approach yielded more accurate predictions than the imprecise blood pumping measurement now used by most physicians.
“Our virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events,” says Natalia Trayanova, professor of biomedical engineering. “This non-invasive and personalized virtual heart-risk assessment could help prevent sudden cardiac deaths and allow patients who are not at risk to avoid unnecessary defibrillator implantations.”
Trayanova, a pioneer in developing personalized imaging-based computer models of the heart, supervised the research and was senior author of the journal article. She holds faculty appointments within Johns Hopkins’ Whiting School of Engineering and its School of Medicine, and she is a core faculty member of the university’s Institute for Computational Medicine. For this study, she joined forces with cardiologist and co-author Katherine Wu, associate professor in the Johns Hopkins School of Medicine, whose research has focused on magnetic resonance imaging approaches to improving cardiovascular risk prediction. Excerpted from The Hub at Johns Hopkins University.