More than 350,000 people each year will experience an out of hospital cardiac arrest. Cardiac arrest is an extremely dangerous circumstance that requires immediate treatment. In cardiac arrest, death results when the heart suddenly stops working properly. This may be caused by abnormal, or irregular, heart rhythms (called arrhythmias). Since prior heart attack, or myocardia infarction, is a major risk factor for arrhythmia, these patients are prime candidates for surgically implanted defibrillators, which monitor heart rhythm and deliver an electric shock if needed to keep the heart beating regularly.
The current tools for assessing whether a patient is likely to actually suffer an arrhythmia and therefore bene t most from the defibrillator (which carries its own risks) are not highly predictive. Dr. Natalia Trayanova, the Murray B. Sachs Professor of Biomedical Engineering and Medicine at Johns Hopkins University, and a team of researchers are working to change this. They have developed a computational model for predicting which heart patients are at greatest risk for arrhythmia. Called VARP, for virtual arrhythmia risk predictor, Dr. Trayanova’s virtual heart uses MRI and other patient-specific cardiac data to create a personalized geometrical model of the heart. The model incorporates not just the wall of the heart, but also all the structural remodeling that occurs after a heart attack. That computer model, coupled with mathematical equations that express the dynamics of the human cells of the heart, is then stressed in a variety of different ways and locations to see if a patient is at risk for sudden cardiac death due to arrhythmia.