11 May Personalized Virtual Heart Map Allows For Better Prediction of Sudden Death Risk

MedicalResearch.com Interview with:

Dr. Natalia Trayanova

Natalia Trayanova PhD, FHRS, FAHA
Murray B. Sachs Endowed Chair
Professor of Biomedical Engineering
Joint Appointment, Medicine
Johns Hopkins University
Institute for Computational Medicine
Johns Hopkins University
Baltimore, MD

MedicalResearch.com: What is the background for this study? What are the main findings?
Dr. Trayanova: The methodology for modeling cardiac electrical function has matured sufficiently that we can now create computational models of the electrical functioning of the entire heart. My research is focused on translating this methodology into the clinic. The goal is to create, if you will, “a virtual heart for every patient”, that will enable the physician to play our scenarios that manifest the heart dysfunction in the given patient, and to enable physicians to make personalized decisions about patient treatment. The present paper is the first application of this overall vision.

The motivation for this particular paper was that determining which patients are at risk for sudden cardiac death represents a major unmet clinical need. Patients at risk receive life-saving implantable defibrillators (ICDs), but because of the low sensitivity and specificity of current approach (based on low ejection fraction), risk assessment is inaccurate. Thus, many patients receive ICDs without needing them, while others die of sudden cardiac death because they are not targeted for ICD therapy under the current clinical recommendations. Our goal was to develop a non-invasive personalized virtual-heart risk assessment tool that has the potential to ultimately prevent sudden cardiac death and avoid unnecessary ICD implantations.

MedicalResearch.com: What should readers take away from your report?

Dr. Trayanova: Our prediction of which patients were at risk was very accurate. The analysis demonstrated that our virtual-heart noninvasive approach to predicting arrhythmia risk in patients with heart injury is superior to the current clinical metric, ejection fraction, as well as other noninvasive and invasive predictors.

MedicalResearch.com: What recommendations do you have for future research as a result of this study?

Dr. Trayanova: The approach will have to be tested in a larger study, with many more patients in a clinical trial — we did only 41. Because we can predict which patients are at risk, we hopefully will be able to use the approach to predict which patients don’t actually need defibrillators. We also want to use the virtual heart approach to determine individualized treatment options, or conduct pre-procedure planning. Doctors could play out treatment scenarios in the virtual heart, and determine which treatment option would work best in the given patient. 

MedicalResearch.com: Thank you for your contribution to the MedicalResearch.com community.

Citation:

Hermenegild J. Arevalo, Fijoy Vadakkumpadan, Eliseo Guallar, Alexander Jebb, Peter Malamas, Katherine C. Wu, Natalia A. Trayanova. Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nature Communications, 2016; 7: 11437 DOI: 10.1038/ncomms11437

Note: Content is Not intended as medical advice. Please consult your health care provider regarding your specific medical condition and questions.

More Medical Research Interviews on MedicalResearch.com

[wysija_form id=”5″]

Last Updated on May 11, 2016 by Marie Benz MD FAAD