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Predicting risk of arrhythmias and sudden cardiac death: There's a computer model for that
Date:12/13/2012

gnals. They suggest that the computer simulation may reflect a lifelong risk of heart rhythm problems, while the conventional ECG may show only a single point in time.

They identified four high-risk mutations that were predicted to have particularly extended repolarization periods and determined that patients with these mutations experienced the greatest number of cardiac events.

Study authors say the results show that a relatively simple model, used in the right hands with the proper experimental data, can give insight into complex ECGs from real patients. "Using this model, we can predict the likelihood that an individual will experience a deadly cardiac event based on the type of mutation they have and how that mutation acts," noted Lopes.

Rice, a seasoned cardiac modeler that led the IBM team, adds, "This is a very powerful study because we used so many different mutations. Oftentimes, scientists will study only one mutation at a time, and the research community remains unconvinced as the right answer may have come by luck. By comparing our results to so much patient data, we've shown that you can get meaningful information out of computer models, hopefully paving the way for wider acceptance and use in the medical community."

Arthur J. Moss, M.D., a world-renowned expert on electrical disturbances of the heart, says that research has shown that the location of a mutation plays an important role in assessing risk, but this is the first time scientists have used the function of a mutation to identify patients at higher risk.

"This kind of movement, from identifying a mutation, to locating where it is, and now to evaluating how the mutation functions, is going to happen in every area of medicine, from heart disease to cancer," said Moss, a study author and longtime professor of Cardiology at the University of Rochester Medical Center. "This type of knowledge is going to help us better predict risk for each individu
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Contact: Emily Boynton
emily_boynton@urmc.rochester.edu
585-273-1757
University of Rochester Medical Center
Source:Eurekalert

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