Predicting cardiac event risk in silico

Michael Rosengarten BEng, MD [creativecommons license] via Wikimedia Commons

Long QT syndrome type 1 (LQT1) is an inherited disorder in which mutations cause a loss of function of the gene KCNQ1. Electrocardiography of affected individuals shows a longer than normal QT interval, representing a prolonged repolarization of the heart wall during the cardiac cycle. This prolongation results in a greater risk of developing arrhythmias and related cardiac events. Arrhythmia caused by mutations in KCNQ1 has been modeled in mice and in zebrafish and has been studied clinically, but attempts to identify the risk levels associated with specific mutations have so far been unsuccessful. Such a risk stratification would help to identify those who are most likely to experience cardiac events, guiding treatment strategies. With this goal in mind, Coeli M. Lopes and Ilan Goldberg (University of Rochester School of Medicine and Dentistry, NY) worked with scientists at IBM campuses in NY and in Melbourne, Australia, to develop a computer model that predicts the effects of different KCNQ1 mutations on transmural repolarization potential (TRP) and their consequent risks for cardiac events.

Lab Anim. (NY) 43, 42 (2013).
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