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. Model input included genetic and physiological data from 633 people with 34 different mutations leading to LQT1. The model itself comprised 192 cells, based on canine cardiac cells, that were assigned to one of three locations within the heart wall and were given different electrophysiological properties accordingly (J. Am. Coll. Cardiol. 60, 2182–2191; 2012).

The research team found that mutation-specific TRP was associated with an increased risk for cardiac events, including syncope, aborted cardiac arrest and sudden cardiac death. For every 10 ms of prolongation, risk of cardiac events increased by 35% and risk of life-threatening events increased by 27%. The results suggest that the computer model can be used to predict clinical outcomes and improve risk assessment in people with LQT1. “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,” Lopes said in a press release.

Application of the model may be particularly valuable for people with mild to moderate QT prolongation, in whom QT interval measurements are less reliable for predicting cardiac events. In addition, the results identify four KCNQ1 mutations that are associated with the highest risk of cardiac events. The study is the first to use computer simulation to predict arrhythmia risk and may lead other researchers to apply more complex cardiac models to evaluate the effects of other genetic and lifestyle factors on heart rhythm.