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

ore than 100,000 complex mathematical equations at least 1,000 times over just to simulate a single heartbeat.

The team turned to more than 600 patients with Long QT syndrome type 1 an inherited disorder that puts patients at greater risk of arrhythmias and sudden cardiac death to help test the model. Patients were drawn from Long QT syndrome registries from the United States, Japan, the Netherlands, Denmark and Sweden.

Patients with the disorder have mutations in a specific gene, KCNQ1, which helps heart cells appropriately generate and transmit electrical signals. The study used 34 different mutations to the gene, identified with patient blood samples and genetic tests. To better understand the characteristics of each mutation how they act and the defects they cause the team recreated all of the mutant proteins in the lab and tested them in various cell lines.

Researchers plugged each mutation's electrical profile into the model to simulate the mutation's effect on the heart wall. The model produced a one-dimensional, transmural (across the heart wall) electrocardiogram or ECG for each mutation, which predicts how the mutation influences the electrical properties of the heart wall as it is excited and relaxes after each beat.

When the team compared this information to patient data, analyses revealed that mutations the model predicted would strongly prolong repolarization the time it takes for the heart wall to recover after each beat put patients at greater risk of potentially life-threatening events. For every 10 milliseconds that repolarization was delayed a patient's risk of sudden cardiac death or aborted cardiac arrest rose by approximately one third.

The authors note that this risk is separate from the increased risk observed for patients that show a very prolonged repolarization on traditional ECGs, when electrodes are placed on the skin and attached to a machine that records the heart's electrical si
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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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