Virtual Simulation Study of Heart Rhythm Problems Promises Better RWE Patient Data
The first computer model to predict several rather than a single cardiac mutation at a time could help us understand the real-world consequences of genetics and lifestyle to the heart for patients.
Researchers at the Aab Cardiovascular Research Institute at the University of Rochester Medical Centre, Australia tested a computer model of a heart wall on more than 600 patients with Long QT syndrome type 1. This model, designed by scientists at IBM, developed 192 health cells to function electronically by assigning different properties to each cell depending on their position in the heart wall. The model simulated a real-life heartbeat through the solution of more than 100,000 complex mathematical equations conducted at least 1,000 times over.
The scientists achieved this electronic heart through studying previous extensive data on the electrical properties of the human heart and canine cardiac cells. The 600 patients chosen for this study suffered from an inherited disorder that puts them at a bigger risk of arrhythmias and sudden cardiac death. Patients were sourced from Long QT syndrome databases in the US, Denmark, Sweden, Japan and the Netherlands. Individuals with the Long QT syndrome disorder have mutations in a specific gene known as KCNQ1. This study utilised 34 different mutations of the gene together with patient genetic tests and blood samples. The research team developed all of the mutant proteins in the lab and then tested them in an array of cell lines in order to discover the nature of these mutations and the defects they cause.
Findings stated that a simple computer model can give a better insight into a more complex electrocardiogram (ECG) for real world patients. It was revealed that a computer simulation can predict a lifelong risk of health rhythm issues while a traditional ECG will only show a single problem at a time. Through this sophisticated computer model, researchers could investigate the real-world effects of new drugs on the electrical activity of the heart. At present, the ability to predict the real-life effects of new drugs is one of the biggest challenges. This complex computer simulation could also keep potentially dangerous drugs off the market meaning that real-world patients would benefit from safer, more efficient medicines.
The real-world connotations of this predicative computer model are infinite. Professor of Cardiology at the University of Rochester Medical Centre Arthur Moss said: “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.This type of knowledge is going to help us better predict risk for each individual patient and provide more aggressive prevention and treatment strategies for those who need it.”
Moss believes that this model highlights the current trend of medical practitioners moving towards diagnosing disease through basic genetics rather than simple signs or symptoms. Cardiac modeller at IBM Jeremy Rice said: “This is a very powerful study because we used so many different mutations. Often, 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 [previous] 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.”
In the future, this research team will continue to work on computer simulations using IBM’s new supercomputer known as the Blue Gene/Q. It is hoped that this supercomputer will help develop a gender-specific model to better understand the type of drugs that cause dangerous disruptions of the heart’s electrical activity.