According to the American Heart Association, more than 850,000 people per year die of cardiovascular disease in the United States alone. However, this number could be greatly reduced in the near future, thanks to technological advancements that allow for complex, personalized models of individual hearts to be created by uniting cardiovascular experts through 3D modeling and simulation.
The field of precision medicine, which uses scientific methods to customize medical treatment to individual patients, is rapidly growing. It holds a great deal of promise for reducing cardiovascular death rates and improving quality of life, though the exact role of precision medicine in human heart modeling has not yet been fully investigated.
In a paper entitled Precision Medicine in Human Heart Modeling, a team of scientists explores the challenges and opportunities involved in personalized heart simulations. The study takes a comprehensive look at the history of human heart models throughout the past three decades, and discusses the researchers’ own heart simulations, many of them using the Living Heart Model as a starting point.
In one example, the researchers—coming from Dassault Systèmes SIMULIA, Stanford University, Pontifica Universidad Catholica de Chile, the University of California, Thornton Tomasetti Inc., Capvidia, Duke University, and the United States Food and Drug Administration–reconstructed the Purkinje network, which is composed of specialized fast-conducting cells located in the subendocardium beneath the inner lining of the heart. A properly functioning Purkinje network is critical for creating synchronized contractions between the right and left ventricles. The network is also a major component in cardiac excitation. To better understand arrhythmias and their treatment, cardiac excitation needs to be effectively modeled.
Using the Living Heart Model as a baseline geometry, the researchers created a Purkinje network for rapid cardiac excitation. They used Abaqus/Standard to simulate the heart through multiple cardiac cycles and triggered excitation by applying an external stimulus to the Purkinje network in the location of the atrioventricular node.
They then post-processed the results of the simulation to create a virtual echocardiogram, placing electrodes in the right and left arm and the left leg. The researchers also compared healthy and diseased activation patterns, side by side.
Another aspect of the study looks at the effects of certain drugs on cardiac electrophysiology. Side effects of some drugs can cause cardiac arrhythmias. Risk evaluation for arrhythmias is necessary to avoid introducing potentially dangerous drugs to the market, but the high cost and long time needed to test new compounds hinders the development of new drugs. However, high-fidelity computational models have recently been used to quantify the effects of drugs on cardiac electrophysiology.
In this portion of the study, the researchers computed the heart’s electrical activity under different pharmacological conditions by adopting a monodomain model and simulating excitation across the ventricular geometry of the Living Heart Model. At the start of the simulation, they excited the Purknije network in the location of the atrioventricular node and used the automaticity of the Purkinje cells to drive the cardiac activation sequence for five seconds.
The researchers then studied the resulting activation sequence for three conditions: without drugs, with the drug Ranolazine, and with the drug Quinidine. They simulated the effect of each drug at the cellular level by selectively blocking specific ionic currents. This simulation predicted slightly altered activation patterns with prolonged QT intervals with Ranolazine, and spontaneous development of a potentially deadly arrhythmia with Quinidine.
These are only two examples of the researchers’ usage of simulation to create personalized heart models. You can read the full study here. With rapid developments occurring in machine learning, data-driven modeling, and physics-based simulation, it is likely, according to the researchers, that science will be able to simulate each person’s individual heart in the next decade. These simulations could be useful in a number of areas, including medical device design, clinical decision making, and personalized treatment planning. Learn more about this powerful technology at Engineered to Cure.
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Clare Scott is a SIMULIA Creative Content Advocacy Specialist at Dassault Systèmes. Prior to her work here, she wrote about the additive manufacturing industry for 3DPrint.com. She earned a Bachelor of Arts from Hiram College and a Master of Arts from University College Dublin. Clare works out of Dassault Systèmes’ Cleveland, Ohio office and enjoys reading, acting in local theatre and spending time outdoors.