NATALIA TRAYANOVAThere is no question that computer modeling is poised to transform medicine, although its effects on health outcomes are yet to be seen.
Over the last decade, computer modeling has helped researchers generate increasingly sophisticated virtual organs. For example, virtual hearts that model complex interactions within the organs—from molecules to cells and tissues and back again—are poised to deliver breakthroughs at the patient bedside.
Building a personalized virtual heart involves constructing a geometric representation of the organ using magnetic resonance imaging (MRI) or computed tomography scans. From there, a computational model of the heart’s inner workings is overlaid on this structure.
How could a patient-specific heart model be used in the clinic? My colleagues and I are testing whether such models can help physicians make better treatment decisions for patients with a life-threatening fast heart rhythm called ventricular tachycardia. Cardiac ablation, a treatment that permanently eliminates the arrhythmia, aims to destroy the responsible heart tissue. But finding the right location is difficult. Patients must undergo a 4- to-12-hour-long interrogation of the electrical functioning of the heart with a probe. But the patient’s virtual heart model could determine the optimal ablation target noninvasively. In retrospective tests, model predictions of optimal ablation targets have proven more accurate than current practices, and prospective studies are now underway. Adoption of this methodology in the clinic could have a dramatic impact, significantly reducing healthcare costs and improving patient well-being.
A second application aims to better identify which patients with infarcted hearts are at risk of developing arrhythmias and would thereby benefit from the implantation of a defibrillator. In defibrillation, an electric shock resets the rhythm of the heart. Presently, that decision is made on the basis of the patient’s ejection fraction—a measure of the portion of blood that flows out of the heart with each pump. If this number is below 35 percent, then a defibrillator is deployed. This criterion, however, is insensitive, and misses many at-risk patients. My colleagues and I are now testing whether patient-specific heart models can be used to predict risk of arrhythmia.
And there are other potential applications for modeling the heart with computers.
Atrial fibrillation is the most common arrhythmia and creates a high risk of stroke. In patients with fibrotic remodeling in their atria (the upper chambers in the heart), ablation has a very low level of success. My colleagues and I are now testing whether computer models of the fibrotic patient atria can predict the optimal ablation targets in these patients.
In patients with normal heart size and anatomy, the defibrillator is implanted in a standard configuration: a battery under the collar bone, plus a transvenous catheter in the heart’s chamber. But in children with malformed or very small hearts, the device must be positioned outside the organ, and the ideal position is difficult to pinpoint. An imperfect setup can cause the defibrillator to discharge unnecessarily or, worse, fail to deliver a shock when needed. My colleagues and I have used a virtual heart-and-torso model that accounts for the unique anatomy of the child’s heart to determine the optimal device positioning. If studies show the model has value in children, it could spare them from repeat procedures needed to reposition the device.
Researchers, medical professionals, patients, and families alike can all be encouraged by the improvements that computer modeling technology will bring to cardiac care, especially for the hearts of the youngest patients.
Natalia Trayanova is the Murray B. Sachs Professor in the department of biomedical engineering and the Institute for Computational Medicine at Johns Hopkins University. Trayanova will further discuss “the computational revolution in medicine, engineering, and science” as a keynote speaker at The Academy of Medicine, Engineering & Science of Texas annual conference this month.