Virtual Heart Modeling Can Help Personalize Cardiac Treatment

Heart rate with ECG graph in the cyberspace

Researchers at Johns Hopkins have developed a method of creating personalized computational models of the heart that can be used to help cardiologists and cardiac surgeons decide on the best treatment options for people with heart problems.

This virtual approach, based on a combination of physiology and physics, can help reveal information about a patient’s heart that might otherwise remain concealed. For example, how it might respond to certain events like whether people will go on to develop an irregular heartbeat, or arrhythmia, after experiencing a heart attack.

The models are created by feeding in a variety of different information such as echocardiography, MRI and electrocardiography test results, plus blood pressure and other factors such as genetic test results, among other things. A virtual heart model is then created based on this information combined with known physiological and physical principles.

While these models are still relatively new, recent studies have shown that they can be used to help improve outcomes for patients. Natalia Trayanova, a professor in biomedical engineering and medicine at Johns Hopkins, specializes in creating these models. Writing in the journal Biophysics Reviews, she outlines with colleagues how they have and can be used to successfully model ventricular arrhythmia and better predict treatment outcomes and risk for future cardiovascular events.

One key way they can be used is to predict which patients are most likely to develop ventricular arrythmias based on their physiology and other characteristics. For example, some people who experience heart attacks (myocardial infarction) go on to develop these arrythmias and some do not, virtual modelling can help predict this more accurately than standard techniques, according to study results.

Another use of these models is to predict how new and experimental treatments might affect the heart and why. “Cell-based cardiac regenerative therapies, a promising treatment to reverse cardiac remodeling in the post-infarct heart, have been found to be arrhythmogenic,” write the authors. “However, why these newly engrafted cells can be arrhythmogenic is poorly understood.”

This has been investigated by several recent virtual studies and they discovered why these cells can cause arrythmias.  “They determined that arrhythmias arising from engraftment were likely to be from re-entrant, not focal, mechanisms, and that the location of the patch engraftment relative to the patient-specific fibrotic distribution was important in determining arrhythmogenicity,” explain the authors.

These kinds of virtual models are also being combined with artificial intelligence technology such as machine learning so the models can improve the more they are used and so patient record data can also be more easily incorporated into the models.

These are just some ways this ‘in silico’ technology is being successfully applied in this area, but the technology is just in its infancy.  “As high-performance computing and machine learning become increasingly sophisticated, newer advancements will likely develop in whole-heart modeling,” conclude the researchers.

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