Predicting when a patient is at risk for developing a blood clot can be challenging for physicians, but new research from Johns Hopkins University and Ohio State University delivers a method for precisely identifying those risk factors.

The study, published in the International Journal of Cardiology, presented findings based on simulations performed using the Stampede supercomputer at the Texas Advanced Computing Center in Austin, Texas, and data from patients who did and did not experience post-heart attack blood clots.

Study results showed that a critical factor in predicting risk factors for clots is the degree to which the mitral jet, a blood shot through the mitral valve, penetrates the left ventricle. Without the blood travelling deep enough, the heart may not properly flush out blood from the chamber, resulting in clots, stroke and other dangerous heart events.

"The beauty of the index is that it doesn't require any additional measurements,” said Rajat Mittal, one of the lead authors on the study and a computational fluid dynamics expert and professor of engineering at Johns Hopkins University, in a statement. “It simply reformulates echocardiogram data into a new metric. The clinician doesn't have to do any additional work."

Mittal and his team examined detailed measurements from 13 patients and used them to construct high-fidelity, patient-specific models of the heart. The models included fluid flow, physical structures and bio-chemistry.

"Because we understood the fluid dynamics in the heart using our computational models, we reached the conclusion that the ejection fraction is not a very accurate measure of flow stasis in the left ventricle," Mittal said. "We showed very clearly that the ejection fraction is not able to differentiate a large fraction of these patient and stratify risk, whereas this e-wave propagation index can very accurately stratify who will get a clot and who will not.”