Saving heart attack victims with computer science

Another story from the boffins @ MIT

Newly discovered subtle markers of heart damage hidden in plain sight among hours of EKG recordings could help doctors identify which heart attack patients are at high risk of dying soon.

That's according to a new study involving researchers from the University of Michigan, MIT, Harvard Medical School, and Brigham and Women's Hospital in Boston. It is published in the Sept. 28 edition of Science Translational Medicine.

The findings could help match tens of thousands of cardiac patients with life-saving treatment in time. Approximately 1 million Americans have a heart attack each year. In certain age groups, more than a quarter of those who survive the initial attack end up dying of complications within a year, according to the American Heart Association.

"Today's methods for determining which heart attack victims need the most aggressive treatments can identify some groups of patients at a high risk of complications. But they miss most of the deaths---up to 70 percent of them," said Zeeshan Syed, an assistant professor in the U-M Department of Electrical Engineering and Computer Science and first author of the study.

Using data mining and machine learning techniques, the researchers sifted through 24-hour continuous electrocardiograms (EKGs or ECGs) from 4,557 heart attack patients enrolled in a large clinical trial led by the Brigham and Women's Hospital/Harvard Medical School TIMI Study Group, one of the world's leading cardiovascular research organizations. The electrocardiogram measures and displays the electrical activity of the heart.