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Decoding the Heart

Tuesday, May 1, 2012 2:06 p.m.

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Zeeshan Syed, assistant professor in the University of Michigan Department of Electrical Engineering and Computer Science, has led the discovery of subtle but potentially life-saving signals hidden in heart attack patients' EKG histories. The findings could save thousands of lives every year.

Zeeshan Syed, assistant professor in the University of Michigan Department of Electrical Engineering and Computer Science, has led the discovery of subtle but potentially life-saving signals hidden in heart attack patients' EKG histories. The findings could save thousands of lives every year.

Fatal heart attacks don’t always kill swiftly.

Weeks or months after a patient has gone home from the hospital, heart damage that seemed minor can prove lethal. In certain age groups, a full quarter of those who survive the actual episode end up dying of sudden cardiac arrest within the year, according to the American Heart Association. Compounding this tragedy, there are treatments that could have saved many of these lives. But doctors don’t always know who needs what in time.

Assistant professor Zeeshan Syed stands outside his office in the Computer Science and Engineering Building at the University of Michigan.“Each one of these deaths is a failure,” said Zeeshan Syed, an assistant professor in the Department of Electrical Engineering and Computer Science. “There must surely be a way to predict and prevent them.” That’s the charge Syed gave himself as a graduate student at MIT and Harvard Medical School, inspired to go back to school after his own father had a heart attack. Now he’s a computer scientist studying one of the body’s own electrical systems, and he is zeroing in on some answers.

Syed and his colleagues have developed new non-invasive and inexpensive tests that could one day help match tens of thousands of cardiac patients with life-saving treatment each year. The input is a day’s worth of EKG history that’s already routinely collected when patients are hospitalized for heart attacks. What comes out, after Syed’s new algorithms work their magic, are new numbers that reveal how much lurking damage the patient’s heart has suffered. It’s damage that other tests often miss. Damage that’s hidden in plain sight. The EKG, also called the ECG, is one of the oldest tools in cardiology. Measuring and displaying the electrical activity of the heart, it keeps a beeping vigil by a patient’s hospital bedside. While doctors and nurses do watch it, it’s hard for them determine the most important predictors of a patient’s future health from long recordings. Doctors typically look at its data in seconds-long snapshots. With human eyes, that’s pretty much all anyone can expect.

“As long as we’re in the mode of people looking at the data, it has to be a small amount of data,” said John Guttag, a professor in the MIT Department of Electrical Engineering and Computer Science who was Syed’s PhD advisor. “You can’t ask a physician to look at 72 hours’ worth of ECG data, so people have focused on the things you can learn by looking at tiny pieces of it.” Those snippets aren’t telling the whole story. To really assess heart health following an attack, you need as much as a whole day of EKG history. That’s where Syed and his colleagues come in.

Their techniques can quickly comb through 24 hours of EKG information from thousands of patients at once. They can identify long-term patterns and subtle rhythm irregularities that give away an unstable heart. And by comparing patients’ results with others with similar clinical histories, they can see more red flags.

Patients with at least one of these abnormalities are two to three times more likely to die in the year after a heart attack, the researchers have found. They’re as much as eight times more likely to die in the first three months. By adding all three of their new metrics to doctors’ current assessment tools, Syed and his colleagues identified 50 percent more heart attack victims who died within a year in the clinical trials whose data they used.

That’s a big improvement, especially when you consider the track record of the status quo.

Today, doctors prescribe the most aggressive post-heart attack treatments based on a mixture of factors: the patient’s overall health and medical history, the results of several blood tests, and an echocardiogram. The echocardiogram uses ultrasound to image the heart and measure how much blood it pumps through with each beat.

These methods do find some patients at high risk of complications. But they actually miss most of the deaths — up to 70 percent of them.

“Unfortunately, it’s a rather blunt metric and we tend to miss a fair number of the patients with the sickest hearts,” said Benjamin Scirica, a cardiologist at Brigham and Women’s Hospital who was also involved in the study. If doctors could more easily pick out which patients are high-risk, they could prescribe closer monitoring, medication or implantable defibrillators to more of the right people.

Implantable defibrillators can shock the heart back into rhythm. They’re expensive and invasive, but lifesaving in the right hearts. Right now, Scirica said, a majority of patients who have the devices never end up utilizing them. And a majority of people who die of sudden cardiac arrest were not shown to be candidates for a defibrillator using current tests. With these researchers’ work, though, tomorrow’s tests might do better. They could reveal grave secrets in the EKG, written in invisible ink.

“I think there’s almost a magical quality to it,” Syed said. “It really is sophisticated computational techniques applied to unsophisticated data. We are picking out these things that are routinely collected in a clinical setting and trying to extract something fundamentally new.

“It’s almost like discovering a treasure right beneath your nose.”

Article topics: Bioinformatics , Health


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