Hidden Data in EKG Recordings Can Predict Heart Attack Patient Risk

While they’re in the hospital, heart attack patients are hooked up to an electrocardiogram, the familiar beep-beep-beep green-line machine. It detects the heart’s rhythm by measuring changes in the electrical conductance of the skin; remember, heartbeats depend on an electrical signal, like the nervous system. 

The fact that these patients have a constant stream of data about their heartbeat means that there’s way too much data for doctors to analyze by themselves on the fly; there’s a lot of unanalyzed data sitting out there. Researchers finally used computer magic on a huge set of data to separate regular noise from abnormal rhythms, and could use this to better predict patient outcomes, without the need for any new kind of actual measurements.

From ScienceDaily:

Syed and his colleagues developed new ways to parse through the data to find abnormalities — “computational biomarkers” that point to defects in the heart muscle and nervous system that evolve over time. The biomarkers are termed morphologic variability, heart rate motifs and symbolic mismatch.

Morphologic variability is the amount of subtle variability in the shape of apparently normal-looking heartbeats over long periods of time. Heart rate motifs refer to specific sequences of changes in heart rate that reflect whether the heart is responding to nervous system signals as it should. And symbolic mismatch measures how different a patient’s long-term EKG signal is compared with those of other patients with similar clinical histories.

To prove that patients in the study whose EKG signals had these properties were sicker and more likely to die, the researchers used the signals to pick out who was still alive a year after a heart attack, and who was not. They found that those with at least one of the abnormalities were between two and three times more likely to die within 12 months. And by adding all three of the techniques to doctors’ current assessment tools, they could predict 50 percent more deaths with fewer false positives.

“That translates into thousands or tens of thousands of patients for whom doctors could potentially prescribe an effective preventative treatment based on a more individualized assessment of their risk of complications,” Syed said.

This is a pretty great development – getting new and vital information from existing data. It’s definitely the affordable and 21st century way to make progress.

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