(RxWiki News) Identifying heart attack patients at risk can be tricky. Subtle EKG recordings may be able to help doctors pinpoint which heart attack patients are at the highest risk of dying soon.
The discovery could help save thousands of patients by ensuring they receive the treatment needed to survive. The American Heart Association has indicated that in some age groups, more than 25 percent of patients who survive the initial heart attack die within a year.
"Go to a hospital immediately with symptoms of a heart attack."
Zeeshan Syed, an assistant professor in the University of Michigan department of electrical engineering and computer science and first author of the study, said that today's methods for determining which heart attack patients need the most aggressive treatment can identify groups of patients at high risk of complications, but often miss most of the deaths -- up to 70 percent of them.
Following a heart attack, patients can be vulnerable to sudden death from irregular heart rhythms. This could be prevented with medication or implantable defibrillators if it is determined which patients could most benefit from the interventions before it's too late.
Researchers reviewed 24-hour continuous electrocardiograms from 4,557 heart attack patients who were enrolled in a large clinical trial led by the Brigham and Women's Hospital and Harvard Medical School. They discovered that EKG signals from many patients who later died of cardiovascular-related causes contained similar errant patterns that were previously dismissed as noise or undetectable.
Syed said information was buried in the noise and almost invisible because of the sheer volume of data. Investigators were able to separate the noise from abnormal behavior about the heart through complicated computational techniques where the markers were picked up.
In order to validate that patients in the study with EKG signals with certain properties were more likely to die, researchers used the signals to pick out who would be alive a year after a heart attack and who would not.
They did this by identifying three computational biomarkers that could indicate a defect in the heart muscle or nervous system
The biomarkers included morphologic variability, the amount of subtle variability in the shape of apparently-normal looking heartbeats over a long time frame, heart rate motifs, which refer to specific sequence changes in heart rate that can show whether the heart is responding to nervous system signals appropriately and symbolic mismatch, which is capable of measuring how different a patient's long-term EKG signal is compared to other patients with similar clinical histories.
They found that those with at least one of three abnormalities were two to three times more likely to die within 12 months.
Doctors often are so overwhelmed with data that they may be unable to interpret all of it, such as 72 hours worth of EKGs. The finding would allow doctors to use data that is routinely collected without additional costs to ensure that high risk patients receive the appropriate treatment.
The study is published in the Sept. 28 edition of Science Translational Medicine.