Data already gathered on ECGs can create a 3-D data model of the heart and provide new biomarkers for predicting heart disease.
These two data visualizations show a 2-D and 3-D view of the heart's electrical activity. The traditional electrocardiogram readout (left) shows activity over several cycles, but does not map magnitude or direction, like in a vectorcardiogram (right).
Doctors have known for decades that early detection of heart conditions helps improve patient outcomes. That’s why researchers are adding another dimension to the data we use to detect heart issues – literally.
Doctors typically use an electrocardiogram, or ECG, to detect heart arrhythmia by monitoring the heart’s electrical activity. Patients are hooked up to a machine through wires and stickers strategically placed on the chest. ECGs (often referred to as EKGs) are life-saving tests, but they only produce a 2-D look at the heart’s function: a line with peaks and valleys representing pulses.
Many ECG machines already collect the data necessary to create vectorcardiograms, or 3-D data models. These models hold potential for expanding the information available to patients and their future heart disease risk. While an ECG can detect a heart problem, a vectorcardiogram holds the potential to explain why – often times without requiring an additional test.
“Our heart and our body are three-dimensional,” says Larisa Tereshchenko, MD, PhD. “The traditional 12-lead ECG uses some angles to look at the heart, but it does not consider the heart as a 3-D structure. We’re seeing that vectorcardiogram data can provide biomarkers for heart disease and provide more information on what’s going on that’s causing it.”
Dr. Tereshchenko is a senior author on a recent study published in Nature Communications which investigated using vectorcardiograms in combination with genomic data to learn more about the heart’s function and develop new biomarkers for heart disease. This study focused on one angle formed in the 3-D model of the heart, called the QRS-T angle, and found genetic markers and new pathways that don’t show up on a traditional ECG reading.
The QRS-T angle reflects fundamental relationships between the heart’s two main electrical phases: activation and relaxation. Vectorcardiograms provide the location of the electrical activity within the 3-D model of the heart. That specificity provides more opportunities for researchers to understand where the problems are occurring.
Dr. Tereshchenko’s lab in Quantitative Health Sciences investigates the heart’s functions that become dysregulated in cardiac arrythmia and develops new tools for screening for and predicting heart disease. This is just one of angles her team is studying as part of a National Institutes of Health grant.
“Knowing how the electrical currents are flowing through certain parts of the heart provides us with direction to investigate those pathways,” Dr. Tereshchenko says. “For example, ventricular conduction disorders are prevalent in 10-20% of patients with heart failure. Vectorcardiograms would allow us to look at the pathways in the ventricle, specifically, to predict these issues – and potentially develop new therapies.”
It’s not enough for the ECG machine to collect the data, it also has to analyze it and produce a reading that clinicians can use. ECG software hasn’t advanced enough, until recently, to do the math for vectorcardiograms, Dr. Tereshchenko says.
Some ECG machines produce QRS-T angle readouts as part of a clinical report, but not all. Currently the machines that provide these readouts are from small businesses or in Europe, Dr. Tereshchenko says.
Studies like these that identify useful biomarkers provide rationale for adding these capabilities to the machines, she added.
“The data is there already, it’s just now an issue of showing how useful it is and why we need to take advantage of it,” she says.
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