Technology helps detect blood flow patterns correlated with a heart attack precursor
Coronary microvascular disease (CMD) is a complication of diabetes and metabolic syndrome, and studies suggest the disease may be a precursor to a heart attack — a common cause of death among people with either condition.
Because of CMD’s potential to provide an early warning that a person has or is developing heart disease, Nationwide Children’s researchers are working to develop a non-invasive method to identify CMD earlier. Currently, the disease is either diagnosed invasively or through a less precise process of elimination.
Aaron J. Trask, PhD, a principal investigator in the Center for Cardiac Research, and members of his lab are focusing on transthoracic Doppler echocardiography (TTDE) to assess changes in blood flow patterns as a means to detect CMD. Their research suggests that flow patterns correlate with coronary microvascular data.
But analysis of TTDE is prone to user bias and is time-consuming. To address the issues, Trask’s lab has developed an algorithm with off-the-shelf mathematical computing software.
When the algorithm was applied to TTDE images of healthy mouse models and mouse models of type 2 diabetes, it reduced variability individually and between users and reduced analysis time 30-fold. The research is published in Scientific Reports.
“The study is the first step in a broader investigation looking into coronary flow patterns and if they may be useful in diagnosing coronary microvascular disease,” Dr. Trask says.
“Coronary microvascular disease is a subtle disease. If we develop a reliable computer program, a physician can utilize it to assess a patient’s coronary flow pattern and other measurements obtained during an echocardiogram to diagnose CMD with some degree of accuracy,” says Dr. Trask, who is also an assistant professor of pediatrics at The Ohio State University College of Medicine. “A patient could begin treatments to prevent worsening of heart disease before the disease potentially progresses towards the heart attack stage.”
To begin their research, Dr. Trask and his lab members used TTDE to study coronary flow patterns and noticed patterns differ between healthy mice and mouse models of diabetes. Ian Sunyecz, then an undergraduate researcher and now a medical student, found patterns, including strength of flow, correlate with changes in the coronary microvasculature.
To enhance the lab’s ability to use and learn from TTDE, Sunyecz led development of an algorithm using MATLAB software. The researchers found the algorithm significantly reduced inconsistencies by and among TTDE users and the time it took to analyze clinically relevant details found in the TTDE images.
Research Assistant Kishan Patel did preliminary work refining aspects of the algorithm with machine learning to try to increase its power to predict whether CMD is present. The lab is also collaborating with Christopher Bartlett, PhD, and William Ray, PhD, principal investigators in the Battelle Center for Mathematical Medicine, on the broader study of machine learning in predicting coronary microvascular disease from coronary flow patterns. Funded by a recent NIH/NIBIB R21 Trailblazer grant led by Dr. Trask, the labs are also looking at cardiac function and other non-invasive parameters that, in combination with flow patterns, may increase the ability to predict and diagnose CMD.
The technology is currently aimed at predicting early heart disease, Dr. Trask says. But it could potentially be applied to other diseases, such as Kawasaki disease, that alter coronary flow; or to monitor transplant patients who are suspected of having coronary flow abnormalities.
Citation: Sunyecz IL, McCallinhart PE, Patel KU, McDermott MR, Trask AJ. Defining Coronary Flow Patterns: Comprehensive Automation of Transthoracic Doppler Coronary Blood Flow. Scientific Reports. 2018 Nov 22;8(1):17268.