For cardiologists, it can be a vexing choice between diagnostic tests when a patient presents with chest pain and possible coronary artery disease, but a new AI tool may help.
According to a study recently published in the European Heart Journal, researchers led by Yale cardiologist Rohan Khera, MD, MS, have applied machine learning techniques to data from two large clinical trials to develop ASSIST©, a new digital decision-aiding tool that can identify which imaging test to pursue in patients who may have coronary artery disease or CAD, a condition caused by plaque buildup in the arterial wall.
"There are strengths and limitations for each of these diagnostic tests," explained Khera, an assistant professor of cardiology at Yale School of Medicine. "If you are able to establish the diagnosis correctly, you would be more likely to pursue optimal medical and procedural therapy, which may then influence the outcomes of patients.”
Recent clinical trials have attempted to determine if one test is optimal. The PROMISE and SCOT-HEART clinical trials have indicated that anatomical imaging has similar outcomes to stress testing, but may improve long-term outcomes in certain patients.
“When patients present with chest pain you have two major testing strategies. Large clinical trials have been done without a conclusive answer, so we wanted to see if the trial data could be used to better understand whether a given patient would benefit from one testing strategy or the other,” said Khera.
To develop the ASSIST tool, researchers gathered data from 9,572 patients who were enrolled in the PROMISE trial through the National Heart, Lung and Blood Institute. The team then created a novel strategy that embedded local data experiments within the larger clinical trial.
“A unique aspect of our approach is that we leverage both arms of a clinical trial, overcoming the limitation of real-world data, where decisions made by clinicians can introduce bias into algorithms,” said Khera.
Among 2,135 patients who underwent functional-first or anatomical-first testing, researchers saw a two-fold lower risk of adverse cardiac events when there was agreement between the test performed and the one recommended by ASSIST. The group expects that this tool will offer clinicians further insight when they make the choice between anatomical or functional testing in chest pain evaluation.
Functional testing, commonly known as a stress test, examines patients for CAD by detecting reduced blood flow to the heart. The second option, anatomical testing, or coronary computed tomography angiography (CCTA), identifies blockages in the blood vessels. Using machine learning algorithms ASSIST provides a recommendation for each patient.