New Machine Learning-Based Tool to Help Physicians Determine Best Test for Chest Pain
By applying machine learning techniques to data from 2 large clinical trials, researchers have created a new digital decision-aiding tool, called ASSIST, that can identify which imaging test to pursue in patients who may have coronary artery disease (CAD).
The new tool, described in a study published in the European Heart Journal, focuses on the long-term outcome for a given patient.
The PROMISE and SCOT-HEART clinical trials have suggested that anatomical imaging has similar outcomes to stress testing, but may improve long-term outcomes in certain patients.
“There are strengths and limitations for each of these diagnostic tests,” said Rohan Khera, MD, Yale School of Medicine, New Haven, Connecticut. “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. When patients present with chest pain you have 2 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.”
To create ASSIST, the researchers obtained data from 9,572 patients who were enrolled in the PROMISE trial, and 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 Dr. Khera.
The tool also proved effective in a distinct population of patients in the SCOT-HEART trial. Among 2,135 patients who underwent functional-first or anatomical-first testing, the authors observed a 2-fold lower risk of adverse cardiac events when there was agreement between the test performed and the one recommended by ASSIST.
Dr. Khera said he hopes this tool will provide further insight to clinicians while they make the choice between anatomical or functional testing in chest pain evaluation.
“While we used advanced methods to derive ASSIST, its application is practical for the clinical setting,” said coauthor Evangelos Oikonomou, MD, Yale University. “It relies on routinely captured patient characteristics and can be used by clinicians with a simple online calculator or can be incorporated in the electronic health record.”
SOURCE: Yale University