Should improve inequities in MI diagnosis
Dr. Mills said the CoDE-ACS model will improve current inequities in MI diagnosis, because of which MI is underrecognized in women and younger people.
“Women have troponin concentrations that are half those of men, and although sex-specific troponin thresholds are recommended in the guidelines, they are not widely used. This automatically leads to underrecognition of heart disease in women. But this new machine learning model performs identically in men and women because it has been trained to recognize the different normal levels in men and women,” he explained.
“It will also help us not to underdiagnose MI in younger people who often have a less classical presentation of MI, and they also generally have very low concentrations of troponin, so any increase in troponin way below the current diagnostic threshold may be very relevant to their risk,” he added.
The researchers are planning a randomized trial of the new model to demonstrate the impact it could have on equality of care and overcrowding in the emergency department. In the trial, patients will be randomly assigned to undergo decision-making on the basis of troponin thresholds (current practice) or to undergo decision-making through the CoDE-ACS model.
“The hope is that this trial will show reductions in unnecessary hospital admissions and length of stay in the emergency department,” Dr. Mills said. Results are expected sometime next year.
“This algorithm is very well trained. It has learned on 20,000 patients, so it has a lot more experience than I have, and I have been a professor of cardiology for 20 years,” Dr. Mills said.
He said he believes these models will get even smarter in the future as more data are added.
“I think the future for good decision-making in emergency care will be informed by clinical decision support from well-trained machine learning algorithms and they will help us guide not just the diagnosis of MI but also heart failure and other important cardiac conditions,” he said.
‘Elegant and exciting’ data
Commenting on the study, John W. McEvoy, MB, University of Galway, Ireland, said: “These are elegant and exciting data; however, the inputs into the machine learning algorithm include all the necessary information to actually diagnose MI. So why predict MI, when a human diagnosis can just be made directly? The answer to this question may depend on whether we trust machines more than humans.”
Dr. Mills noted that clinical judgment will always be an important part of MI diagnosis.
“Currently, using the troponin threshold approach, experienced clinicians will be able to nuance the results, but invariably, there is disagreement on this, and this can be a major source of tension within clinical care. By providing more individualized information, this will help enormously in the decision-making process,” he said.
“This model is not about replacing clinical decision-making. It’s more about augmenting decision-making and giving clinicians guidance to be able to improve efficiency and reduce inequality,” he added.
The study was funded with support from the National Institute for Health Research and NHSX, the British Heart Foundation, and Wellcome Leap. Dr. Mills has received honoraria or consultancy from Abbott Diagnostics, Roche Diagnostics, Siemens Healthineers, and LumiraDx. He is employed by the University of Edinburgh, which has filed a patent on the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome score.
A version of this article first appeared on Medscape.com.