New AI tool could reduce risks and hospital time for heart attack patients
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In what is being called a major advance for heart attack treatment, researchers from the Ottawa Heart Institute, the Montreal Heart Institute, and the Centre hospitalier de l'Universite de Montreal (CHUM) have successfully used artificial intelligence to assess heart function in real time during emergency procedures.
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Researchers found the artificial intelligence tool — called CathEF — is able to accurately measure how well the heart is pumping in real time after a heart attack. That allows doctors to make 'faster, better-informed decisions, improving patient care in life-or-death situations,' said Dr. Pascal Thériault-Lauzier. He is a clinical scientist at the Ottawa Heart Institute and the lead author of the study, which was published in New England Journal of Medicine Artificial Intelligence this week.
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Using artificial intelligence to assess heart damage after a heart attack can give health care providers necessary information more quickly and prevent many patients from having to undergo more intrusive tests that carry a risk of complications. The AI tool could also allow some patients to leave hospital sooner, said Theriault-Lauzier.
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Currently, patients rushed to hospital experiencing reduced blood flow to the heart undergo a series of procedures to assess and treat the heart. Those include a procedure called a coronary angiography in which dye is injected and doctors use X-rays to look at the heart's blood vessels and assess how well it is pumping. If a blockage is found, a balloon catheter is used to inflate and widen the artery, often followed by the placement of a stent to keep the artery open.
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Doctors assess the patient's so-called ejection fraction, which measures the heart's ability to pump blood out to the body. The information is crucial in order for health providers to decide which course of treatment the patient needs. Additional tests, such as echocardiograms or the more invasive imaging procedure, are currently used to assess ejection fraction in order to guide treatment decisions. Completing those tests takes time and can carry risks.
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Thériault-Lauzier said the use of artificial intelligence could change that by quickly assessing ejection fraction using the original images taken, without the need for further invasive procedures.
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Patients found to be stable could leave hospital sooner and be followed up for further treatment, he said. Those with more damage would continue to be monitored in hospital and start medication quickly.
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The results are promising, but the AI tool is not ready for clinical use. Approvals and commercialization could take a few years, said Thériault-Lauzier.
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