A new study published in The Lancet Digital Health has found that artificial intelligence (AI) can more accurately assess risk in patients with the most common type of heart attack than current clinical methods. Led by researchers at the University of Zurich, the international study analyzed data from over 600,000 patients and suggests that many patients may need to be re-classified, which could significantly impact global heart attack treatment strategies.
The study focused on patients suffering from non-ST-elevation acute coronary syndrome (NSTE-ACS), a prevalent form of heart attack. Currently, doctors use the GRACE score to estimate risk and decide on the timing of invasive treatments such as angiography and stenting. Although widely adopted in clinical guidelines, this method doesn’t always capture the full complexity of patient risk profiles.
To address this gap, the researchers used AI to analyze data from the VERDICT trial, a major clinical trial in cardiology and taught the model to identify which patients genuinely benefit from early invasive treatment. The model, called GRACE 3.0, leverages machine learning to enhance decision-making and patient care.
“The results were striking. While some patients gained substantial benefit from early intervention, others showed little or no benefit,” said first author Florian A. Wenzl from UZH’s Center for Molecular Cardiology and the UK’s National Health Service. This finding challenges the current one-size-fits-all approach and suggests that some patients may be receiving unnecessary or suboptimal treatment.
“By re-analyzing clinical trial data, our model GRACE 3.0 learned who actually benefits from early invasive treatment – and who does not. This may imply a shift in how we should be managing these patients,” Wenzl added.
The researchers hope GRACE 3.0 will empower clinicians with a simple, validated, and AI-powered tool to improve outcomes for heart attack patients worldwide.
Reference: Extension of the GRACE score for non-ST-elevation acute coronary syndrome: a development and validation study in ten countries, Wenzl, Florian A et al. The Lancet Digital Health, Volume 0, Issue 0, 100907
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