HEART attacks are one of the leading causes of death globally. Despite recent advances in the diagnosis and treatment of myocardial infarction, female patients experience worse clinical outcomes compared with their male counterparts. This has been a cause for concern for cardiologists and healthcare practitioners. Therefore, a team led by Thomas F. Lüscher from the Center for Molecular Cardiology at the University of Zurich, Switzerland, investigated the effect of biological sex in myocardial infarction. The researchers evaluated the sex-specific performance of the Global Registry of Acute Coronary Events (GRACE) 2.0 score in non-ST-segment elevation acute coronary syndrome and also developed a machine learning-based GRACE 3.0 score to account for sex differences in the disease phenotype.
This study analysed data from 420,781 consecutive patients with non-ST-segment elevation acute coronary syndrome. First author Florian A. Wenzl commented: “The study shows that established models which guide current patient management are less accurate in females,” favouring their incorrect stratification into the low-to-intermediate risk group, for which early invasive treatment is not indicated. However, by using a machine learning algorithm and the largest datasets in Europe, Wenzl and colleagues were able to develop an artificial intelligence-based risk sore, “which accounts for sex-related differences in the baseline risk profile and improves the prediction of mortality in both sexes.” Ultimately, this led to a clinically relevant reclassification of female patients to the high-risk group.
Discussing the wider relevance of the research results, Wenzl said: “Our study heralds the era of artificial intelligence in the treatment of heart attacks.” This is important because artificial intelligence and big data analytics are considered essential elements of personalised patient care and individualised treatments. Lüscher also emphasised the significance of the findings: “I hope the implementation of this novel score in treatment algorithms will refine current treatment strategies, reduce sex inequalities, and eventually improve the survival of patients with heart attacks.”
In summary, use of the recently developed GRACE 3.0 score has the potential to reduce sex inequalities in risk stratification. Overall, increased awareness of sex differences in disease characteristics as well as the patient risk profile is a critical step to improving clinical outcomes in people with non-ST-segment elevation acute coronary syndrome.
