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From Classic to Modern Prognostic Biomarkers in Patients with Acute Myocardial Infarction

Journal

Publisher

MDPI
DOI: 10.3390/ijms23169168

Keywords

biomarkers; myocardial infarction; prognosis; cardiac function

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Despite lacking an ideal biomarker for risk assessment in patients with AMI, recent studies have shown promising results. Several novel biomarkers related to the pathophysiological processes in AMI patients have been identified, and their inclusion in multi-biomarker strategies may provide additional prognostic value. Additionally, the use of artificial intelligence algorithms for risk stratification and prognostic assessment has the potential to significantly improve outcomes.
Despite all the important advances in its diagnosis and treatment, acute myocardial infarction (AMI) is still one of the most prominent causes of morbidity and mortality worldwide. Early identification of patients at high risk of poor outcomes through the measurement of various biomarker concentrations might contribute to more accurate risk stratification and help to guide more individualized therapeutic strategies, thus improving prognoses. The aim of this article is to provide an overview of the role and applications of cardiac biomarkers in risk stratification and prognostic assessment for patients with myocardial infarction. Although there is no ideal biomarker that can provide prognostic information for risk assessment in patients with AMI, the results obtained in recent years are promising. Several novel biomarkers related to the pathophysiological processes found in patients with myocardial infarction, such as inflammation, neurohormonal activation, myocardial stress, myocardial necrosis, cardiac remodeling and vasoactive processes, have been identified; they may bring additional value for AMI prognosis when included in multi-biomarker strategies. Furthermore, the use of artificial intelligence algorithms for risk stratification and prognostic assessment in these patients may have an extremely important role in improving outcomes.

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