From Classic to Modern Prognostic Biomarkers in Patients with Acute Myocardial Infarction.
Cristian StătescuLarisa AnghelBogdan-Sorin TudurachiAndreea LeonteLaura-Cătălina BencheaRadu-Andy SascăuPublished in: International journal of molecular sciences (2022)
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.
Keyphrases
- acute myocardial infarction
- left ventricular
- artificial intelligence
- machine learning
- percutaneous coronary intervention
- risk assessment
- heart failure
- end stage renal disease
- deep learning
- big data
- oxidative stress
- chronic kidney disease
- newly diagnosed
- acute coronary syndrome
- peritoneal dialysis
- healthcare
- type diabetes
- heavy metals
- coronary artery disease
- metabolic syndrome
- human health
- social media
- health information
- patient reported outcomes
- atrial fibrillation