Antithrombotic treatment for chronic coronary syndrome: Evidence and future perspectives.

Background The clinical presentation of coronary artery disease can range from asymptomatic, through stable disease in the form of chronic coronary syndrome, to acute coronary syndrome. Chronic coronary syndrome is a frequent condition, and secondary prevention of ischaemic events is essential. Summary Antithrombotic therapy is a key component of secondary prevention strategies, and it may vary in type and intensity depending on patient characteristics, comorbidities, and revascularisation modalities. Dual antiplatelet therapy is the default strategy in patients with chronic coronary syndrome and recent coronary stent implantation, while antiplatelet monotherapy is commonly prescribed for long-term prevention of cardiovascular events. Oral anticoagulation, in combination with antiplatelet therapy or alone, is used in patients with e.g., concomitant atrial fibrillation or venous thromboembolism. Key messages This review provides an overview of antithrombotic treatment strategies in patients with chronic coronary syndrome. Key messages from current guidelines are conveyed, and we provide future perspectives on long-term antithrombotic strategies.

Statistical and machine learning methods for analysis of multiplex protein data from a novel proximity extension assay in patients with ST-elevation myocardial infarction.

Using data from patients with ST-elevation myocardial infarction (STEMI), we explored how machine learning methods can be used for analysing multiplex protein data obtained from proximity extension assays. Blood samples were obtained from 48 STEMI-patients at admission and after three months. A subset of patients also had blood samples obtained at four and 12 h after admission. Multiplex protein data were obtained using a proximity extension assay. A random forest model was used to assess the predictive power and importance of biomarkers to distinguish between the acute and the stable phase. The similarity of response profiles was investigated using K-means clustering. Out of 92 proteins, 26 proteins were found to significantly distinguish the acute and the stable phase following STEMI. The five proteins tissue factor pathway inhibitor, azurocidin, spondin-1, myeloperoxidase and myoglobin were found to be highly important for differentiating between the acute and the stable phase. Four of these proteins shared response profiles over the four time-points. Machine learning methods can be used to identify and assess novel predictive biomarkers as showcased in the present study population of patients with STEMI.

Detection of biomarkers using a novel proximity extension assay in patients with ST-elevation myocardial infarction.

BACKGROUND: ST-elevation myocardial infarction (STEMI) involves inflammation, activation of platelets, coagulation and changes in fibrinolysis as well as remodelling of myocardial tissue after STEMI. Recently, new proximity extension assays including panels of biomarkers for cardiovascular disease have been developed. This study investigates a wide range of cardiovascular protein biomarkers in the acute phase of STEMI compared with the stable phase three months after STEMI. We hypothesized that major changes in inflammation, haemostasis, tissue remodelling, and proteolysis are prevalent. MATERIALS AND METHODS: This was a prospective, observational study including 48 STEMI patients (mean age 60 ±â€¯12 years, 79% men) treated with primary percutaneous coronary intervention (PPCI). Blood samples were obtained immediately prior to PPCI and again three months later. Levels of 92 biomarkers reflecting inflammation, immune response, cell adhesion, haemostasis, fibrinolysis, tissue remodelling, and proteolysis were assessed using a proximity extension assay (Olink® CARDIOVASCULAR III). RESULTS: When comparing the acute phase of STEMI with the stable phase three months later, a total of 29 biomarkers differed significantly after Bonferroni correction (p < 0.0005). In the acute phase of STEMI, we found an overall increase of biomarkers reflecting immune and inflammatory response, cell adhesion, and haemostasis. Biomarkers reflecting tissue remodelling and proteolysis were increased at three months follow-up compared with the acute phase. Out of the 29 biomarkers, six biomarkers did not confirm our predefined hypotheses. CONCLUSIONS: Using a novel proximity extension assay technique, we detected changes in several biomarkers when comparing the acute phase with three months follow-up in patients with STEMI. These biomarkers may play important roles in the pathogenesis of STEMI.