Aim: Early risk classification after acute myocardial infarction is very important for the prediction of complications. In this study, it was aimed to find the most sensitive panel in the prediction of prognosis after acute myocardial infarction by multi-biomarker approach.

Materials and Methods: 120 patients who were diagnosed with the acute coronary syndrome were included. Patients’ heart failure and death outcomes that accrued after acute myocardial infarction were followed up. Heart failure and all causes of death were noted and the predictive values of the markers and new panels for these poor outcomes were examined.

Results: In the prediction of heart failure after acute myocardial infarction, glucose’s area under the curve (AUC), was 0.714, hs-cTnT’s 0.779, and NT-proBNP’s 0.842 (p=0.035, p=0.002, p<0.001 respectively). In the panel created using hs-cTNT, NT-proBNP, copeptin, myoglobin, and glucose, the AUC reached 0.917. In mortality prediction, copeptin’s AUC was 0.696, myoglobin’s 0.713, and glucose’s 0.800 (p=0.045, p=0.003, p=<0.001 respectively). In the panel created using hs-cTNT, copeptin, myoglobin, and glucose, the AUC reached 0.865 in prediction of mortality.

Conclusion: Biomarker combinations may increase the predictive values of biomarkers. It is demonstrated that the powerful prognostic panels were created using serum hs-cTNT, NT-proBNP, copeptin, myoglobin, and glucose levels in this study.