Cardiac troponins in patients with suspected or confirmed acute coronary syndrome : new applications for biomarkers in coronary artery disease.

The cardiac troponins are the biochemical markers of choice for the diagnosis of acute myocardial infarction (AMI) and risk prediction in patients with acute coronary syndrome (ACS). In this thesis, the role of early serial cardiac troponin I (cTnI) testing was assessed in fairly unselected patient populations admitted because of chest pain and participating in the FAST II-study (n=197) and the FASTER I-study (n=380). Additionally, the importance of cTnI testing in stable post-ACS patients from the FRISC II-study (n=1092) was studied. The analyses in chest pain patients demonstrate that cTnI is very useful for early diagnostic and prognostic assessment. cTnI allowed already 2 hours after admission the reliable exclusion of AMI and the identification of low-risk patients when ECG findings and a renal marker such as cystatin C were added as conjuncts. Other biomarkers such as CK-MB, myoglobin, NT-pro BNP or CRP did not provide superior clinical information. However, myoglobin may be valuable in combination with cTnI results for the early prediction of an impending major AMI when used as input variable for an artificial neural network. Such an approach applying cTnI results only may also furthermore improve the early diagnosis of AMI. Persistent cTnI elevation > 0.01 μg/L was detectable using a high-sensitive assay in 26% of the stable post-ACS patients from the FRISC II-study. NT-pro BNP levels at 6 months were the most important variable independently associated to persistent cTnI elevation besides male gender, indicating a relationship between adverse left ventricular remodeling processes and cTnI leakage. Patients with persistent cTnI elevation had a considerable risk for both mortality and AMI during 5 year follow-up. These analyses thus, confirm the value of cTnI for early assessment of chest pain patients and provide new and unique evidence regarding the role of cTnI for risk prediction in post-ACS populations.