Zeliha Günnur DİKMEN, Eda KARAİSMAİLOĞLU, Ahmet Ergun KARAAĞAOĞLU, Filiz AKBIYIK

A statistical approach to evaluate the performance of cardiac biomarkers in predicting death due to acute myocardial infarction: time-dependent ROC curve

Background/aim: Myoglobin, cardiac troponin T, B-type natriuretic peptide (BNP), and creatine kinase isoenzyme MB (CK-MB)are frequently used biomarkers for evaluating risk of patients admitted to an emergency department with chest pain. Recently, timedependent receiver operating characteristic (ROC) analysis has been used to evaluate the predictive power of biomarkers where diseasestatus can change over time. We aimed to determine the best set of biomarkers that estimate cardiac death during follow-up time. Wealso obtained optimal cut-off values of these biomarkers, which differentiates between patients with and without risk of death. A webtool was developed to estimate time intervals in risk.Materials and methods: A total of 410 patients admitted to the emergency department with chest pain and shortness of breath wereincluded. Cox regression analysis was used to determine an optimal set of biomarkers that can be used for estimating cardiac deathand to combine the significant biomarkers. Time-dependent ROC analysis was performed for evaluating performances of significantbiomarkers and a combined biomarker during 240 h. The bootstrap method was used to compare statistical significance and the Youdenindex was used to determine optimal cut-off values.Results: Myoglobin and BNP were significant by multivariate Cox regression analysis. Areas under the time-dependent ROC curves ofmyoglobin and BNP were about 0.80 during 240 h, and that of the combined biomarker (myoglobin + BNP) increased to 0.90 duringthe first 180 h.Conclusion: Although myoglobin is not clinically specific to a cardiac event, in our study both myoglobin and BNP were found to bestatistically significant for estimating cardiac death. Using this combined biomarker may increase the power of prediction. Our web toolcan be useful for evaluating the risk status of new patients and helping clinicians in making decisions.

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