Inflammatory plasma proteins predict short-term mortality in patients with an acute myocardial infarction

The aim of this study was to investigate the association between inflammatory markers and 28-day mortality in patients with ST-elevation myocardial infarction (STEMI).

In 398 STEMI patients recorded between 2009 and 2013 by the population-based Myocardial Infarction Registry Augsburg, 92 protein biomarkers were measured in admission arterial blood samples using the OLINK inflammatory panel. In multivariable-adjusted logistic regression models, the association between each marker and 28-day mortality was investigated. The values of the biomarkers most significantly associated with mortality were standardized and summarized to obtain a prediction score for 28-day mortality. The predictive ability of this biomarker score was compared to the established GRACE score using ROC analysis. Finally, a combined total score was generated by adding the standardized biomarker score to the standardized GRACE score.

The markers IL-6, IL-8, IL-10, FGF-21, FGF-23, ST1A1, MCP-1, 4E-BP1, and CST5 were most significantly associated with 28-day mortality, each with FDR-adjusted (false discovery rate adjusted) p-values of < 0.01 in the multivariable logistic regression model. In a ROC analysis, the biomarker score and the GRACE score showed comparable predictive ability for 28-day mortality (biomarker score AUC: 0.7859 [CI: 0.6735-0.89], GRACE score AUC: 0.7961 [CI: 0.6965-0.8802]). By combining the biomarker score and the Grace score, the predictive ability improved with an AUC of 0.8305 [CI: 0.7269-0.9187]. A continuous Net Reclassification Improvement (cNRI) of 0.566 (CI: 0.192-0.94, p-value: 0.003) and an Integrated Discrimination Improvement (IDI) of 0.083 ((CI: 0.016-0.149, p-value: 0.015) confirmed the superiority of the combined score over the GARCE score.

Inflammatory biomarkers may play a significant role in the pathophysiology of acute myocardial infarction (AMI) and AMI-related mortality and might be a promising starting point for personalized medicine, which aims to provide each patient with tailored therapy.