Diagnostic Biomarkers for Risk Estimation of In-Hospital and Post-Discharge Cardiovascular Mortality in ST-Segment Elevation Myocardial Infarction (STEMI) Patients.

Background: ST-segment-elevation myocardial infarction (STEMI) continues to be associated with substantial short- and long-term cardiovascular (CV) mortality despite advances in treatment. Accurate early risk stratification remains critical for optimizing outcomes. Emerging biomarkers including CRP, sST2, and FABP may enhance predictive precision beyond classical markers. This study aimed to evaluate the prognostic value of these biomarkers for in-hospital and 18-month post-discharge CV mortality in STEMI patients. Methods: In this prospective, single-center study, 179 consecutive STEMI patients admitted September 2020-June 2021 underwent biomarker evaluation upon admission. Serum concentrations of CRP, sST2, and H-FABP were measured by ELISA. Patients were followed for in-hospital outcomes and post-discharge mortality during 18-month follow-up (FU) (last patient, last visit January 2023). ROC analysis was used to determine biomarker cut-off values. Cox regression and Kaplan-Meier analyses assessed associations with mortality. Results: In-hospital mortality was 7.8% (14/179). Elevated CRP (>11 mg/L) was significantly associated with higher in-hospital mortality (21.4% vs. 3.7%, p < 0.01). sST2 and H-FABP showed trends toward worse outcomes at higher levels, although their independent predictive value was less robust. Cox regression identified CRP > 11 mg/L (HR = 4.93, p < 0.01), admission glucose, and reduced GFR as independent predictors of in-hospital mortality. During FU, 18 of 165 discharged patients (10.1%) experienced CV death. Higher sST2 levels were significantly associated with post-discharge mortality in midterm FU (p = 0.041). Conclusions: We could show that CRP > 11 mg/L is a strong predictor of in-hospital mortality while elevated sST2 is associated with CV mortality during midterm FU in STEMI patients. Incorporating these biomarkers into clinical risk models may enhance early risk prediction and identify patients at higher risk for post-discharge events.