Serum levels of S100B protein and neuron-specific enolase are associated with mortality in critically ill patient

Izabela Duda; Łukasz Krzych; Halina Jędrzejowska-Szypułka; Joanna Lewin-Kowalik

doi:10.18388/abp.2017_1619

Izabela Duda Department of Anesthesiology and Intensive Care, School of Medicine, Medical University of Silesia, Poland.
Łukasz Krzych Department of Anesthesiology and Intensive Care, School of Medicine, Medical University of Silesia, Poland.
Halina Jędrzejowska-Szypułka Department of Physiology, School of Medicine, Medical University of Silesia, Poland.
Joanna Lewin-Kowalik

DOI: https://doi.org/10.18388/abp.2017_1619

Keywords: S100B, neuron-specific enolase, mortality, critically ill

Abstract

Introduction. Evaluation of the prognostic potential of protein S100B and neuron-specific enolase (NSE) as predictors of mortality in critically ill patients in intensive care units (ICU).

Materials and Methods. The study was conducted on 62 patients. Basic clinical variables and blood samples for S100B and NSE level testing were obtained during the first four days after admission. Mortality was described as the patient's death during hospitalization in the ICU.

Results. 35% of the patients died. The level of S100B and NSE was significantly higher in non-survivors in comparison with survivors (p=0.007 and p=0.02 respectively). Mortality risk was significantly higher in patients with higher levels of biomarkers than the reference values for S100B (OR 9.00; 95% CI 2.38-33.99; p<0.001) as well as for NSE (OR 5.75; 95%CI 1.31-25,.27; p=0.016). Receiver operating characteristic proved that S100B is a better mortality predictor than NSE (AUC 0.76 for S100B and 0.68 for NSE). From all the other variables, the Apache II score turned out to be the only significant predictor of mortality (AUC 0.88).

Conclusion. There is a significant correlation between mortality in the ICU and increased serum concentration of S100B and NSE. This correlation is stronger for S100B. Testing for serum levels of S100B and NSE may be useful for prediction of treatment outcomes in the ICU patients.

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