The Prognostic Value of Cardiac Biomarkers in Combination with the SOFA Score for the Evaluation of Sepsis-Related Mortality.

Background and Objectives: Sepsis is a life-threatening organ dysfunction, and specific biomarkers could improve prognostic assessment in septic patients. The Sequential Organ Failure Assessment (SOFA) score is the standard tool for clinical sepsis monitoring. Recent studies highlight the need for its revision and the identification of rapid, specific, sensitive predictors of sepsis mortality. The aim of this study was to determine the significance of cardiac biomarkers alone or combined with the SOFA score for evaluating sepsis-related mortality. Materials and Methods: This is a retrospective, single-center study with a relatively small sample size of 73 septic patients (Sepsis-3 criteria) hospitalized in an intensive care unit (ICU) and intermediate care unit (IMCU). All patients had standard laboratory parameters, cardiac biomarkers, and the SOFA score available upon admission. Statistical analyses included non-parametric Mann-Whitney U test, ROC (Receiver Operating Characteristic) curve analysis, Hanley & McNeil method and Hosmer-Lemeshow goodness-of-fit test. Results: Lactate (p < 0.001) and SOFA (p < 0.001) showed the highest area under the curve (AUC) values, and all cardiac biomarkers had statistically significant AUCs (p < 0.05) for sepsis mortality prediction. A comparison of all ROC curves was conducted, but no statistically significant differences were observed. Adding hs-cTn (high-sensitivity cardiac troponin) and lactate to the SOFA score increased its AUC from 0.767 to 0.827 (p = 0.421). Conclusions: The results showed that non-survivors of sepsis had significantly higher levels of cardiac biomarkers compared to survivors. There were no statistically significant differences in the areas under the ROC curves among the three markers, or between the markers and SOFA. The addition of cardiac biomarkers to SOFA did not improve the discriminatory ability of the SOFA score. Further research with a larger sample size is required to validate and generalize the findings.