The Prognostic Value of Biomarkers in Patients Diagnosed with Spontaneous Bacterial Peritonitis

Background: Spontaneous bacterial peritonitis (SBP) is a life-threatening complication of cirrhosis associated with high morbidity and mortality. Early risk stratification remains challenging due to the invasive nature of ascitic polymorphonuclear cell counting and the limited sensitivity of conventional markers. Novel composite biomarkers such as the blood urea nitrogen/albumin ratio (BAR), red cell distribution width/albumin ratio (RAR), and neutrophil-to-lymphocyte ratio (NLR) may offer practical prognostic value. This study aimed to evaluate the predictive power of these biomarkers for in-hospital mortality in patients diagnosed with SBP in the emergency department. Methods: This retrospective, observational, cohort study included adult patients diagnosed with SBP between March 2022 and October 2024. SBP diagnosis was confirmed by ascitic fluid leukocyte count > 250/mm³. Demographic data, laboratory parameters, and clinical outcomes were obtained from hospital records. RAR and BAR ratios were calculated at admission. Outcomes were analyzed according to ward/intensive care unit (ICU) admission and in-hospital mortality. Receiver operating characteristic (ROC) analysis and logistic regression were performed to assess prognostic performance. Results: A total of 112 patients were included (mean age 63.44 ± 13.16 years, 56.3% male). In-hospital mortality was 43.8%. Both BAR and RAR ratios were significantly higher in non-survivors (p = 0.016 and p = 0.001, respectively). BAR showed the highest prognostic performance (Area under the curve 0.761), with an optimal cutoff > 1.86 (sensitivity 64.44%, specificity 79.66%). RAR had an area under the curve (AUC) of 0.638, and NLR had an AUC of 0.658. Conclusions: Among the evaluated biomarkers, BAR emerged as the strongest predictor of in-hospital mortality in SBP, outperforming both RAR and NLR. Although albumin alone was not predictive, its use in composite ratios improved prognostic accuracy. These easily accessible biomarkers may support early risk stratification in emergency settings.