Application of Bacterial DNA Quantitative Detection in Diagnosis of SBP in Cirrhosis: A Multi Center Diagnostic Experiment

Background and Significance: Spontaneous bacterial peritonitis (SBP) is a common and life-threatening complication in patients with liver cirrhosis, characterized by high incidence and mortality. The current diagnostic standard relies on ascitic fluid polymorphonuclear leukocyte (PMN) count ≥250 cells/μL. However, this threshold is associated with high rates of missed diagnoses, poor correlation with clinical symptoms, and delays in pathogen identification due to the low sensitivity and prolonged time of traditional ascitic fluid culture. With the development of molecular diagnostics, bacterial DNA (bactDNA) detection-especially through droplet digital PCR (ddPCR)-has emerged as a promising technique due to its high sensitivity, absolute quantification capability, and robustness. This study aims to evaluate the diagnostic value and accuracy of ddPCR-based quantification of bacterial DNA in ascitic fluid for SBP, providing a novel and objective diagnostic tool to improve early and accurate detection and to inform targeted antimicrobial therapy. Objectives: To assess the diagnostic accuracy (sensitivity, specificity, predictive values) of ddPCR-based quantification of total bacterial DNA in ascitic fluid for SBP. To evaluate the diagnostic performance of the ratio of Gram-positive to Gram-negative bacterial DNA (G+/G-) in predicting SBP. To explore the potential of ddPCR-based bacterial DNA quantification as a complementary or alternative method to conventional PMN-based diagnostic criteria. Study Design and Methods: This is a prospective diagnostic study involving 700 patients with liver cirrhosis and ascites. Ascitic fluid samples will be analyzed using ddPCR to quantify bacterial DNA levels. These results will be compared with traditional diagnostic criteria, including PMN counts and expert panel assessments based on clinical symptoms and laboratory findings. Statistical analyses will include correlation analysis, ROC curve analysis, and consistency testing