Rapid and sensitive identification of pleural and peritoneal infections by droplet digital PCR

Pleural and peritoneal infections cause substantial morbidity and mortality. Traditional diagnostic methods rely on the cultivation of clinical samples, which usually takes days to obtain report and holds a low detection sensitivity. In this study, we evaluated a 5-fluorescent-channel droplet digital PCR (ddPCR) system and 5 assay panels for culture-independent rapid pathogen detections directly from pleural and peritoneal fluid samples. Traditional culture of the same sample was used as reference. A total of 40 pleural fluid samples and 19 peritoneal fluid samples were tested in this study. Twenty-five positives including 4 polymicrobial infections by culture and 26 positives including 11 polymicrobial infections by ddPCR were detected for pleural fluid samples; 14 positives including 2 polymicrobial infections by culture and 15 positives including 3 polymicrobial infections by ddPCR were detected for peritoneal fluid samples. Klebsiella pneumoniae was the most common bacterium detected both in pleural and in peritoneal fluid samples. The sensitivity of the ddPCR assay for pleural and peritoneal fluid samples was 96% (95% confidence interval (CI) = 79.65 to 99.90%) and 92.86% (95% CI = 66.13 to 99.82%), respectively. The turnaround time of the ddPCR assay was approximately 3 h comparing with 38.30 +/- 22.44 h for culture-based identifications. Our results demonstrated that the ddPCR assay is a rapid and sensitive method for identifying pathogens responsible for pleural and peritoneal infections and would be a promising approach for early diagnosis and optimizing treatment of infections.

Automated summary

This study evaluated a rapid and sensitive method for identifying pathogens responsible for pleural and peritoneal infections through direct detection from fluid samples without relying on cultivation. The results demonstrated that the ddPCR assay is efficient in detecting pathogens in pleural and peritoneal fluid samples, offering a promising approach for early diagnosis and treatment optimization.