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Next-generation microbiological testing in intraabdominal infections with PCR technology
Julian Horn1, Philipp Höhn2, Johanna Strotmann2
1Department of General and Visceral Surgery, St. Josef-Hospital Bochum, Ruhr University Bochum, Gudrunstraße 56, 44791, Bochum, Germany. julian.horn@klinikum-bochum.de.
Purpose:
Intraabdominal infections (IAI) are increasing worldwide and are a major contributor to morbidity and mortality. Among IAI, the number of multi-drug resistant organisms (MDRO) is increasing globally. We tested the Unyvero A50® for intraabdominal infections, compared the detected microorganisms and antibiotic resistance, and compared the results with those of routine microbiology.
Methods:
We prospectively compared samples obtained from surgical patients using PCR-based Unyvero IAI cartridges against routine microbiology for the detection of microorganisms. Additionally, we identified clinical parameters that correlated with the microbiological findings. Data were analyzed using the t-test and Mann-Whitney U test.
Results:
Sixty-two samples were analyzed. The PCR system identified more microorganisms, mostly Bacteroides species, Escherichia coli, and Enterococcus spp. For bacterial resistance, the PCR system results were fully concordant with those of routine microbiology, resulting in a sensitivity, specificity, and positive and negative predictive value (PPV, NPV) of 100%. The sensitivity, specificity, PPV, and NPV for the detection of microorganisms were 74%, 58%, 60%, and 72%, respectively. CRP levels were significantly higher in patients with detectable microorganisms. We identified more microorganisms and bacterial resistance in hospital-acquired intra-abdominal infections by using the PCR system.
Discussion:
IAI warrants early identification of the microorganisms involved and their resistance to allow for adequate antibiotic therapy. PCR systems enable physicians to rapidly adjust their antibiotic treatment. Conventional microbiological culture and testing remain essential for determining the minimal growth inhibition concentrations for antibiotic therapy.
Insights
The Unyvero A50® PCR system detected more microorganisms in intra-abdominal infections (IAI) compared to routine methods. It showed 100% accuracy for antibiotic resistance, aiding faster treatment adjustments for multi-drug resistant organisms (MDRO).
Area of Science:
- Clinical Microbiology
- Infectious Diseases
- Molecular Diagnostics
Background:
- Intra-abdominal infections (IAI) are a growing global health concern, often caused by multi-drug resistant organisms (MDRO).
- Timely identification of pathogens and their antibiotic resistance is crucial for effective treatment of IAI.
Purpose of the Study:
- To evaluate the Unyvero A50® system for detecting microorganisms and antibiotic resistance in intra-abdominal infections.
- To compare the performance of the Unyvero A50® with conventional microbiology techniques.
- To identify clinical parameters associated with microbiological findings in IAI patients.
Main Methods:
- Prospective comparison of PCR-based Unyvero IAI cartridges with routine microbiology for 62 surgical patient samples.
- Analysis of clinical parameters, including C-reactive protein (CRP) levels, correlated with microbiological results.
- Statistical analysis using t-test and Mann-Whitney U test.
Main Results:
- The Unyvero A50® identified a greater number of microorganisms, including Bacteroides, Escherichia coli, and Enterococcus species.
- 100% concordance was observed between the PCR system and routine methods for detecting bacterial resistance (100% sensitivity, specificity, PPV, NPV).
- Higher CRP levels were associated with detectable microorganisms; more pathogens and resistance were identified in hospital-acquired IAI using the PCR system.
Conclusions:
- The Unyvero A50® system offers rapid detection of microorganisms and antibiotic resistance in intra-abdominal infections.
- This rapid identification facilitates timely adjustment of antibiotic therapy.
- Conventional microbiological culture remains essential for determining minimal inhibitory concentrations for optimal antibiotic selection.
