Utilization of MALDI-TOF MS in the etiological diagnosis of deep-seated anaerobic bacterial infections.

Purpose: Deep-seated abscesses can be caused by a wide array of bacteria in various anatomical sites, the precise identification of which is crucial for implementing organism-specific treatments which can reduce morbidity and mortality. MALDI-TOF MS is a powerful proteomic method for the swift and accurate identification of anaerobic organisms. The aim of this study was to investigate deep-seated infections by MALDI-TOF MS (in comparison to VITEK®2 ANC ID card and phenotypic biochemical tests) and to determine the susceptibility pattern of identified microorganisms.

Materials And Methods: A total of 104 samples from patients suspected of deep-seated infections were aseptically collected and subjected to microscopy, aerobic/anaerobic cultures and subsequent identification via MALDI-TOF MS followed by antimicrobial susceptibility testing. Anaerobic bacteria were also identified using the VITEK-2 system and phenotypic biochemical tests.

Results: Out of the 104 samples tested, 41.3 % (43/104) showed positive results, predominantly in pus specimens (88 %). Mixed infections were found in 21 % of the positive cases. Of the 52 organisms identified from positive specimens, 19.2 % (10/52) were obligate anaerobes, with Bacteroides fragilis group being the most prevalent, followed by both Clostridium perfringens and Clostridium sporogenes respectively. Escherichia coli was observed to be the most common facultative anaerobic isolate. All obligate anaerobes were successfully identified to the species level via MALDI-TOF MS. In contrast, the VITEK®2 ANC ID card identified only 40 % (4/10) anaerobic bacteria to the species level. All obligate anaerobic organisms showed 100 % susceptibility to metronidazole, vancomycin and ertapenem. 25 % of the Bacteroides spp. and 50 % of Clostridium perfringens isolates were found to be resistant to clindamycin.

Conclusion: MALDI-TOF MS proves as a beneficial diagnostic tool for bacterial identification, eliminating the labour-intensive and time consuming conventional microbiological methods. Its accuracy of bacterial detection further helps in combating antibiotic resistance and improving patient outcomes in deep-seated infections.