Rapid Detection of Carbapenemase-Producing Acinetobacter Baumannii Using MALDI-TOF Mass Spectrometry: A Technological Advancement in Clinical Microbiology.

Abstract

Background: The emergence of CRAB presents a critical challenge in healthcare settings, necessitating rapid and accurate detection methods. MALDI-TOF MS has emerged as a promising technology for antimicrobial resistance detection, offering advantages over conventional molecular assays.

Objective: This study evaluates the diagnostic performance and clinical applicability of MALDI-TOF MS for the rapid identification of carbapenemase-producing A. baumannii isolates, aiming to enhance laboratory efficiency and antimicrobial stewardship.

Materials and methods: A total of 200 clinical A. baumannii isolates were collected from a tertiary care hospital in China throughout 2023. Carbapenemase activity was assessed using MALDI-TOF MS, and results were compared against molecular identification of carbapenemase-encoding genes via multiplex PCR. Diagnostic performance metrics, including sensitivity, specificity, and predictive values, were analyzed.

Results: MALDI-TOF MS demonstrated high diagnostic accuracy, correctly identifying carbapenemase activity in 95 of 100 CRAB isolates and classifying 98 of 100 carbapenem-susceptible strains. Molecular assays confirmed the presence of carbapenemase genes in 96 resistant isolates. Performance metrics showed MALDI-TOF MS achieved a sensitivity of 98.96%, specificity of 99.00%, and overall accuracy of 98.98%. The system effectively detected various carbapenemase types, including OXA-48-like enzymes (40%), New Delhi metallo-β-lactamase (30%), Klebsiella pneumoniae carbapenemase (15%), Verona integron-encoded metallo-β-lactamase (10%), and active-on-imipenem (IMP) (1%), with a single missed detection of an IMP-producing isolate.

Conclusion: MALDI-TOF MS offers a rapid, reliable, and cost-effective approach for carbapenemase detection in A. baumannii, significantly improving the speed of antimicrobial resistance screening in clinical laboratories. Its seamless integration into existing workflows enhances diagnostic efficiency and supports real-time antimicrobial resistance surveillance.