[Detection of heterogeneous Staphylococci by the Autobac system using a nafcillin disk]

Area of Science:

• Clinical Microbiology
• Infectious Diseases
• Diagnostic Technology

Background:

• Accurate detection of Methicillin-Resistant Staphylococcus aureus (MRSA) is crucial for effective patient treatment and infection control.
• Automated systems for antimicrobial susceptibility testing (AST) aim to streamline laboratory workflows but face challenges in MRSA detection.
• Current methods for MRSA identification require optimization for improved sensitivity and specificity in automated platforms.

Purpose of the Study:

• To evaluate the performance of a 1.0 microgram nafcillin disk compared to a 5.0 microgram oxacillin disk for MRSA detection using the Autobac automated system.
• To assess the concordance between disk diffusion methods and the Autobac system for identifying MRSA strains.
• To determine the optimal disk diffusion parameters for reliable MRSA detection in automated AST.

Main Methods:

• Two disk diffusion tests were performed on 49 Staphylococcus aureus strains: one using a 1.0 microgram nafcillin disk with the Autobac system, and another using a 5.0 microgram oxacillin disk on Mueller-Hinton agar supplemented with NaCl.
• Tests were conducted with incubation at 37 degrees C.
• Results were compared to a reference method, with Light Scattering Index (LSI) values analyzed for discrepancies.

Main Results:

• A concordance rate of 87.8% was observed between the two tested methods.
• Six strains showed discrepancies; five exhibited decreasing Light Scattering Index (LSI) sensitivity near the cutoff value.
• The 1.0 microgram nafcillin disk demonstrated superior performance compared to the 5.0 microgram oxacillin disk within the Autobac automated system.

Conclusions:

• The 1.0 microgram nafcillin disk is more effective than the 5.0 microgram oxacillin disk for MRSA detection using the Autobac system.
• Optimization of disk diffusion parameters is essential for enhancing the accuracy of automated MRSA identification.
• Improved automated detection of MRSA can significantly impact clinical decision-making and infection control strategies.