여름정기학술대회
2022여름초록
발표자 및 발표 내용
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주저자
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공동저자
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접수자
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Methicillin-resistant
Staphylococcus aureus (MRSA) is a major cause of healthcare-associated
infections such as bacteremia, pneumonia, and surgical wound infection, and it
is challenging to deal with as it has resistance against many other
antibiotics. Rapid detection of MRSA is essential to prevent such deadly
infections, and prompt treatment of antimicrobials against MRSA improved
treatment outcomes. However, traditional MRSA screening/confirmatory tests
based on molecular diagnostics involve PCR, sequencing, and DNA chips plus
bacterial culture with antimicrobial susceptibility tests, which are
time-consuming, labor-intensive, and costly. The primary objective of this
study was to evaluate the clinical performance of rapid MRSA screening software
based on matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS and machine learning
techniques. AMRQuest software was developed to be able to compare MALDI-TOF
mass spectra of S. aureus with a database by working on the machine
learning technique and was successfully used to screen MRSA and identify the
bacterial species simultaneously. The cefoxitin disk diffusion test was
conducted to use the test results as the reference values. From the test, the
sensitivity, specificity, percent agreements, and Cohen’s kappa value were
calculated to determine the accuracy of the AMRQuest software. The SCCmecA
gene was detected to compare the discrepancy between the cefoxitin disk
diffusion test and the results of AMRQuest MRSA screening. Using the results
from the AMRQuest Software, MRSA and MSSA were successfully distinguished
statistically (p<0.0001), and the PPV and NPV were estimated to be 95% and
78%, respectively. In conclusion, the clinical performance of AMRQuest software
for MRSA screening was evaluated to determine if it would be sufficient for use
in laboratories.
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