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Manual Reading of Sensititre Broth Microdilution System Panels Improves Accuracy of Susceptibility Reporting for Polymyxin Antibiotics

Accurate and reproducible antimicrobial susceptibility testing (AST) of polymyxin antibiotics is critical, as these drugs are last-line therapeutic options for the treatment of multidrug-resistant Gram-negative bacterial infections. However, polymyxin AST in the routine laboratory remains challengin...

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Bibliographic Details
Published in:Journal of clinical microbiology 2021-08, Vol.59 (9), p.e0033221-e0033221
Main Authors: Bellerose, Michelle M, Clark, Andrew E, Youn, Jung-Ho, Weingarten, Rebecca A, Crooks, Chelsea M, Dekker, John P, Frank, Karen M
Format: Article
Language:English
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Summary:Accurate and reproducible antimicrobial susceptibility testing (AST) of polymyxin antibiotics is critical, as these drugs are last-line therapeutic options for the treatment of multidrug-resistant Gram-negative bacterial infections. However, polymyxin AST in the routine laboratory remains challenging. In this study, we evaluated the performance of an automated broth microdilution (BMD) system (Sensititre, ThermoFisher) compared to that of agar dilution (AD) for colistin and polymyxin B AST of 129 , Pseudomonas aeruginosa, and Acinetobacter baumannii complex clinical isolates. MICs derived from the Sensititre instrument based on two operator comparisons demonstrated overall categorical agreement (CA) of 86% and 89% compared to AD for colistin and 89% and 92% compared to AD for polymyxin B. However, error rates were higher than recommended by CLSI. Manual inspection of microdilution wells revealed microbial growth and skip wells which were erroneously interpreted by the Aris 2X instrument. Using manually interpreted BMD MICs read by two operators increased the overall categorical agreements to 88% and 95% compared to AD for colistin and 92% and 96% compared to AD for polymyxin B. Laboratories choosing to use the Sensititre platform for polymyxin AST should consider manual evaluation of wells as part of their algorithm.
ISSN:0095-1137
1098-660X
DOI:10.1128/JCM.00332-21