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BioMed Research International
Volume 2015, Article ID 516410, 18 pages
http://dx.doi.org/10.1155/2015/516410
Research Article

Performances and Reliability of Bruker Microflex LT and VITEK MS MALDI-TOF Mass Spectrometry Systems for the Identification of Clinical Microorganisms

1Department of Research & Development, Duzen Laboratories Group, 06680 Ankara, Turkey
2Department of Clinical Microbiology, Duzen Laboratories Group, 34387 Istanbul, Turkey
3Department of Clinical Microbiology, Duzen Laboratories Group, 06680 Ankara, Turkey

Received 1 September 2015; Revised 14 November 2015; Accepted 16 November 2015

Academic Editor: Qaisar Mahmood

Copyright © 2015 Kivanc Bilecen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In clinical microbiology laboratories, routine microbial identification is mostly performed using culture based methodologies requiring 24 to 72 hours from culturing to identification. Matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) technology has been established as a cost effective, reliable, and faster alternative identification platform. In this study, we evaluated the reliability of the two available MALDI-TOF MS systems for their routine clinical level identification accuracy and efficiency in a clinical microbiology laboratory setting. A total of 1,341 routine phenotypically identified clinical bacterial and fungal isolates were selected and simultaneously analyzed using VITEK MS (bioMérieux, France) and Microflex LT (Bruker Diagnostics, Germany) MALDI-TOF MS systems. For any isolate that could not be identified with either of the systems and for any discordant result, 16S rDNA gene or ITS1/ITS2 sequencing was used. VITEK MS and Microflex LT correctly identified 1,303 (97.17%) and 1,298 (96.79%) isolates to the species level, respectively. In 114 (8.50%) isolates initial phenotypic identification was inaccurate. Both systems showed a similar identification efficiency and workflow robustness, and they were twice as more accurate compared to routine phenotypic identification in our sample pool. MALDITOF systems with their accuracy and robustness offer a good identification platform for routine clinical microbiology laboratories.