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Multicenter Study Evaluating the Vitek MS System for Identification of Medically Important Yeasts
Author(s) -
Lars F. Westblade,
Rebecca Jennemann,
John A. Branda,
Maureen Bythrow,
Mary Jane Ferraro,
Omai B. Garner,
Christine C. Ginocchio,
Michael A. Lewinski,
Ryhana Manji,
A. Brian Mochon,
Gary W. Procop,
Sandra S. Richter,
Jenna Rychert,
Linda Sercia,
CareyAnn D. Burnham
Publication year - 2013
Publication title -
journal of clinical microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.349
H-Index - 255
eISSN - 1070-633X
pISSN - 0095-1137
DOI - 10.1128/jcm.00680-13
Subject(s) - microbiology and biotechnology , biology , cryptococcus neoformans , candida dubliniensis , candida parapsilosis , candida krusei , candida albicans , corpus albicans , geotrichum
The optimal management of fungal infections is correlated with timely organism identification. Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) is revolutionizing the identification of yeasts isolated from clinical specimens. We present a multicenter study assessing the performance of the Vitek MS system (bioMérieux) in identifying medically important yeasts. A collection of 852 isolates was tested, including 20 Candida species (626 isolates, including 58 C. albicans, 62 C. glabrata, and 53 C. krusei isolates), 35 Cryptococcus neoformans isolates, and 191 other clinically relevant yeast isolates; in total, 31 different species were evaluated. Isolates were directly applied to a target plate, followed by a formic acid overlay. Mass spectra were acquired using the Vitek MS system and were analyzed using the Vitek MS v2.0 database. The gold standard for identification was sequence analysis of the D2 region of the 26S rRNA gene. In total, 823 isolates (96.6%) were identified to the genus level and 819 isolates (96.1%) were identified to the species level. Twenty-four isolates (2.8%) were not identified, and five isolates (0.6%) were misidentified. Misidentified isolates included one isolate of C. albicans (n = 58) identified as Candida dubliniensis, one isolate of Candida parapsilosis (n = 73) identified as Candida pelliculosa, and three isolates of Geotrichum klebahnii (n = 6) identified as Geotrichum candidum. The identification of clinically relevant yeasts using MS is superior to the phenotypic identification systems currently employed in clinical microbiology laboratories.

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