Evaluation of Common Commercial Systems for the Identification of Yeast Isolates in Microbiology Laboratories: A Multicenter Study

KARABIÇAK N., Altun H. U., KARATUNA O., Hazirolan G., AKSU N., Adiloglu A., ...More

MIKROBIYOLOJI BULTENI, vol.49, no.2, pp.210-220, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 49 Issue: 2
  • Publication Date: 2015
  • Doi Number: 10.5578/mb.9370
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.210-220
  • Hacettepe University Affiliated: Yes


Accurate and rapid identification of yeast isolates have become important in recent years for not only antifungal susceptibility testing due to the species-specific clinical resistance breakpoints but also early initiation of appropriate antifungal therapy. In clinical microbiology laboratories species identification of yeasts is often performed with several commercial systems based on biochemical properties and rarely according to the physiological and morphological characteristics. The aim of this study was to compare the two common commercial systems, VITEK 2 YST ID Card (Vitek; bioMerieux, France) and API 20C AUX (API; bioMerieux, France) with conventional mycological methods. A total of 473 clinical yeast strains isolated from clinical specimens in different university and training/research hospitals and identified by Vitek system were included in the study. The isolates were re-identified with API and conventional methods including morphological identification in the Mycology Reference Laboratory of the Public Health Institute of Turkey. Candida dubliniensis MYA 583, Candida krusei ATCC 6258, Candida parapsilosis ATCC 22019, Candida albicans ATCC 10231 and Cryptococcus neoformans ATCC 32268 were used as quality control strains and those standard strains were studied consecutively 10 days with both of the methods. The results of identification by Vitek and API were compared with the results of conventional methods for those 473 yeast isolates [6 genus (Candida, Cryptococcus, Blastoshizomyces, Rhodotorula, Saccharomyces, Trichosporon), 17 species (5 common and 12 rarely isolated)]. The performances of the systems were better (Vitek: 95%; API: 96%) for the commonly detected species (C.albicans, C.parapsilosis, C.glabrata, C.tropicalis and C.krusei) than those for rarely detected species (Vitek: 78.4%; API: 71.6%) (p= 0.155). Misidentification or unidentification were mostly detected for C.parapsilosis (Vitek: 6/87; API: 7/87) and C.glabrata (Vitek: 9/104; API: 3/104) by both of the systems. For rarely detected yeast isolates, misidentification or unidentification were most frequently observed in species of C.pelliculosa (Vitek: 3/11; API: 6/11) and C.dubliniensis (API and Vitek: 2/5) isolates. Candida guilliermondii (API: 2/5) isolates had lower rate of identification with API compared to other species. Blastoschizomyces capitatus and Saccharomyces cerevisiae isolates could not be identified by both of the systems. As a result, the accurate diagnosis of Vitek and API systems were similar in terms of consistency (86.3%). Two systems performed well in correct identification of common clinical yeast species (at least 95%), while the identification of rare species was more challenging indicating that they require further morphological and physiological testing. The addition of morphological identification to commercial systems will be useful for accurate diagnosis and treatment of mixed infections.